JP4612270B2 - Design method of bioactive peptide and use thereof - Google Patents

Description

【００５０】
Ａ．アミノ酸相補プロファイル波形評価（相補ペプチドライブラリの生成）
アミノ酸相補プロファイル波形評価とは、標的アミノ酸配列の物理化学的なアミノ酸指標を基に、標的アミノ酸配列と相互作用するアミノ酸配列を有するペプチドを評価する方法である。この評価法は、標的タンパク質の標的部位が１つの連続するアミノ酸配列からなる場合に特に有用である。以下、アミノ酸相補プロファイル波形評価において用いられる用語及びこの評価の概要について述べる。
【００５１】
「標的アミノ酸配列」とは、相補アミノ酸配列の設計において標的となるアミノ酸配列をいう。即ち、本発明は、この「標的アミノ酸配列」に相互作用するアミノ酸配列（相補アミノ酸配列）からなる生理活性ペプチドを設計することを目的とする。好ましくは、標的アミノ酸配列は、創薬のターゲットとなる標的タンパク質（例えば、受容体、酵素等）中に見出されるアミノ酸配列である。
【００５２】
「相補アミノ酸配列」とは、本発明における相補（性）の定義を満たすアミノ酸配列をいう。
【００５３】
「相補（性）」とは、標的アミノ酸配列に任意のアミノ酸指標を当てはめたプロファイル波形に対し、移動平均により低域通過させた移動平均プロファイル波形に負の相関性を持つ相補移動平均プロファイル波形を近似するプロファイル波形をもつアミノ酸配列と標的アミノ酸配列との間の関係のことをいう。したがって、あるアミノ酸配列の移動平均プロファイル波形が、他のアミノ酸配列の移動平均プロファイル波形よりも、標的アミノ酸配列の相補移動平均プロファイル波形との相関係数Ｒ（以下に説明）が小さい場合、当該アミノ酸配列は、標的アミノ酸配列に「より相補的」である。
【００５４】
「プロファイル波形」とは、アミノ酸配列にアミノ酸指標を当てはめることにより生成される波形のことをいう。
【００５５】
「移動平均プロファイル波形」とは、プロファイル波形を所定のウィンドウ幅で移動平均させた波形をいう。標的アミノ酸配列に任意のアミノ酸指標を当てはめたプロファイル波形をＴ_iとすると、任意（奇数）のウィンドウ幅ｗでの移動平均プロファイル波形ｘ_jは、[式１]によって示される。
【００５６】
【数１】

【００５７】
また、本明細書中では特に、相補アミノ酸配列の移動平均プロファイル波形を、「相補移動平均プロファイル波形」と呼ぶ。相補アミノ酸配列候補に任意のアミノ酸指標を当てはめたプロファイル波形をＣ_iとすると、任意（奇数）のウィンドウ幅ｗでの相補移動平均プロファイル波形ｙ_jは、［式２］によって示される。
【００５８】
【数２】

【００５９】
「ウィンドウ（Window）幅」とは、移動平均プロファイル波形を作成する際、プロファイル波形を足し合わせる範囲の幅をいう。ウィンドウ幅は、任意の奇数に設定可能であるが、通常は１〜１３、好ましくは３〜１３、より好ましくは５〜１１に設定される。
【００６０】
「アミノ酸指標」とは、アミノ酸の物理化学的特徴を数値化した指標のことをいう。アミノ酸指標は、現在までに４００種類以上蓄積されており、例えば、京都大学化学研究所が提供するデータベースＡＡｉｎｄｅｘなどから、これらの指標を検索することができる。これらのアミノ酸の指標は、疎水性、β構造形成のしやすさ、αヘリックス形成及びターン形成のしやすさ、並びに側鎖の物理化学的性質（例えば、側鎖体積の相対的な大きさ）の５つの特徴に大別できる（例えば、Tomii and Kanehisa, Protein Eng., 9, 27-36 (1996)を参照のこと）。本発明において、プロファイル波形を生成する際のアミノ酸指標は、約４００種類から選択可能である。また、上記データベースは、当該プログラムに付帯して当該装置（後述）の記憶装置に格納されたものであってもよく、また、インターネットなどの通信によって参照可能な外部の記憶装置に格納されたものであってもよい。
【００６１】
本発明において用いられる好ましいアミノ酸指標の例としては、疎水度に基づく指標、電気的特性に基づく指標、α−ヘリックスおよびβ−シートのとりやすさを表す指標、側鎖体積の相対的な大きさを表す指標が挙げられるが、アミノ酸指標は、より好ましくは、疎水度に基づく指標及び電気的特性に基づく指標から選択される。疎水度に基づく指標としては、例えば、Kyte-Doolittleのhydropathy index、Jonesらのhydrophobicity、およびConsensus Normalized Hydrophobicity scale [参考文献３]等が挙げられるが、特に、Consensus Normalized Hydrophobicity scaleが好ましい。また、電気的特性に基づく指標とは、分子の分極度合、静電的な相互作用を表す指標をいい、例えば、FauchereらのLocalized electrical effect、GranthamらのPolarity、およびElectron-ion interaction potential（EIIP）[参考文献４]等が挙げられるが、特に、Electron-ion interaction potential（EIIP）が好ましい [参考文献４]。
【００６２】
「相補度パラメータ」とは、標的アミノ酸配列の移動平均プロファイル波形と相補アミノ酸配列の相補移動平均プロファイル波形との間の相補性を示す値をいう。相補度パラメータの一例は、以下［式３］に示される相関係数Ｒである。
【００６３】
【数３】

【００６４】
ここで、移動平均プロファイル波形の平均値は、［式４］で示される。
【００６５】
【数４】

【００６６】
また、相補移動平均プロファイル波形の平均値は、［式５］で示される。
【００６７】
【数５】

【００６８】
当業者は、上記［式３］の相関係数に加え、上記［式３］から誘導される数式より算出される値を相補度パラメータとして使用することができる。
【００６９】
「平均値パラメータ（Ｐ_ave）」とは、（ｉ）以下［式６］に示される、標的アミノ酸配列のプロファイル波形の平均値
【００７０】
【数６】

【００７１】
または（ｉｉ）以下［式７］に示される、使用したアミノ酸指標の平均値
【００７２】
【数７】

【００７３】
をいう。
【００７４】
「フィルター値」とは、相補アミノ酸配列候補数を絞り込むために設定される値をいうが、本発明では、特に、相補度パラメータに関するフィルター値および平均値パラメータに関するフィルター値が利用される。
【００７５】
相補度パラメータに関するフィルター値としては、例えば、相関係数フィルター値Ｒ_t（この値Ｒ_tに基づき、Ｒ＜Ｒ_tに該当する相関係数Ｒを有する相補アミノ酸配列候補のみが選択される）が利用される。好ましくは、標的アミノ酸配列の移動平均プロファイル波形と相補アミノ酸配列の相補移動平均プロファイル波形との間には負の相関性が必要とされる。従って、相関係数フィルター値Ｒ_tは、−１以上の任意の負の数に設定可能であるが、好ましくはＲ_t≦−０．９に設定される。なお、相補度パラメータに関するフィルター値は、相補度パラメータの計算前に予め設定されていてもよいし、計算の後に適宜設定してもよい。
【００７６】
また、平均値パラメータに関するフィルター値として、Ｐ_aveフィルター値ａ，ｂが設定される。この値ａ，ｂに基づき、ａ＜Ｐ_ave＜ｂに該当するＰ_aveを有する相補アミノ酸配列候補のみが選択される。なお、Ｐ_aveが疎水度の高い値をとる場合には、得られる生理活性ペプチドが不溶性になり実験が困難になるので、ａ，ｂの値は、好ましくは、親水度が高い値に設定される。なお、Ｐ_aveフィルター値は、Ｐ_aveの計算前に予め設定されていてもよいし、Ｐ_ave計算の後に適宜設定してもよい。
【００７７】
フィルター値として、相関係数フィルター値Ｒ_tのみを用いてもよいが、好ましくは、Ｐ_aveフィルター値ａ，ｂがさらに用いられる。この場合には、条件式は以下の通りである。
【００７８】
【数８】

【００７９】
本評価法によって選択されたアミノ酸配列（相補ペプチドライブラリ）は、好ましくは、後述の２次スクリーニングに供される。
【００８０】
Ｂ．アミノ酸相互作用領域評価（断片化ペプチドライブラリの生成）
アミノ酸相互作用領域評価とは、標的タンパク質と相互作用するタンパク質の一次構造（アミノ酸配列）情報を利用して、標的タンパク質に結合しうるペプチドを設計する方法をいう。この評価法は、標的タンパク質に相互作用する、若しくは相互作用が予測されるタンパク質の一次構造（アミノ酸配列）が既知である場合に特に有用である。以下、アミノ酸相互作用領域評価において用いられる用語及びこの評価の概要について述べる。
【００８１】
標的タンパク質の標的部位と相互作用するタンパク質中の「相互作用領域」については、ある標的タンパク質と相互作用しうる領域が既に特定されているタンパク質が存在する場合には、その領域を選択する。１つのタンパク質内に複数の相互作用領域が存在する場合には、それらの複数の領域を相互作用領域として選択する。ある標的タンパク質と相互作用し得るタンパク質が複数知られている場合には、これらのタンパク質から複数の相互作用領域を取得することもできる。一方、標的タンパク質と相互作用するタンパク質自体は既知であるが、その相互作用する領域が特定されていない場合には、当該分野で周知の方法（例えば、ＲＢＤ法［参考文献５］）によってこの領域を選択することができる。尚、ある標的タンパク質と相互作用するタンパク質自体が未知の場合にも本評価法は適用可能であるが、好ましくは、ある標的タンパク質と相互作用するタンパク質自体が既知の場合に本評価法は適用される。
【００８２】
本評価法において抽出されるアミノ酸配列の長さ（即ち、アミノ酸残基の数）としては、上記の相互作用領域の全長以内であれば、任意の長さのアミノ酸配列を抽出することが可能であるが、好ましくは、３〜７個のアミノ酸残基からなるアミノ酸配列、より好ましくは４個のアミノ酸残基からなるアミノ酸配列を抽出することができる。なお、アミノ酸配列の抽出は網羅的に行なわれる。例えば、Ｘ個のアミノ酸残基からなるアミノ酸配列を抽出する場合には、上記の相互作用領域のＮ末端からＣ末端にかけて、Ｎ−Ｘ＋１個のアミノ酸配列が抽出され、断片化ペプチドライブラリとして格納される。また、抽出されるアミノ酸配列の長さは統一してもよいが、変動させてもよい。例えば、Ｘ個のアミノ酸残基からなるアミノ酸配列を、相互作用領域から網羅的にＮ−Ｘ＋１個抽出し、Ｘ’個（Ｘとは異なる数）のアミノ酸残基からなるアミノ酸配列を、同じ相互作用領域から重複して網羅的にＮ−Ｘ’＋１個抽出して、長さの異なるアミノ酸配列を断片化ペプチドライブラリに格納してもよい。本評価法によって抽出されたアミノ酸配列（断片化ペプチドライブラリ）は、後述の２次スクリーニングによって選択される。
【００８３】
Ｃ．アミノ酸位置依存的結合有意性評価（位置スコアペプチドライブラリの生成）
アミノ酸位置依存的結合有意性評価は、千〜数十万にも及ぶアミノ酸配列を含むペプチドライブラリからランダムにわずか数％のアミノ酸配列を取得し、標的タンパク質との相互作用エネルギーを評価することにより、高速なΔＧ計算を可能とするスコアマトリクスを構築し、ペプチド設計を実施することを特徴とする。なお、この評価法は、基質特異性が似ている複数のタンパク質が存在する場合に、このうちの１つのタンパク質を標的タンパク質として選択し、この標的タンパク質に特異性が高いペプチドを設計することもできる。
【００８４】
本評価法は、任意の標的タンパク質に対して適用可能であるが、好ましくは、標的タンパクとして分子表面にポケットを持ち、かつ結合に伴う構造変化が少ないと考えられる酵素など（例えば、ペプチダーゼ）に対して適用される。これはスコアマトリクスの作成の際、位置依存的なアミノ酸出現頻度をその基盤としているところに由来している。すなわち、本評価法は、ペプチドとの結合の際、限定された骨格及び結合様式を必要とするものに対して好ましい。
【００８５】
「解析用ライブラリ」は、一定の長さのアミノ酸配列（即ち、特定の数のアミノ酸残基からなるアミノ酸配列）を網羅的に生成したものからランダムに抽出されたアミノ酸配列の集合からなる。解析用ライブラリに含まれるアミノ酸配列の数は、網羅的に生成された一定の長さのアミノ酸配列の総数に対して数％でよい。例えば、Ｘ個のアミノ酸残基からなるアミノ酸配列を設計する場合には、２０^Xの組み合わせを網羅的に生成し、生成された２０^X個のアミノ酸配列から数％をランダムに選択して解析用ライブラリとして使用する。また、解析用ライブラリとして選択されるアミノ酸配列の数は、特定の数に限定されるものではなく、適宜設定可能である。なお、本評価で設計可能なアミノ酸配列の長さは特に限定されないが、２〜１０個のアミノ酸残基からなるアミノ酸配列が好ましく、３〜５個のアミノ酸残基からなるアミノ酸配列がより好ましい。また、「解析用ライブラリ」の妥当性を評価するために、「評価用ライブラリ」の生成も必要に応じて行なわれる。「評価用ライブラリ」としては、網羅的に生成されたアミノ酸配列のうち、「解析用ライブラリ」に用いられたアミノ酸配列を除いたものから選択される。「評価用ライブラリ」として用意されるアミノ酸配列の数は、特に限定されないが、「解析用ライブラリ」として用意されるアミノ酸配列の数よりも少ない数に設定される。
【００８６】
「分子間エネルギーパラメータ」については、後述の「ＩＩ．２次スクリーニング」に記載したものと同義であり、その計算についても後述の方法と同様に行なわれる。
【００８７】
「アミノ酸出現頻度に基づくスコアマトリクス」とは、アミノ酸出現頻度に基づいて生成されたマトリクスであるならばいかなるものをも意味する。「アミノ酸出現頻度に基づくスコアマトリクス」の例は、下記［式９］に従い生成されるＰＳＳマトリクス（Positional Scanning Score−MATRIX）である。なお、ａ_ijは、解析用ライブラリに含まれる全ペプチド群中のポジションｊにおけるアミノ酸ｉの出現回数を示し、ｂ_ijは、解析用ライブラリに含まれる閾値ΔＧ以下のペプチド群中のポジションｊにおけるアミノ酸ｉの出現回数である。なお、上記では、閾値ΔＧは、予め設定されていてもよいが、後述する方法で設定されてもよい。
【００８８】
【数９】

【００８９】
「アミノ酸出現頻度に基づくスコア」は、上記「アミノ酸出現頻度に基づくスコアマトリクス」に従って計算されるものを意味し、その一例は、下記［式１０］によって計算されるＰＳＳ（Positional Scanning Score）である［参考文献６］。なお、下記［式１０］において、ｎは、決定されるべきアミノ酸の数を示し、Ｃ_ijは、２０×ｎのマトリクスであり、０若しくは１の値で構成される。また、任意のアミノ酸配列中のポジションｊにおけるアミノ酸ｉと一致する要素は１、不一致の要素は０とする。
【００９０】
【数１０】

【００９１】
「アミノ酸位置依存的な分子間エネルギーパラメータに基づくマトリクス」とは、解析用ライブラリとして抽出された各アミノ酸配列について計算された「分子間エネルギーパラメータ」と「アミノ酸出現頻度に基づくスコア」との間の相関解析で得られた回帰式を用いて、「アミノ酸出現頻度に基づくスコアマトリクス」を変換したものをいう。一例は、ＰＳＧマトリクス（Positional Scanning ΔＧ−MATRIX）である。なお、「アミノ酸出現頻度に基づくスコアマトリクス」に変換する際に、好ましくは、定数項は各ポジションに均一に分配される。
【００９２】
「アミノ酸位置依存的な分子間エネルギーパラメータ値」とは、上記「アミノ酸出現頻度に基づくスコアマトリクス」によって計算されるものであり、その一例は、下記式［式１１］によって計算されるＰＳＧ（Positional Scanning ΔＧ）であるが、自由エネルギーと同義のパラメータをも使用可能である。なお、下記［式１１］において、ＰＳＧ_ijはＰＳＧマトリクス中のｉｊの要素を示す。
【００９３】
【数１１】

【００９４】
本評価法によって選択されたアミノ酸配列（位置スコアペプチドライブラリ）は、好ましくは、後述の２次スクリーニングに供される。
【００９５】
ＩＩ．２次スクリーニング
「分子間エネルギーパラメータ」とは、相補アミノ酸配列と標的タンパク質の標的部位との間の分子間エネルギーに基づくパラメータをいう。分子間エネルギーパラメータは、当該分野で公知である任意の方法により算出される分子間エネルギーに関するパラメータを意味する。当該分野で公知である任意の方法により算出される分子間エネルギーパラメータとしては、例えば、ＭＭ３［参考文献７］、Ａｍｂｅｒ［参考文献９］、Ｃｈａｒｍｍ［参考文献１０］力場によって算出されるものが挙げられる。好ましくは、分子間エネルギーパラメータとして、Ａｍｂｅｒ力場を用いた分子間エネルギー（Ｅ_mol）および阻害定数（Ｋ_i）が利用される。
【００９６】
「分子間エネルギー（Ｅ_mol）」は、下記［式１２］（Ａｍｂｅｒ力場［参考文献９、１１］を利用）によって計算される。
【００９７】
【数１２】

【００９８】
ここで、好ましくは、［式１２］の各係数は、Ａｍｂｅｒ力場により経験的パラメータとして与えられているものである。
【００９９】
「阻害定数（Ｋ_i）」は、下記［式１３］によって計算される。
【０１００】
【数１３】

【０１０１】
なお、ΔＧは、標的タンパク質の標的部位と相補アミノ酸配列からなるペプチド候補との間での複合体生成によるものであり、当該分野で公知の任意のエネルギー関数を用いて計算されるが、好ましくは、AutoDockエネルギー関数［参考文献２］を用いて計算される。
【０１０２】
標的タンパク質の標的部位に対して好ましい生理活性ペプチドは、任意の分子間エネルギーパラメータに対して設定される閾値条件を満たすことが必要とされる。好ましくは、分子間エネルギーパラメータとして上記Ｅ_mol又はＫ_iが利用される場合、以下の［式１４］又は［式１５］のいずれかを満たすことが必要とされる。
【０１０３】
【数１４】

【０１０４】
又は
【０１０５】
【数１５】

【０１０６】
これまで１次スクリーニング、２次スクリーニングについて述べてきたが、以降は、２次スクリーニングで得られたアミノ酸配列を有するペプチドを、便宜上「リードペプチド」と呼ぶこともある。
【０１０７】
ＩＩＩ．３次スクリーニング
３次スクリーニングとして、「アミノ酸変異ΔＥ（例えば、ΔＧ）評価」によってリードペプチドの変異を実施することができる。アミノ酸変異ΔＥ評価とは、リードペプチドを構成する各アミノ酸を、それ以外のアミノ酸に置換して変異ペプチドを作成し、この変異ペプチドと標的タンパク質とのΔＥ_mutant（例えば、ΔＧ_mutant）を計算して、変異ペプチドを評価する方法をいう。例えば、２次スクリーニングで得られたペプチドが４個のアミノ酸残基から構成され、このうち１個のアミノ酸残基のみを他の天然アミノ酸に置換する場合には、４×１９個の変異ペプチドを網羅的に作成し、これらの変異ペプチドと標的タンパク質とのΔＥ_mutant（例えば、ΔＧ_mutant）をそれぞれ計算する。ΔＥ_mutant（例えば、ΔＧ_mutant）の計算は、２次スクリーニングでの計算と同様に、当該分野で公知の任意のエネルギー関数を用いて計算されるが、好ましくは、AutoDockエネルギー関数を用いて計算される。一例では、ΔＥとして結合自由エネルギーを用いる場合、各変異ペプチドについて得られたΔＧ_mutantとリードペプチドのΔＧ_leadとの差であるΔΔＧを下記［式１６］により計算する。
【０１０８】
【数１６】

【０１０９】
上記式から、ΔΔＧが負になる変異ペプチドは、リードペプチドに比べて、標的タンパク質とより安定な複合体を形成すること、並びにΔΔＧが正になる変異ペプチドは、リードペプチドと比べて、標的タンパク質とより不安定な複合体を形成することが理解される。従って、３次スクリーニングにおいては、ΔΔＧが負となるようなアミノ酸変異の導入が好ましい。尚、上記の例示では、リードペプチドを構成するアミノ酸残基のうち１個のアミノ酸残基のみを置換したが、ΔΔＧが負になるアミノ酸変異を組み合わせて、２個又は３個のアミノ酸残基の置換を実施してもよい。上述のごとく３次スクリーニングを行なうことにより、より最適な生理活性ペプチドを設計することができる。
【０１１０】
アミノ酸の置換では、リードペプチド中の各アミノ酸を、それ以外の１９種類の天然アミノ酸に置換することができる。また、天然アミノ酸以外にも、さらに、任意のα−アミノ酸、β−アミノ酸、γ−アミノ酸などの非天然アミノ酸で置換することもできる。但し、α−アミノ酸以外での置換は、主鎖の配置に変化をもたらす可能性が高いため、任意の非天然α−アミノ酸での置換が好ましい。また、Ｌ体、Ｄ体のいずれのアミノ酸にも置換可能である。
【０１１１】
また、このアミノ酸の置換は、「アミノ酸の側鎖の最適化」という観点から捉えることもできる。例えば、１次スクリーニング、２次スクリーニングを経て上記「アミノ酸変異ΔＥ（例えば、ΔＧ）評価」を行い、ある標的タンパク質の標的部位に対して最も好ましいペプチドＡｌａ−Ｃｙｓ−Ｐｈｅ−Ｖａｌが得られたと仮定する。このような場合、このペプチドＡｌａ−Ｃｙｓ−Ｐｈｅ−Ｖａｌをベースにしてより好ましいペプチドを得るために、このペプチドを構成する各アミノ酸の側鎖について再検証することも可能である。具体的には、Ｍｅｔの側鎖（−ＣＨ₂ＣＨ₂ＳＣＨ₃）において水素原子の代わりにハロゲン原子を導入した側鎖やさらなる基を導入した側鎖について、上記「アミノ酸変異ΔＥ（例えば、ΔＧ）評価」を行い、より適切な変異ペプチドを得ることも可能である。上記では例としてＭｅｔの側鎖の変異について述べたが、もちろん、Ｍｅｔ以外の天然アミノ酸の側鎖、及び非天然アミノ酸（好ましくは、非天然α−アミノ酸）の側鎖についても同様に再検証することが可能である。一般論として、ある程度大きなタンパク質の場合には、アミノ酸置換は天然アミノ酸に制限されやすい。なぜならば、実際にそのようなタンパク質を合成する場合、ＤＮＡのコード領域を変異し、細胞系や無細胞系による翻訳によって変異（置換）タンパク質を合成するからである。しかし、低分子ペプチドの場合は、そのペプチドを構成するアミノ酸の種類は天然アミノ酸に制限されない。なぜなら、低分子ペプチドは固相合成によって容易に合成可能であり、原料となるアミノ酸の供給さえ可能であれば、天然アミノ酸や非天然アミノ酸に限らず重合反応は容易だからである。従って、「アミノ酸変異ΔＥ（例えば、ΔＧ）評価」を行なう３次スクリーニングは、アミノ酸側鎖をより最適化し、さらに特異性の高い生理活性ペプチドの取得を可能にする。
【０１１２】
なお、「Ｉ．１次スクリーニング」、「ＩＩ．２次スクリーニング」、「ＩＩＩ．３次スクリーニング」の中で挙げた特定の式はあくまで例示であり、それと同義の式やそれから誘導される式も全て各計算において本発明で利用されるものとする。
【０１１３】
以下、本発明を詳述する。
本発明の方法は、上記（１）に示すとおり、上記（ａ１）〜（ｇ１）のステップを有するものであればよく、また、上記（５）に示すとおり、上記（ａ１’）〜（ｉ１’）のステップを有するものであればよい。
【０１１４】
これら本発明による方法を実施するための具体的な手段・形態は限定されないが、処理すべきデータ量が膨大であることを考慮すれば、上記（９）〜（１２）、および上記（１３）〜（１６）に挙げた、本発明によるプログラムによるコンピュータ処理が最も好ましい実施形態である。本発明によるプログラムに含まれる処理ステップは、本発明による方法の発明の各ステップの技術的思想と等しい。よって、以下に、本発明によるプログラムを詳細に説明することによって、本発明による方法をも同時に説明するものとする。
【０１１５】
本発明のプログラムは、標的アミノ酸配列と相互作用する生理活性ペプチドの設計、および標的タンパク質と相互作用する生理活性ペプチドの設計（即ち、標的アミノ酸配列のみならず、標的タンパク質の標的部位に存在する他のアミノ酸配列との相互作用をも考慮する生理活性ペプチドの設計）という観点から大きく２つに分けることができる。一方は、上記（９）〜（１２）に示すプログラムであり、これは、標的アミノ酸配列と相互作用する生理活性ペプチドの設計を行うものである。特に、上記（９）のプログラムに含まれる上記（ａ１）〜（ｆ１）のステップを「１次スクリーニング」と呼ぶこともできる。
【０１１６】
また、本発明によるプログラムのうち、他方は、上記（１３）〜（１６）に示すプログラムであり、これは、標的タンパク質と相互作用する生理活性ペプチドの設計を行うものである。特に、上記（１３）に含まれる上記（ａ１’）〜（ｆ１’）のステップを「１次スクリーニング」と呼び、さらに上記（ｇ１’）〜（ｉ１’）のステップを「２次スクリーニング」と呼ぶこともできる。これら、「１次スクリーニング」と「２次スクリーニング」とを組み合わせることによって、標的タンパク質の標的部位に対してより適切な生理活性ペプチドを取得することが可能になる。
【０１１７】
さらに、上記（１３）〜（１６）のプログラムに対し、上記（２４）に含まれる上記（Ｉ）〜（ＩＩＩ）のステップを組み合わせることもできる。この上記（Ｉ）〜（ＩＩＩ）のステップを「３次スクリーニング」と呼ぶこともできる。「１次スクリーニング」、「２次スクリーニング」にさらに「３次スクリーニング」を組み合わせることによって、より標的タンパク質の標的部位に対して特異性の高い生理活性ペプチドを取得することが可能になる。
【０１１８】
別の局面では、本発明は、上記（２５）及び（２６）のプログラムを提供する。なお、上記（２５）のプログラムに含まれる上記（ａ２）〜（ｂ２）のステップを「１次スクリーニング」と呼ぶこともできる。また、上記（２６）のプログラムに含まれる上記（ａ２’）〜（ｂ２’）のステップを「１次スクリーニング」と呼ぶことができ、さらに、上記（ｃ２’）〜（ｅ２’）のステップを「２次スクリーニング」と呼ぶことができる。なお、上記（２６）のプログラムに、上記（２７）に含まれる上記（Ｉ）〜（ＩＩＩ）のステップ（「３次スクリーニング」）を組み合わせることもできる。
【０１１９】
さらに別の局面では、本発明は、上記（２８）及び（２９）のプログラムを提供する。なお、上記（２８）のプログラムに含まれる上記（ａ３）〜（ｈ３）のステップを「１次スクリーニング」と呼ぶこともできる。また、上記（２９）のプログラムに含まれる上記（ａ３’）〜（ｈ３’）のステップを「１次スクリーニング」と呼ぶことができ、さらに、上記（ｉ２’）〜（ｋ２’）のステップを「２次スクリーニング」と呼ぶことができる。なお、上記（２９）のプログラムに、上記（３０）に含まれる上記（Ｉ）〜（ＩＩＩ）のステップ（「３次スクリーニング」）を組み合わせることもできる。
【０１２０】
本発明における生理活性ペプチド設計の一例を図１に示し、さらに本発明のシステム全体を図２に示す。なお、図１及び図２には２次スクリーニングが記載されているが、この２次スクリーニングは、必要に応じて行なわれるものであり、さらに図２に記載されている３次スクリーニングを併用してもよい。
【０１２１】
本発明において、１次スクリーニングは、３種類のプログラムから構成されており、標的タンパク質の性質等に応じて使い分けられる。一方、２次スクリーニングは、全て共通したプログラムであり、また、３次スクリーニングも全て共通したプログラムである。先ず、１次スクリーニングの選択について説明する。
【０１２２】
図３は、１次スクリーニングの選択におけるプログラムフローを示すフローチャートである。１次スクリーニングの種類としては、リガンド情報の有無、標的部位の連続・不連続、酵素又は表面にポケットを持つかどうかの３つの判断基準により、最も適切なものを選択することができる。以下、１次スクリーニングの選択について、図３のステップ５０１〜５０６を参照して詳細に説明する。
【０１２３】
図３のステップ５０１は、ある標的タンパク質に対してリガンドとなるペプチドが存在するか否かを判断するステップである。即ち、ある標的タンパク質に対するリガンドが未知である場合には、図３のステップ５０２に進み、リガンドが既知である場合には、図３のステップ５０４に進む。
【０１２４】
図３のステップ５０２は、標的タンパク質の標的部位が、連続するアミノ酸配列から主に構成されているか否かを判断するステップである。標的タンパク質の標的部位が、例えば、結晶構造の解析により解明されている場合は、その情報をもとにして判断する。標的タンパク質の標的部位が未知の場合は、例えば、当該分野で公知の立体構造予測プログラムによって判断する。その結果、標的タンパク質の標的部位が、不連続なアミノ酸配列から構成される場合には、アミノ酸位置依存的結合有意性評価が１次スクリーニングとして行なわれる。一方、標的タンパク質の標的部位が、連続するアミノ酸配列から主に構成される場合には、図３のステップ５０３へと進む。
【０１２５】
図３のステップ５０３は、標的タンパク質が、酵素であるか否か、又は表面にポケットを持つタンパク質であるか否かを判断するステップである。表面にポケットを持つタンパク質としては、例えば、受容体が挙げられる。また、標的タンパク質の種類（例えば、酵素、受容体等）が未知である場合には、例えば、相同性検索によって、その標的タンパク質の種類を予測することができる。図３のステップ５０３において、標的タンパク質が、酵素でも、表面にポケットを持つタンパク質でもないと判断された場合には、１次スクリーニングとしてアミノ酸相補プロファイル波形評価が用いられる。一方、標的タンパク質が、酵素、又は表面にポケットを持つタンパク質のいずれかであると判断された場合には、１次スクリーニングとして、アミノ酸相補プロファイル波形評価又はアミノ酸位置依存的結合有意性評価が用いられるが、好ましくは、アミノ酸位置依存的結合有意性評価が用いられる。
【０１２６】
図３のステップ５０４は、標的タンパク質の標的部位が、連続するアミノ酸配列から主に構成されているか否かを判断するステップである。なお、図３のステップ５０４では、図３のステップ５０２と同様にして判断される。その結果、標的タンパク質の標的部位が、不連続なアミノ酸配列から構成されると判断される場合には、図３のステップ５０５に進む。一方、標的タンパク質の標的部位が、連続するアミノ酸配列から主に構成されると判断される場合には、図３のステップ５０６へと進む。
【０１２７】
図３のステップ５０５は、標的タンパク質が、酵素であるか否か、又は表面にポケットを持つタンパク質であるか否かを判断するステップである。図３のステップ５０５では、図３のステップ５０３と同様にして判断される。標的タンパク質が、酵素でも、表面にポケットを持つタンパク質でもないと判断された場合には、１次スクリーニングとしてアミノ酸相互作用領域評価が用いられる。一方、標的タンパク質が、酵素、又は表面にポケットを持つタンパク質のいずれかであると判断された場合には、１次スクリーニングとして、アミノ酸相互作用領域評価又はアミノ酸位置依存的結合有意性評価が用いられるが、好ましくは、アミノ酸相互作用領域評価が用いられる。
【０１２８】
図３のステップ５０６は、標的タンパク質が、酵素であるか否か、又は表面にポケットを持つタンパク質であるか否かを判断するステップである。図３のステップ５０６では、図３のステップ５０３と同様にして判断される。標的タンパク質が、酵素でも、表面にポケットを持つタンパク質でもないと判断された場合には、１次スクリーニングとして、アミノ酸相補プロファイル波形評価又はアミノ酸相互作用領域評価が用いられるが、好ましくは、アミノ酸相互作用領域評価が用いられる。一方、標的タンパク質が、酵素、又は表面にポケットを持つタンパク質のいずれかであると判断された場合には、１次スクリーニングとして、アミノ酸相補プロファイル波形評価、アミノ酸相互作用領域評価又はアミノ酸位置依存的結合有意性評価のいずれを用いてもよいが、好ましくは、アミノ酸相互作用領域評価又はアミノ酸位置依存的結合有意性評価が用いられ、より好ましくは、アミノ酸相互作用領域評価が用いられる。
【０１２９】
以下、１次スクリーニングに関しては、図４のステップ１０１〜１１１（アミノ酸相補プロファイル波形評価）、図６のステップ３０１〜３０２（アミノ酸相互作用領域評価）及び図８のステップ４０１〜４０８（アミノ酸位置依存的結合有意性評価）を参照して説明する。２次スクリーニングに関しては、図４のステップ１１２以降を参照して説明する。また、３次スクリーニングに関しては、図５のステップ２０１〜２０４を参照して説明する。
【０１３０】
図４は、上記（９）および（１３）のプログラムフローを示すフローチャートである。先ず、上記（９）のプログラムについて以下に詳述する。
【０１３１】
上記ステップ（ａ１）は、図４のフローチャートにおけるステップ１０１に対応する。データ入力手段としては、例えば、タッチパネル、キーボード、マウス等が挙げられる。他の入力手段として、ペンタブレット、音声入力装置などを用いることもできる。入力される標的アミノ酸配列としては、リガンド結合部位、基質結合部位、タンパク質間相互作用部位等のアミノ酸配列が適宜選択される。当業者は、あるタンパク質に関するＸ線解析データに基づき、又はあるタンパク質に関する一般的なタンパク質立体構造予測プログラム等の予測に基づき、標的アミノ酸配列を適宜選択し、その配列データを入力することができる。また、上記のような手法によらず仮想的に設定したアミノ酸配列（即ち、任意のアミノ酸配列）を標的アミノ酸配列としてデータ入力してもよい。
【０１３２】
上記（９）のプログラムは、図４のフローチャートに示すステップ１０２に対応するステップを含んでいないが、必要に応じて図４のステップ１０２に対応するステップを含むことができる。図４のステップ１０２では、１以上のアミノ酸指標、およびウィンドウ幅の入力が可能であり、具体的には記載されてはいないものの、相補度パラメータのフィルター値（例えば、相関係数フィルター値Ｒ_t）、Ｐ_aveフィルター値ａ，ｂ、分子間エネルギーパラメータの閾値（例えば、
【０１３３】
【数１７】

【０１３４】
）等の入力を受け付けることもできる。入力手段としては、図４のステップ１０１で用いた入力手段と同様のものを使用できる。これらのパラメータは、操作時に逐一ユーザーが選択入力する形式であっても、予め初期値が設定されており、所望によりユーザーが変更し得る形式であってもよい。
【０１３５】
上記ステップ（ｂ１）は、図４のステップ１０３に対応する。上記ステップ（ｂ１）では、図４のステップ１０２で設定されている１以上のアミノ酸指標に従って、図４のステップ１０１で入力された標的アミノ酸配列の配列データから１以上のプロファイル波形を生成し、次いで１以上の移動平均プロファイル波形に変換する（図４のステップ１０３）。具体的には、得られた標的アミノ酸配列について上記［式１］で示される演算処理を実行する。標的アミノ酸配列に関する１以上の移動平均プロファイル波形のデータは、図４のステップ１０７へと送られる。
【０１３６】
上記ステップ（ｃ１）は、図４のステップ１０４、ステップ１０５に対応する。上記ステップ（ｃ１）では、相補アミノ酸配列候補が生成され（図４のステップ１０４）、生成された相補アミノ酸配列について、図４のステップ１０２で設定されている１以上のアミノ酸指標に従って１以上のプロファイル波形が生成され、次いで１以上の相補移動平均プロファイル波形に変換される（図４のステップ１０５）。具体的には、得られた相補アミノ酸配列候補について上記［式２］で示される演算処理を実行する。相補アミノ酸配列候補に関する１以上の相補移動平均プロファイル波形のデータは、相補度パラメータの計算のために、図４のステップ１０７へと送られる。
【０１３７】
上記（９）のプログラムは、図４のステップ１０６に対応するステップを含んでいないが、必要に応じて図４のステップ１０６に対応するステップを含むことができる。標的アミノ酸配列のアミノ酸残基数がｎの場合、２０ⁿ個の相補アミノ酸配列候補が生成され、それらのそれぞれが１以上の相補移動平均プロファイル波形に変換されるまで、図４のステップ１０６は、図４のステップ１０４、ステップ１０５を繰り返すように指令する。２０ⁿ個の相補アミノ酸配列候補が生成され、それらのそれぞれが１以上の相補移動平均プロファイル波形に変換された場合には、相補アミノ酸配列候補の生成（図４のステップ１０４）およびそれに伴う相補移動平均プロファイル波形の生成（図４のステップ１０５）が終了する。
【０１３８】
上記ステップ（ｄ１）は、図４のステップ１０７に対応する。上記ステップ（ｄ１）では、同一のアミノ酸指標に由来する１以上の相補度パラメータ（例えば、［式３］で示される相関係数）が、標的アミノ酸配列に対する１以上の移動平均プロファイル波形と相補アミノ酸配列候補の１以上の相補移動平均プロファイル波形との間でそれぞれ計算される（図４のステップ１０７）。
【０１３９】
例えば、標的アミノ酸配列の移動平均プロファイル波形および相補アミノ酸配列の相補移動平均プロファイル波形への変換の際に、１つのアミノ酸指標のみを使用した場合には、１つの相補度パラメータのみが図４のステップ１０７で算出される。
【０１４０】
また、標的アミノ酸配列の移動平均プロファイル波形および相補アミノ酸配列の相補移動平均プロファイル波形への変換の際に、２以上のアミノ酸指標を使用した場合には、２以上の相補度パラメータが図４のステップ１０７で算出される。
【０１４１】
上記（９）のプログラムは、図４のステップ１０８に対応するステップを含んでいないが、必要に応じて図４のステップ１０８に対応するステップを含むことができる。図４のステップ１０８では、使用された１以上のアミノ酸指標に基づいて１以上の平均値パラメータが算出される。
【０１４２】
上記ステップ（ｅ１）は、図４に具体的には示されていない。しかし、上記ステップ（ｅ１）を、図４のステップ１０８とステップ１０９との間に含むことができる。上記ステップ（ｅ１）では、相補アミノ酸配列候補が、図４のステップ１０７で算出された１以上の該相補度パラメータ（必要に応じて、図４のステップ１０８で算出された１以上の平均値パラメータ）とともに記憶手段に記憶される。
【０１４３】
上記ステップ（ｆ１）は、図４のステップ１０９に対応する。上記ステップ（ｆ１）では、図４のステップ１０７で算出された１以上の相補度パラメータ（必要に応じて、図４のステップ１０８で算出された１以上の平均値パラメータ）に基づき相補アミノ酸配列候補を抽出する。抽出は、フィルター値による条件に基づいて行なわれる。フィルター値は、操作時に逐一ユーザーが選択入力する形式であっても、予め初期値が設定されており、所望によりユーザーが変更し得る形式であってもよい。
【０１４４】
上記ステップ（ｄ１）において標的アミノ酸配列と相補アミノ酸配列との間で１つの相補度パラメータのみが算出される場合には、上記ステップ（ｆ１）において、相補アミノ酸配列候補は、その１つの相補度パラメータに対するフィルター値の条件を満たすように抽出される。なお、この抽出処理において、１つの平均値パラメータのフィルター値を併用することもできる。
【０１４５】
上記ステップ（ｄ１）において、標的アミノ酸配列と相補アミノ酸配列との間で２以上の相補度パラメータが算出される場合には、上記ステップ（ｆ１）において、２以上の相補度パラメータを総合的に考慮し、相補アミノ酸配列候補を抽出する。なお、この抽出処理において、２以上の平均値パラメータを総合的に考慮して併用することもできる。当業者は、２以上の相補度パラメータ（必要に応じて、２以上の平均値パラメータ）を算出した場合には、重要視するパラメータを優先的に考慮し、所望の相補アミノ酸配列候補を抽出するように条件設定することができる。
【０１４６】
上記プログラム（９）は、図４のステップ１１０に対応するステップを含んでいないが、必要に応じて図４のステップ１１０に対応するステップを含むこともできる。図４のステップ１１０では、別のアミノ酸指標を用いて相補アミノ酸配列候補をさらに選別するか否かが決定される。具体的には、上記ステップ（ｆ１）で抽出された相補アミノ酸配列候補について、図４のステップ１０２で設定されているアミノ酸指標（以前のアミノ酸指標とは異なる）に従い、さらに上記ステップ（ｂ１）〜（ｆ１）を１回以上繰り返すか否かが決定される。上記ステップ（ｂ１）〜（ｆ１）を１回以上繰り返すか否か、その繰り返す回数、およびその際に使用する１以上のアミノ酸指標は、操作時に逐一ユーザーが選択入力する形式であっても、予め設定されており、所望によりユーザーが変更し得る形式であってもよい。
【０１４７】
図４のステップ１１０において、別のアミノ酸指標を用いて相補アミノ酸配列候補をさらに選別する必要がないと判断される場合には、図４のステップ１１１へと進み、標的タンパク質との相互作用を考慮するか否かが決定される。なお、上記プログラム（９）は、標的アミノ酸配列との相互作用を考慮するためのものであり、標的タンパク質自体との相互作用を考慮しない実施形態であるため、図４のステップ１１１では常にＮと判断される。
【０１４８】
上記ステップ（ｇ１）は、図４に示されていないが、図４のステップ１１１のＮの後に含めることもできる。上記ステップ（ｇ１）は、標的アミノ酸配列に対する相補アミノ酸配列候補を、その相補度パラメータ等とともに表示する。表示手段としては、通常のディスプレイ装置やプリンター等が用いられる。好ましくは、抽出された相補アミノ酸配列候補は、各パラメータ毎に順位付けされて上位から、又は各パラメータを総合的に考慮して上位から表示される。
【０１４９】
次に、上記（１３）のプログラムについて以下に詳述する。このプログラムは、標的アミノ酸配列との相互作用に加え、標的タンパク質の標的部位との相互作用をも考慮するものである。
【０１５０】
上記（１３）のプログラムの上記ステップ（ａ１’）〜（ｆ１’）は、上記（９）のプログラムのステップ（ａ１）〜（ｆ１）に対応している。従って、上記（１３）のプログラムのステップ（ａ１’）〜（ｆ１’）は、上記（９）のプログラムのステップ（ａ１）〜（ｆ１）と同様に行なわれる。但し、図４のステップ１０２では、標的タンパク質に関するデータ（例えば、標的タンパク質のアミノ酸配列データ、標的タンパク質の標的部位についてのデータ等）を入力することが可能である。なお、上記プログラム（１３）は、標的タンパク質自体との相互作用を考慮する実施形態であるため、図４のステップ１１１では常にＹと判断される。
【０１５１】
上記ステップ（ｇ１’）は、図４のステップ１１２に対応する。ステップ（ｇ１’）では、標的タンパク質の標的部位と相補アミノ酸配列候補との分子間エネルギーパラメータが計算される。この計算は、図４のステップ１０２に入力されている標的タンパク質に関するデータ（例えば、標的タンパク質のアミノ酸配列データ、標的タンパク質の標的部位についてのデータ等）、相補アミノ酸配列候補の配列データ等を利用して実行される。
【０１５２】
上記ステップ（ｈ１’）は、図４には示されていないものの、図４のステップ１１２とステップ１１３との間に含めることができる。上記ステップ（ｈ１’）では、相補アミノ酸配列候補が、図４のステップ１１２で算出された分子間エネルギーパラメータとともに記憶手段に記憶される。
【０１５３】
上記ステップ（ｉ１’）は、図４のステップ１１３に対応する。上記ステップ（ｉ１’）では、記憶手段に記憶された情報に基づき、分子間エネルギーパラメータの閾値条件を満たす相補アミノ酸配列候補が抽出される。
【０１５４】
上記ステップ（ｊ１’）は、図４に示されてはいないものの、図４のステップ１１３のＹの後に含めることができる。上記ステップ（ｊ１’）では、標的アミノ酸配列に対する相補アミノ酸配列候補を、その相補度パラメータ等とともに表示する。表示手段としては、通常のディスプレイ装置やプリンター等が用いられる。好ましくは、抽出された相補アミノ酸配列は、良好な分子間エネルギーパラメータに基づいて順位付けして表示される。
【０１５５】
また、上記ステップ（ｉ１’）とステップ（ｊ１’）の間に、上記ステップ（Ｉ）〜（ＩＩＩ）を含めることができる。上記ステップ（Ｉ）〜（ＩＩＩ）は、図５のステップ２０１〜２０３にそれぞれ対応する。以下、上記ステップ（Ｉ）〜（ＩＩＩ）について詳述する。
【０１５６】
上記ステップ（Ｉ）は、図５のステップ２０１に対応する。上記ステップ（Ｉ）では、上記ステップ（ｉ１’）で抽出されたアミノ酸配列に対しアミノ酸変異を導入したアミノ酸配列が生成される。上記ステップ（ｉ１’）で抽出されたアミノ酸配列に対し、１つのアミノ酸を置換する場合には、もとのアミノ酸以外の１９種類の天然アミノ酸で置換したアミノ酸配列が網羅的に生成される。また、上記ステップ（ｉ１’）で抽出されたアミノ酸配列に対し、複数（２又は３以上）のアミノ酸を置換して、それらのアミノ酸配列が網羅的に生成される（例えば、２個のアミノ酸を他の天然アミノ酸で置換する場合には１９×１９個のアミノ酸配列が生成される）。さらに、天然アミノ酸のみならず非天然アミノ酸配列をもアミノ酸置換に利用可能である。これらのアミノ酸のデータは予め記憶されているものを利用する形式であっても、外部のデータベースを参照して必要なデータを取り込む形式であってもよい。
【０１５７】
上記ステップ（ＩＩ）は、図５のステップ２０２に対応する。上記ステップ（ＩＩ）では、上記ステップ（Ｉ）で生成された全てのアミノ酸配列と標的タンパク質の標的部位との分子間エネルギーパラメータが計算される。この計算は、上記ステップ（ｇ１’）と同様にして行なわれる。
【０１５８】
上記ステップ（ＩＩＩ）は、図５のステップ２０３に対応する。上記ステップ（ＩＩＩ）では、上記ステップ（ｉ１’）で抽出されたアミノ酸配列と標的タンパク質の標的部位との分子間エネルギーパラメータを対照として、上記ステップ（ＩＩ）で計算された分子間エネルギーパラメータを比較し、対照の分子間エネルギーパラメータよりも安定な分子間エネルギーパラメータを有するアミノ酸配列を抽出する。上記ステップ（ｉ１’）で抽出されたアミノ酸配列と標的タンパク質の標的部位との分子間エネルギーパラメータとしては、ステップ（ｇ１’）で計算された値を使用することができる。比較の結果、対照の分子間エネルギーパラメータよりも安定な分子間エネルギーパラメータを有するアミノ酸配列が抽出される。
【０１５９】
なお、上記ステップ（ＩＩＩ）の後に、図５のステップ２０４を含ませることもできる。図５のステップ２０４では、上記ステップ（ＩＩＩ）で抽出されたアミノ酸配列について上記ステップ（Ｉ）〜（ＩＩＩ）を再度繰り返すかが決定される。図５のステップ２０４において上記ステップ（Ｉ）〜（ＩＩＩ）を再度繰り返す必要がないと判断された場合に、アミノ酸配列の抽出が終了し、上記ステップ（ｊ１’）へと進む。但し、図５のステップ２０４の後に、各アミノ酸側鎖の最適化処理を行なうステップも設けることができる。このような場合には、各アミノ酸側鎖の最適化処理を行なうステップの終了後に、上記ステップ（ｊ１’）へと進む。
【０１６０】
図６は、上記（２５）、及び上記（２６）の一部についてのプログラムフローを示すフローチャートである。このプログラムは、標的タンパク質に相互作用する、若しくは相互作用が予測されるタンパク質の一次構造（アミノ酸配列）が既知である場合に特に有用である。先ず、上記（２５）のプログラムについて以下に詳述する。
【０１６１】
上記（２５）のプログラムにおける上記ステップ（ａ２）は、図６のステップ３０１に対応する。上記ステップ（ａ２）では、標的タンパク質の標的部位と相互作用するタンパク質中の相互作用領域が特定される。標的タンパク質と相互作用しうる領域が既に特定されているタンパク質が存在する場合には、その領域が選択される。１つのタンパク質内に複数の相互作用領域が存在する場合には、それらの複数の領域を相互作用領域として選択する。標的タンパク質と相互作用し得るタンパク質が複数知られている場合には、これらのタンパク質のそれぞれから複数の相互作用領域を選択することもできる。一方、標的タンパク質と相互作用するタンパク質自体は既知であるが、その相互作用する領域が特定されていない場合には、当該分野で周知の方法（例えば、ＲＢＤ法［参考文献５］）によってこの領域を選択することができる。
【０１６２】
上記ステップ（ｂ２）は、図６のステップ３０２に対応する。上記ステップ（ｂ２）では、該相互作用領域から任意の長さのアミノ酸配列を抽出する。上記ステップ（ｂ２）で実行されるアミノ酸配列の抽出についての概要を図７に示す。上記ステップ（ｂ２）で抽出されるアミノ酸配列の長さ（即ち、アミノ酸残基の数）としては、上記の相互作用領域の全長以内であれば、任意の長さのアミノ酸配列を抽出することが可能である。なお、アミノ酸配列の抽出は網羅的に行なわれる。例えば、Ｘ個のアミノ酸残基からなるアミノ酸配列を抽出する場合には、上記の相互作用領域のＮ末端からＣ末端にかけて、Ｎ−Ｘ＋１個のアミノ酸配列を抽出する。また、抽出されるアミノ酸配列の長さは統一してもよいが、異なる長さのアミノ酸配列についても網羅的に抽出してもよい。
【０１６３】
次に、上記（２６）のプログラムについて以下に詳述する。
【０１６４】
上記（２６）のプログラムの上記ステップ（ａ２’）〜（ｂ２’）は、上記（２５）のプログラムの上記ステップ（ａ２）〜（ｂ２）に対応している。従って、上記（２６）のプログラムの上記ステップ（ａ２’）〜（ｂ２’）は、上記（２５）のプログラムの上記ステップ（ａ２）〜（ｂ２）と同様に行なわれる。
【０１６５】
また、上記（２６）のプログラムの上記ステップ（ｃ２’）〜（ｆ２’）は、上記（１３）のプログラムの上記ステップ（ｇ１’）〜（ｊ１’）に対応している。従って、上記（２６）のプログラムの上記ステップ（ｃ２’）〜（ｆ２’）は、上記（１３）のプログラムの上記ステップ（ｇ１’）〜（ｊ１’）と同様に行なわれる。
【０１６６】
また、上記ステップ（ｅ２’）とステップ（ｆ２’）の間に、上記ステップ（Ｉ）〜（ＩＩＩ）を含めることができる。上記ステップ（Ｉ）〜（ＩＩＩ）は、上記と同様に行なわれる。
【０１６７】
図８は、上記（２８）、及び上記（２９）の一部についてのプログラムフローを示すフローチャートである。このプログラムは、標的タンパク質として分子表面にポケットを持ち、かつ結合に伴う構造変化が少ないと考えられる酵素など（例えば、ペプチダーゼ）に特に有用である。なお、上記（２８）で行なわれる処理についての概要を図９に示す。以下、上記（２８）のプログラムについて詳述するが、より理解を深めるため、標的タンパク質としてカスパーゼを例に挙げて順次説明する。
【０１６８】
先ず、カスパーゼの基質となるタンパク質について説明し、そのアミノ酸位置について定義する。カスパーゼの基質となるタンパク質の切断部位のアミノ酸配列はＸ−Ｘ−Ｘ−Ｄモチーフである。モチーフ中のＤの位置は、基質切断部位のＮ末端側であるＰ１部位を示している。Ｐ１位はアスパラギン酸を必須とし、各カスパーゼに認識される阻害ペプチドの違いは、残りのＰ２〜Ｐ４位のアミノ酸配列に依存していると考えられる。よって以降は、Ｘ−Ｘ−Ｘ−Ｄモチーフ中の各アミノ酸位置を明確に示すため、Ｐ４−Ｐ３−Ｐ２−Ｄとして表現する。
【０１６９】
上記（２８）のプログラムにおける上記ステップ（ａ３）は、図８のステップ４０１に対応する。上記ステップ（ａ３）では、一定の長さのアミノ酸配列を網羅的に生成し、その中からランダムにアミノ酸配列を選択して解析用ライブラリとして抽出する。なお、上記ステップ（ａ３）には含めていないが、図８のステップ４０１においてさらに評価用ライブラリを抽出するステップを含めることもできる。上記ステップ（ａ３）で網羅的に生成されるアミノ酸配列の「一定の長さ」（即ち、アミノ酸残基の数）は、特に限定されないが、好ましくは、２〜１０個のアミノ酸残基程度の長さであり、より好ましくは、３〜５個のアミノ酸残基程度の長さである。
【０１７０】
例えば、Ｐ４−Ｐ３−Ｐ２−Ｄモチーフを網羅的に解析するには、４個のアミノ酸残基のアミノ酸配列について２０³、即ち８０００通りの組み合わせを考えなければならない（Ｄは固定されている）。ここでは、例えば、その５％に当たる４００個のアミノ酸配列をランダムに選択し、解析用ライブラリとして３６０個のアミノ酸配列、評価用ライブラリとして４０個のアミノ酸配列を抽出することもできる。また、カスパーゼについてはその阻害ペプチドが既知であるが、カスパーゼ阻害ペプチドの複合体結晶構造中の阻害ペプチドは、どのカスパーゼにおいても、ほぼ同じ主鎖構造を保持している。すなわち、カスパーゼ活性部位の形状及び触媒機構がそれを限定しているものと考えられる。このような知見を、ペプチド配座を発生させる際に役立てることもできる。例えば、ペプチド配座を発生させる際、主鎖の構造は結晶構造中の構造を用い、側鎖を付加することにより任意のペプチドを構築することもできる。また、側鎖のＶＤＷ接触を除外するためＴＩＮＫＥＲ[参考文献１２]を用いてエネルギー最適化を実施することもできる。このとき主鎖はＩＮＡＣＴＩＶＥコマンドを用いて固定されてもよい。
【０１７１】
上記ステップ（ｂ３）は、図８のステップ４０２に対応する。上記ステップ（ｂ３）では、解析用ライブラリとして抽出された各アミノ酸配列について分子間エネルギーパラメータを計算する。この計算は、上記プログラム（１３）の上記ステップ（ｇ１’）と同様にして行なわれる。例えば、Ｐ４−Ｐ３−Ｐ２−Ｄモチーフに対して網羅的に生成された８０００通りのアミノ酸配列について、AutoDockを用いて下記［式１８］で示される計算を行うこともできる。
【０１７２】
【数１８】

【０１７３】
上記式の各係数は、３０個のタンパク質−リガンド複合体構造とその実測されたＫi値を用いて回帰分析により経験的に定められた値である。なお、AutoDock3.0より配置探索にはラマルク進化論に基づく遺伝的アルゴリズムを新たに採用している。また、ここでは、４残基ペプチドの主鎖を固定し、側鎖を可変としている。設定されるパラメータ値の例を表２に示す。上記のように、計算条件の詳細については、適宜設定可能である。
【０１７４】
【表２】

【０１７５】
また、上記ステップ（ｂ３）には含めていないが、抽出されたアミノ酸配列の配置（例えば、ＲＭＳ（主鎖）に基づくもの）を、対照（例えば、結晶構造中のペプチド）の配置と比較して、不適切な配置をとるものを以降の計算から除外するステップを上記ステップ（ｂ３）に含めることもできる。例えば、Ｐ４−Ｐ３−Ｐ２−Ｄモチーフのアミノ酸配列について、結晶構造中の阻害ペプチドとのＲＭＳ（主鎖）によりその配置を確認する。そして、ＲＭＳが大きいものについては、カスパーゼ活性部位に適切に配置されず基質としては機能しないと考え、以降の計算から除外することもできる。どのような配置であれ強く結合すれば問題ないと思われるかもしれないが、ＦＭＫ、ＣＨＯのような修飾基がカスパーゼ活性中心に配置されるためには、ペプチドの適切な配置が必須因子となるためである。
【０１７６】
上記ステップ（ｃ３）は、図８のステップ４０３に対応する。上記ステップ（ｃ３）では、上記ステップ（ｂ３）で計算された分子間エネルギーパラメータを用いて、アミノ酸出現頻度に基づくスコアマトリクスが生成される。なお、分子間エネルギーパラメータの閾値（例えば、ΔＧの閾値）は、予め設定されていてもよいし、後述するように設定されてもよい。例えば、３６０個のアミノ酸配列を含む解析用ライブラリを用いて、各ポジションＰ４、Ｐ３、Ｐ２における２０種類のアミノ酸出現頻度に基づいたＰＳＳマトリクスを生成する。ポジションｊにおけるアミノ酸ｉのＰＳＳ_ijは、上記［式９］により計算される。この場合、ポジションｊの範囲は１〜３であり、それぞれＰ４〜Ｐ２に対応し、アミノ酸ｉの範囲は１〜２０であり、それぞれのアミノ酸種に対応する。
【０１７７】
上記ステップ（ｄ３）は、図８のステップ４０４に対応する。上記ステップ（ｄ３）では、アミノ酸出現頻度に基づくスコアマトリクスを用いて、アミノ酸出現頻度に基づくスコアを計算する。例えば、ＰＳＳマトリクスを用いることにより、４個のアミノ酸残基からなる任意のアミノ酸配列（Ｐ４−Ｐ３−Ｐ２−Ｄモチーフ：Ｐ１はＤ固定なので考慮しない）のカスパーゼに対する結合力の強さを、上記［式１０］により、ＰＳＳとして計算することができる。この場合、Ｃ_ijは２０×３のマトリクスであり、０若しくは１の値で構成される。任意のアミノ酸配列のポジションｊにおけるアミノ酸ｉと一致する要素は１、不一致の要素は０とする。
【０１７８】
上記ステップ（ｅ３）は、図８のステップ４０５に対応する。上記ステップ（ｅ３）では、ステップ（ｂ３）で計算された分子間エネルギーパラメータと該スコアとの間で相関解析を行い、回帰式を得る。解析用ライブラリに含まれる各アミノ酸配列について、ＰＳＳと分子間エネルギーパラメータ（例えば、結合自由エネルギー）との間に高い相関性があれば、新たなアミノ酸配列について高速に分子間エネルギーパラメータを予測することが可能となる。もちろんＰＳＳは各ポジション独立にしか評価できないため、ポジション間におけるアミノ酸の組み合わせによる影響は考慮できない。なお、上記ステップ（ｃ３）で述べた分子間エネルギーパラメータの閾値（例えば、ΔＧの閾値）は、ＰＳＳと分子間エネルギーパラメータの閾値（例えば、ΔＧの閾値）との間の相関係数Ｒを最大にするように設定されてもよい。この場合には、相関係数Ｒを最大にするように設定された閾値が、再度、上記ステップ（ｃ３）にフィードバックされ、上記ステップ（ｃ３）、上記ステップ（ｄ３）及び上記ステップ（ｅ３）が再度行なわれる。
【０１７９】
上記ステップ（ｆ３）は、図８のステップ４０６に対応する。上記ステップ（ｆ３）では、該回帰式を用いて、アミノ酸出現頻度に基づくスコアマトリクスがアミノ酸位置依存的な分子間エネルギーパラメータに基づくマトリクスに変換される。この変換では、例えば、上述したＰＳＧマトリクスが作成される。なお、回帰式における定数項は、各ポジションに不均一に分配してもよいが、好ましくは、各ポジションに均一に分配される。
【０１８０】
上記ステップ（ｇ３）は、図８のステップ４０７に対応する。上記ステップ（ｇ３）では、アミノ酸位置依存的な分子間エネルギーパラメータに基づくマトリクスから、アミノ酸位置依存的な分子間エネルギーパラメータ値が計算される。例えば、ＰＳＧマトリクスを用いることにより、任意の４残基ペプチド（Ｐ４−Ｐ３−Ｐ２−Ｄモチーフ：Ｐ１はＤ固定なので考慮しない）とカスパーゼ間の結合自由エネルギーをＰＳＧとして高速に計算することができる。
【０１８１】
上記ステップ（ｈ３）は、図８のステップ４０８に対応する。上記ステップ（ｈ３）では、所定のアミノ酸位置依存的な分子間エネルギーパラメータ値以下のアミノ酸配列、即ち、閾値以下のアミノ酸配列が抽出される。この値は、予め設定されている値を使用してもよいし、抽出時に設定してもよい。
【０１８２】
次に、上記（２９）のプログラムについて以下に詳述する。
【０１８３】
上記（２９）のプログラムの上記ステップ（ａ３’）〜（ｈ３’）は、上記（２８）のプログラムの上記ステップ（ａ３）〜（ｈ３）に対応している。従って、上記（２９）のプログラムの上記ステップ（ａ３’）〜（ｈ３’）は、上記（２８）のプログラムの上記ステップ（ａ３）〜（ｈ３）と同様に行なわれる。
【０１８４】
また、上記（２９）のプログラムの上記ステップ（ｉ３’）〜（ｌ３’）は、上記（２６）のプログラムの上記ステップ（ｃ２’）〜（ｆ２’）に対応している。従って、上記（２９）のプログラムの上記ステップ（ｉ３’）〜（ｌ３’）は、上記（２６）のプログラムの上記ステップ（ｃ２’）〜（ｆ２’）と同様に行なわれる。
【０１８５】
さらに、上記ステップ（ｋ３’）とステップ（ｌ３’）の間に、上記ステップ（Ｉ）〜（ＩＩＩ）を含めることができる。上記ステップ（Ｉ）〜（ＩＩＩ）は、上記と同様に行なわれる。
【０１８６】
また、基質特異性が似ている複数のタンパク質が存在している場合に、これらタンパク質のうちの１つを標的タンパク質として選択し、これに特異的なペプチドを設計する際にも、上記（２８）および（２９）のプログラムは有用である。例として、上記（２８）のプログラムにより説明する。まず、基質特異性が似ている各タンパク質について、上記プログラム（２８）の上記ステップ（ａ３）〜（ｈ３）を行なう。なお、各タンパク質について、上記ステップ（ａ３）〜（ｈ３）を並行して行ってもよいし、上記ステップ（ａ３）〜（ｈ３）を順番に行なってもよい。次いで、標的タンパク質とそれ以外のタンパク質について、アミノ酸位置依存的な分子間エネルギーパラメータ値（例えば、ＰＳＧ）の差を計算し、その差によってフィルターをかけるステップを設ける。この差は、適宜設定可能である。例えば、標的タンパク質に対するＫｉ値が、他のタンパク質のＫｉ値よりも二桁低いものを希望する場合には、ΔＧでは、２．７２８ｋｃａｌ／ｍｏｌに相当するため、これをフィルターとして用いることができる。もちろん、上記プログラム（２９）の上記ステップ（ｈ３’）の後に、同様のステップを設け、次いで上記ステップ（ｉ３’）へと進むこともできる。簡単に説明したが、本発明は、上記（２８）および（２９）のプログラムに、さらに上述したようなステップを含むこともできる。このようなプログラムも本発明の範囲に含まれる。なお、さらなる具体例については、実施例３に記載されており、このようなプログラムの理解に役立つであろう。
【０１８７】
本発明の上記（１７）の記録媒体は、上記した本発明のプログラム（９）〜（１６）が、コンピュータ読み取り可能な記録媒体に記録されたものである。ここで「コンピュータ読み取り可能な記録媒体」とは、電子データを記録することができ、且つ必要に応じてコンピュータが読み出すことができる任意の記録媒体をいい、例えば、磁気テープ、磁気ディスク、磁気ドラム、IＣカード、光読み取り式ディスク（例えば、ＣＤ、ＤＶＤ）等の可搬情報記録媒体が挙げられる。
【０１８８】
本発明による上記（１８）〜（２０）の抽出処理装置、上記（２１）〜（２３）の抽出処理装置、および上記（３１）、（３２）の装置は、中央処理装置とメモリとを備えたコンピュータを主体として構成され、上記した本発明のプログラムを実行可能なように有する、生理活性ペプチド抽出処理のための専用機である。
【０１８９】
上記（１８）〜（２３）の装置は、標的アミノ酸配列と相互作用する生理活性ペプチドの設計、および標的タンパク質と相互作用する生理活性ペプチドの設計という観点から大きく２つに分けることができる。一方は、上記（１８）〜（２０）に示す装置であり、これは、標的アミノ酸配列と相互作用する生理活性ペプチドの設計を行うものである。他方は、上記（２１）〜（２３）に示す装置であり、これは、標的アミノ酸配列との相互作用に加え、標的タンパク質自体との相互作用を考慮して生理活性ペプチドの設計を行うものである。
【０１９０】
上記（１８）〜（２０）の装置は、図１０に示すように、データ入力部イ、データ編集部ロ、相補アミノ酸配列候補生成部ハ、相補度計算部ニ、相補アミノ酸配列候補記憶部ホ、相補アミノ酸配列探索部ヘ、及び相補アミノ酸配列表示部トを含んで構成される。
上記（１８）〜（２０）の装置において、該データ入力部イは、上記ステップ（ａ１）を実行する手段を含み、該データ編集部ロは、上記ステップ（ｂ１）を実行する手段を含み、該相補アミノ酸配列候補生成部ハは、上記ステップ（ｃ１）を実行する手段を含み、該相補度計算部ニは、上記ステップ（ｄ１）を実行する手段を含み、該相補アミノ酸配列候補記憶部ホは、上記ステップ（ｅ１）を実行する手段を含み、該相補アミノ酸配列探索部ヘは、上記ステップ（ｆ１）を実行する手段を含み、該相補アミノ酸配列表示部トは、上記ステップ（ｇ１）を実行する手段を含む。
【０１９１】
上記（２１）〜（２３）の装置は、上記（１８）〜（２０）の装置と同様、図１０に示すように、データ入力部イ、データ編集部ロ、相補アミノ酸配列候補生成部ハ、相補度計算部ニ、相補アミノ酸配列候補記憶部ホ、相補アミノ酸配列探索部ヘ、及び相補アミノ酸配列表示部トを含んで構成される。
上記（２１）〜（２３）の装置において、該データ入力部イは、上記ステップ（ａ１’）を実行する手段を含み、該データ編集部ロは、上記ステップ（ｂ１’）を実行する手段を含み、該相補アミノ酸配列候補生成部ハは、上記ステップ（ｃ１’）を実行する手段を含み、該相補度計算部ニは、ステップ（ｋ１’）同一のアミノ酸指標に由来する相補度パラメータを、該標的アミノ酸配列に対する１以上の移動平均プロファイル波形と相補アミノ酸配列候補の１以上の相補移動平均プロファイル波形との間でそれぞれ計算し、さらに標的タンパク質の標的部位との分子間エネルギーパラメータを計算する手段（上記ステップ（ｄ１’）（ｇ１’）を実行する手段）を含み、該相補アミノ酸配列候補記憶部ホは、ステップ（ｌ１’）相補アミノ酸配列候補を、該相補度パラメータおよび該分子間エネルギーパラメータとともに記憶する手段（上記ステップ（ｅ１’）（ｈ１’）を実行する手段）を含み、該相補アミノ酸配列探索部ヘは、ステップ（ｍ１’）手段（ｋ１’）によって記憶された情報に基づいて、所定の数の相補アミノ酸配列を抽出する手段（上記ステップ（ｆ１’）（ｉ１’）を実行する手段）を含み、該相補アミノ酸配列表示部トは、ステップ（ｎ１’）該相補アミノ酸配列探索部によって抽出された相補アミノ酸配列を、生理活性ペプチドの候補として表示する手段（上記ステップ（ｉ１’）を実行する手段）を含む。
【０１９２】
上記（３１）、（３２）の装置についても、上記（１８）〜（２３）の装置と同様であって、上記（３１）の装置に含まれる「（イ２）相互作用領域特定部、（ロ２）アミノ酸配列第１探索部、（ハ２）分子間エネルギー計算部、（ニ２）アミノ酸配列記憶部、（ホ２）アミノ酸配列第２探索部、（ヘ２）アミノ酸配列表示部」、および、上記（３２）の装置に含まれる、「（イ３）アミノ酸配列第１探索部、（ロ３）分子間エネルギー第１計算部、（ハ３）スコアマトリクス生成部、（ニ３）スコア計算部、（ホ３）回帰式形成部、（ヘ３）マトリクス変換部、（ト３）アミノ酸位置依存的エネルギー計算部、（チ３）アミノ酸配列第２探索部、（リ３）分子間エネルギー第２計算部、（ヌ３）アミノ酸配列記憶部、（ル３）アミノ酸配列第３探索部、（オ３）アミノ酸配列表示部」は、いずれも、上記（２６）、（２９）のプログラムと、該プログラムを実行するよう構成されたコンピュータ（中央演算装置（ＣＰＵ）、記憶装置（メモリー））、その他、必要に応じてさらに加えられる周辺装置（外部記憶装置、入力装置、表示装置など）によって構成され、さらには他のコンピュータとのネットワークを構成に加えてもよい。
上記（３１）の装置に含まれる各部（イ２）〜（ヘ２）、上記（３２）の装置に含まれる各部（イ３）〜（オ３）については、それぞれ、上記（２６）、（２９）のプログラムの説明において、詳細に述べたとおりである。
【０１９３】
本発明装置は、表示されたデータを印刷するプリンター等の出力装置や、データを保存するための外部記憶装置、本発明のプログラムの実行に必要なデータベースが保存された外部記憶装置等、生理活性ペプチドの設計においてユーザーに利便性を与える他の機器を、構成としてさらに包含することができる。
【０１９４】
【実施例】
以下、実施例を示して本発明を説明するが、本発明は、以下の特定の実施例に限定されるものではない。
【０１９５】
実施例１ カスパーゼ−３に対する生理活性ペプチド（阻害ペプチド）の設計カスパーゼ−３の２０６〜２０９位のアミノ酸配列ＷＲＮＳ(配列番号１０９)を標的アミノ酸配列として、それと結合しカスパーゼ−３の活性を阻害するペプチド候補を本発明のプログラムを用いて予測した。アミノ酸指標として疎水度に基づく指標［参考文献３］を用い、ウィンドウ幅は１に設定した。Ｐ_aveの範囲は−０．３９から−０．３７、Ｒ_tは−０．９と設定し１次スクリーニングにより、１０５個の相補アミノ酸配列を有するペプチド候補を得た（表３）。なお、表３の順位１〜１０５の配列を、それぞれ順番に、配列番号４〜１０８とする。
【０１９６】
【表３】

【０１９７】
次いで２次スクリーニングにより結合力の強い相補アミノ酸配列ＤＮＬＤ（配列番号９７）（Ｋ_i＝０．０３０９ｎＭ：１位）、ＤＥＶＤ（配列番号８８）（Ｋ_i＝０．１４９ｎＭ：２位）を得た（表３）。
【０１９８】
ＤＥＶＤ（配列番号８８）はカスパーゼ−３阻害ペプチドとして既知のアミノ酸配列でありＫ_iの実測値および結晶構造が知られている。図１１は本発明により予測された相補アミノ酸配列ＤＥＶＤと結晶構造との比較図を示す。ＲＭＳ (root mean squar, 原子間の平均的ずれ)（全原子）が２．４Åと結晶構造と非常に近い構造予測に成功している。又、本発明により予測されたＫ_i値が０．１４９ｎＭに対し、実測値［参考文献１３］Ｋ_iは０．２３ｎＭであった。
【０１９９】
また、現在知られていないＤＮＬＤ（配列番号９７）というＫ_i値が最も低い０．０３０９ｎＭの相補アミノ酸配列を得た。ＤＥＶＤはもともと基質となるタンパク質のアミノ酸配列より得られたものであり、しかもカスパーゼ−３以外にもカスパーゼ−７、カスパーゼ−８に強く結合［参考文献１３］し、阻害するという特性があり、特異的阻害剤としての問題点も指摘されている。本システムで提示されるペプチド配列ＤＮＬＤは全くの新規配列であるため、この特異性の問題を解決できる可能性も与える。
【０２００】
以上の結果から、本発明のプログラムは、相補アミノ酸候補の設計において非常に有用であることが確認された。
【０２０１】
実施例２ Ｆａｓ（Receptor）に対する生理活性ペプチドの設計
Ｆａｓ（Receptor）の97〜110位のアミノ酸配列ＦＳＳＫＣＲＲＣＲＬＣＤＥＧ（配列番号１）を標的アミノ酸配列として、それと結合して相互作用し得る生理活性ペプチドの候補を、本発明のプログラムを用いて予測した。アミノ酸指標として疎水度に基づく指標［参考文献３］を用い、ウィンドウ幅は５に設定した。Ｐ_aveの範囲は、−０．１５から＋０．１５、Ｒ_tは−０．９と設定し、１次スクリーニングにより、相補アミノ酸配列を有するペプチド候補を得た。次いで、２次スクリーニングにおいて分子間エネルギーを計算した。最終的に相補アミノ酸配列ＥＰＰＭＴＦＩＳＩＨＴＭＣＨ（配列番号２）を得た。
【０２０２】
試験例１ 相補アミノ酸配列（配列番号２）からなるペプチドによるアポトーシスの誘導
上記実施例１で得られた相補アミノ酸配列（配列番号２）からなるペプチド（以下、Ｆａｓ相補ペプチドと省略する）を化学合成した。次いで、Ｆａｓを発現しているヒト卵巣癌培養細胞株ＮＯＳ４を用いて、Ｆａｓ相補ペプチドによるアポトーシス誘導能をそのスクランブルペプチドＴＦＩＨＰＳＭＨＴＣＭＰＥＩ（配列番号３）と比較解析した。なお、ＮＯＳ４は、重症の卵巣癌患者から切除した癌細胞より、本発明者らの研究室で樹立され、維持されている。ヒト卵巣癌培養細胞株ＮＯＳ４は、１０％ウシ胎仔血清含有ＲＰＭＩ培地中、５％ＣＯ₂湿気下、３７℃で培養した。５×１０⁶個のヒト卵巣癌培養細胞株ＮＯＳ４を、１００μｇ／ｍｌの相補ペプチドの存在下で３７℃、２４時間処理し、次いでＤＮＡ断片化を測定した。ＤＮＡ断片化は、細胞核をヨウ化プロピジウムで染色し、ＦＡＣＳ装置（FACSCalibur, Jose., CA）を用いたフローサイトメトリーにより測定した。その結果、Ｆａｓ相補ペプチドは、１００μｇ／ｍｌの濃度において、約４０％の細胞でアポトーシスを誘導した。以上の結果により、Ｆａｓ相補ペプチドがＦａｓに対する生理活性ペプチドとして機能することが確認された。
【０２０３】
【表４】

【０２０４】
試験例２ Ｆａｓ相補ペプチドテトラマーによるアポトーシスの誘導
Ｆａｓ相補ペプチド（以下、ＦＲＰ−２と省略する場合がある）をリジンポリマー（ＭＡＰ）の４ブランチに結合させたＦａｓ相補ペプチドのテトラマー［（ＦＲＰ−２）₄−ＭＡＰ］を化学合成した。次いで、（ＦＲＰ−２）₄−ＭＡＰのアポトーシス誘導能を上記試験例１と同様の方法を用いて調べた。その結果、Ｆａｓ相補ペプチドのテトラマーは、５ｍｇ／ｍｌの濃度において、約５０％のヒト卵巣癌培養細胞株ＮＯＳ４にアポトーシスを誘導した（図１２）。以上の結果から、Ｆａｓ相補ペプチドはＭＡＰを用いてマルティマー（maltimer）にすることによって、モノマーに比べ約３０倍強いアポトーシス誘導能を有することが判明した。
【０２０５】
試験例３ Ｆａｓ相補ペプチドのテトラマーによるin vivoにおけるアポトーシス誘導
ヒトグリオーマ細胞株（Ｕ２５１−ＳＰ）をヌードマウスの脳内に移植した担癌動物実験系を用いて、（ＦＲＰ−２）₄−ＭＡＰのin vivoにおける抗腫瘍効果を調べた。Ｕ２５１−ＳＰをヌードマウスの脳内に移植して１週間後に、（ＦＲＰ−２）₄−ＭＡＰを２μｇ／２μｌで脳内の癌組織に局所内注入した。３０日後に解剖し、常法によりホルマリン固定による脳組織切断プレパラートを作成し、光学顕微鏡下で観察した。その結果、Ｆａｓ相補ペプチドのテトラマー処理群では、アポトーシスの誘発による癌細胞死による癌の萎縮が認められた（図１３）。以上の一連の結果から、本発明のプログラムは、生理活性ペプチドの設計において非常に有用であることが確認された。
【０２０６】
実施例３ 既存のカスパーゼペプチド阻害剤の評価
まず初めに、カスパーゼ-3,-7,-8,-9を用いて１次スクリーニングを実施した。結果を図１４〜１７に示す。どのカスパーゼにおいてもPSSとΔG_calcの間に平均-0.71の相関係数R (図１４Ｄ、１５Ｄ、１６Ｄ、１７Ｄ)を得ることができた。
【０２０７】
次いで、PSSの予測能力を評価するために、評価用ライブラリに含まれる40ペプチドのPSSを用いΔG_calcとの相関解析（図１４Ｅ、１５Ｅ、１６Ｅ、１７Ｅ）を実施した。解析ライブラリと同様の値、平均-0.66の相関係数Rを得ることができた。したがって、膨大なペプチドライブラリの１次スクリーニングとして十分に利用可能であると言える。
【０２０８】
各カスパーゼのPSG評価は、実測Ki値(表５)[参考文献１３]が既知の阻害ペプチドAc-WEHD-CHO（配列番号１１０）, Ac-YVAD-CHO（配列番号１１１）, Ac-DEVD-CHO（配列番号１１２）, Boc-IETD-CHO（配列番号１１３）, Boc-AEVD-CHO（配列番号１１４）を用いて実施した。
【０２０９】
【表５】

【０２１０】
Ki値が、10,000nM以上のものは10,000nMとして設定し、Ki値は[式１９]によりΔGに変換した(表６)。
【０２１１】
【数１９】

【０２１２】
【表６】

【０２１３】
また、5つの阻害ペプチドの予測ΔGは、カスパーゼ-3,-7,-8,-9それぞれのPSGマトリクスを用いて[式4]により計算した。実測値との比較を表６に示す。
【０２１４】
全体的に予測値は実測値と比較して低い値となっているが、相関係数R_pepを計算すると、その値は平均0.93と高い。これはカスパーゼに対する阻害ペプチドの相対的な親和性予測が可能であることを示す。また相関係数R_caspを計算すると、その値の平均は0.71であった。これはカスパーゼに対する阻害ペプチドの特異性予測が可能であることを示す。
【０２１５】
実施例４ カスパーゼ-3特異的阻害ペプチドの設計
PSGマトリクスは高速な親和性及び特異性予測を可能とする。P4-P3-P2-Dで表現可能な8000ペプチドの各カスパーゼに対するΔGは、PSGを用いて高速に計算することができる。カスパーゼ-3特異的阻害ペプチド設計を例として述べる。システム構成は図１８に示す。
【０２１６】
まず、8000ペプチドを含むVirtual Libraryからペプチドを１つ取り出し、カスパーゼ-3,-7,-8,-9それぞれのPSGマトリクスを用いて、PSG_casp-3, PSG_casp-7, PSG_casp-8, PSG_casp-9を計算する。次に下記[式２０]を用いてカスパーゼ-7,-8,-9とカスパーゼ-3とのPSGの差を計算する。
【０２１７】
【数２０】

【０２１８】
本システムにおいてカスパーゼ-3特異的阻害ペプチド候補は、カスパーゼ-7,-8,-9全てに対して二桁低いKi値を有するペプチドとして定義した。Ki値二桁の差はΔＧでは約2.728kcal/molの差に相当する。よって各カスパーゼのフィルターとして[式２１]を用いた。
【０２１９】
【数２１】

【０２２０】
以上の操作を8000ペプチド全てに実施し、各カスパーゼのフィルターを全て通過できたペプチドのみが２次スクリーニングにより評価されることになる。最終的に２次スクリーニングで選定された阻害ペプチドを設計ペプチドとしてin vitro及びin vivoで評価する。
【０２２１】
カスパーゼ-7,-8,-9全てにおいて[式２０]を満たすペプチド配列を8000ペプチド配列より選択した。結果を表７に示す。
【０２２２】
【表７】

【０２２３】
表７では、各カスパーゼに対するPSGと２次スクリーニング結果であるΔGcalcが示されている。PPVD（配列番号１１５）, QPVD（配列番号１１６）, TPVD（配列番号１１７）の３ペプチドに関してはPSGとΔGcalcの間に0.93以上の高い相関が見られた。一方SPVD（配列番号１１８）では上記３ペプチドと比較して、0.73という低い相関であった。SPVDのカスパーゼ-8に対する結合自由エネルギーはPSGでは-9.99kcal/molと予測されたのに対しΔGcalcでは-10.92kcal/molであった。カスパーゼ-3に対するΔGcalcが-11.20kcal/molと高く評価されたことも重なって、カスパーゼ-3,-8においてΔGcalcの差が0.28kcal/molしかない。この結果からカスパーゼ-3特異的阻害ペプチドとして機能しないことが示唆される。
【０２２４】
PPVD,QPVD,TPVDにおいてもカスパーゼ-3に対するΔGcalcがPSGと比較して高く予測されているため、Ki値で二桁差までは期待できないもののカスパーゼ-3阻害ペプチドとして十分に機能するものと考えられる。
【０２２５】
結果としてカスパーゼ-3特異的阻害ペプチドとして以下の３つの候補ペプチドを提示する。
【０２２６】
【表８】

【０２２７】
実施例５ FasL中のFas結合領域を用いたアポトーシス誘導ペプチドの設計
断片化ペプチドライブラリの作成はまずFas Ligandの細胞外領域にあたる144〜281位をRBD法にかけ、Fasとの結合領域を151〜176位と同定した。次に、この限定領域を,Ｎ末側から4残基ずつ抽出し、計23個の断片化ペプチドを得た。
【０２２８】
Fas(Receptor)に対してもFasLとの結合領域をRBD法により99〜102位に同定した。この99〜102位のアミノ酸配列SKCR（配列番号１１９）を標的領域として、それと結合して相互作用し得るペプチドを、断片化ペプチドライブラリを用いて、２次スクリーニングより選別した。
【０２２９】
結果として、Fas Ligand上の領域162〜165のアミノ酸配列WEDT（配列番号１２０）(Ki=0.19μM)がもっとも結合力のある配列であった(図１９)。この領域は、Fas-Fas Ligand複合体モデルからもFASとの結合領域であることが確認されている。
【０２３０】
次に、３次スクリーニングとして、WEDTをリードペプチドとして1残基目から4残基目まで一残基アミノ酸置換を実施し、変異を実施した(表９)。
【０２３１】
【表９】

【０２３２】
表９より、WEWT（配列番号１２１）のKi値は0.083μMであった。WEWTはWEDTよりもΔGが0.52kcal/mol低くなり、より結合力のあるペプチドと予測され、WEWTを生理活性ペプチド候補として設計した。
【０２３３】
試験例４ FasL様ペプチドテトラマーによるアポトーシス誘導
Fasは3量体となって作用することから、Fas3量体の形成する空間37Å四方にWEWTペプチド（配列番号１２１）がうまくはまり込むようにするために、本ペプチドをMAP-8の4アミノ基を保護化した残り4つのブランチに結合させMAP化し、候補ペプチドを作成した(以下,(FLLP-1)₄-MAP₈と略す。)(図２０)。次いで、(FLLP-1)₄-MAP₈のアポトーシス誘導能を上記試験例１と同様の方法を用いて調べた。その結果、(FLLP-1)₄-MAP₈は、約0.1μg/mlの濃度において、約50%のヒト卵巣癌培養細胞株NOS4にアポトーシスを誘導した(表１０)。
【０２３４】
【表１０】

【０２３５】
以上の結果より、本手法は極めて有用であることが確認された。
【０２３６】
参考文献
[1] Nature, 411, 613 (2001)
[2] Morris,G.M. et al, J. Comp. Chem.,19, 1639-1662, (1998)
[3] Eisenberg D, et al., J. Ann. Rev. Biochem., 53, 596-623 (1984)
[4] Cosic I, et al., J IEEE Trans. Biomed. Eng., 32, 337-341 (1985)
[5] Gallet X. et al, J. Mol. Biol., 302, 917-926 (2000)
[6] Zhao, Y. et al., J. Immunol. 167, 2130-2141 (2001)
[7] Eisenberg D, et al., Proc. Natl. Acad. Sci. USA, 81, 140 (1984)
[8] Allinger NL, et al. J. Am. Chem. Soc., 99, 8127-8134 (1977)
[9] Weiner SJ, et al., J. Am. Chem. Soc., 106, 765-784 (1984)
[10] Brooks BR, et al., J. Comput. Chem., 4, 187 (1983)
[11] Wang J, et al., Proteins, 36, 1-19 (1999)
[12] Pappu,R.V. et al, J. Phys. Chem. B, 102, 9725-9742 (1998)
[13] Garcia-Calvo M, et al., J. Biol. Chem., 273, 32608-32613 (1998)
【０２３７】
【発明の効果】
本発明によれば、費用及び時間のかかる生化学的手法や、信頼性に乏しく候補の絞り込みが不可能な従来の生理活性ペプチド予測理論を用いることなく、経済的で迅速かつ効率的な生理活性ペプチドの設計が数学的計算によって可能となる。また、本発明によれば、１次スクリーニングとして複数の評価法を、標的タンパク質の性質に応じて適宜選択できる。さらに、本発明によれば、３次スクリーニングにおいてアミノ酸置換を施しそれらを評価することによって、最適化されたアミノ酸配列を有する生理活性ペプチドの取得が可能になる。
【０２３８】
【配列表フリーテキスト】
配列番号１：Ｆａｓの９７〜１１０位のアミノ酸配列。
配列番号２：Ｆａｓの９７〜１１０位のアミノ酸配列に相補的なアミノ酸配列。
配列番号３：配列番号２のアミノ酸配列をスクランブルしたアミノ酸配列。
配列番号４〜１０８：カスパーゼ−３の２０６〜２０９位のアミノ酸配列に相補的なアミノ酸配列候補。
配列番号１０９：カスパーゼ−３の２０６〜２０９位のアミノ酸配列。
配列番号１１０：カスパーゼ阻害剤のアミノ酸配列。
配列番号１１１：カスパーゼ阻害剤のアミノ酸配列。
配列番号１１２：カスパーゼ阻害剤のアミノ酸配列。
配列番号１１３：カスパーゼ阻害剤のアミノ酸配列。
配列番号１１４：カスパーゼ阻害剤のアミノ酸配列。
配列番号１１５：カスパーゼ−３特異的阻害剤のアミノ酸配列。
配列番号１１６：カスパーゼ−３特異的阻害剤のアミノ酸配列。
配列番号１１７：カスパーゼ−３特異的阻害剤のアミノ酸配列。
配列番号１１８：カスパーゼ−３非特異的阻害剤のアミノ酸配列。
配列番号１１９：Ｆａｓの９９〜１０２位のアミノ酸配列。
配列番号１２０：Ｆａｓリガンドの１６２〜１６５位のアミノ酸配列。
配列番号１２１：アポトーシス誘導ペプチドのアミノ酸配列。
【０２３９】
【配列表】

【図面の簡単な説明】
【図１】生理活性ペプチド設計の一例を示す。
【図２】生理活性ペプチド設計における本発明のシステム全体を示す。
【図３】１次スクリーニングの選択に用いられるプログラムのフローチャートを示す。
【図４】生理活性ペプチドの設計に用いられるプログラムのフローチャートを示す。なお、このフローチャートは、１次スクリーニングとしてアミノ酸相補プロファイル波形評価を行い、さらに２次スクリーニングを行なったものに対応する。
【図５】生理活性ペプチド設計における、３次スクリーニングについてのプログラムのフローチャートを示す。
【図６】アミノ酸相互作用領域評価（１次スクリーニング）についてのプログラムのフローチャートを示す。
【図７】リガンド（タンパク質）のアミノ酸配列からの断片化ペプチドの抽出を示す。
【図８】アミノ酸位置依存的結合有意性評価（１次スクリーニング）についてのプログラムのフローチャートを示す。
【図９】アミノ酸位置依存的結合有意性評価（１次スクリーニング）の概要を示す。
【図１０】生理活性ペプチドを設計するための装置の構成の例を示す。
【図１１】相補アミノ酸配列ＤＥＶＤと結晶構造の重ね合わせを示す。
【図１２】Ｆａｓ相補ペプチドテトラマーのアポトーシス誘導能を示す。
【図１３】Ｆａｓ相補ペプチドテトラマーで処理したマウス脳組織と腫瘍体積の統計データを示す。
【図１４】カスパーゼー３を標的タンパク質として、１次スクリーニング（アミノ酸位置依存的結合有意性評価）を行なった結果について示す。図１４Ａは、モチーフの各位置でのＰＳＳを示す。図１４Ｂは、モチーフの各位置でのＰＳＳマトリクスを示す。図１４Ｃは、モチーフの各位置でのＰＳＧマトリクスを示す。図１４Ｄは、解析用ライブラリを用いた相関解析を示す。図１４Ｅは、評価用ライブラリを用いた相関解析を示す。
【図１５】カスパーゼー７を標的タンパク質として、１次スクリーニング（アミノ酸位置依存的結合有意性評価）を行なった結果について示す。図１５Ａは、モチーフの各位置でのＰＳＳを示す。図１５Ｂは、モチーフの各位置でのＰＳＳマトリクスを示す。図１５Ｃは、モチーフの各位置でのＰＳＧマトリクスを示す。図１５Ｄは、解析用ライブラリを用いた相関解析を示す。図１５Ｅは、評価用ライブラリを用いた相関解析を示す。
【図１６】カスパーゼー８を標的タンパク質として、１次スクリーニング（アミノ酸位置依存的結合有意性評価）を行なった結果について示す。図１６Ａは、モチーフの各位置でのＰＳＳを示す。図１６Ｂは、モチーフの各位置でのＰＳＳマトリクスを示す。図１６Ｃは、モチーフの各位置でのＰＳＧマトリクスを示す。図１６Ｄは、解析用ライブラリを用いた相関解析を示す。図１６Ｅは、評価用ライブラリを用いた相関解析を示す。
【図１７】カスパーゼー９を標的タンパク質として、１次スクリーニング（アミノ酸位置依存的結合有意性評価）を行なった結果について示す。図１７Ａは、モチーフの各位置でのＰＳＳを示す。図１７Ｂは、モチーフの各位置でのＰＳＳマトリクスを示す。図１７Ｃは、モチーフの各位置でのＰＳＧマトリクスを示す。図１７Ｄは、解析用ライブラリを用いた相関解析を示す。図１７Ｅは、評価用ライブラリを用いた相関解析を示す。
【図１８】カスパーゼ−３特異的阻害ペプチドの設計におけるシステム構成を示す。
【図１９】Ｆａｓ（９９−１０２）に対するFas Ligand４残基ペプチドの結合自由エネルギーを示す。
【図２０】４つのＷＥＷＴペプチドをＭＡＰ−８に結合させることによって得られたペプチドを示す。[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a method for developing useful peptidic drugs. In particular, the present invention specifically describes a method for designing a physiologically active peptide capable of binding to a target site consisting of an arbitrary continuous amino acid sequence and a discontinuous amino acid sequence on a protein, an apparatus therefor, and the method described above. The present invention relates to a program for causing a computer to execute, and a computer-readable recording medium on which the program is recorded.
[0002]
[Prior art]
Various biological signals (neurotransmitters, hormones, cytokines) emitted from the extracellular information transmission system (neural system, endocrine system, immune system) stretched in the body are received by the intracellular information transmission system in the target cell.・ It is transmitted and an appropriate response is made. Here, many biological signals are transmitted by interaction between proteins. For example, various protein-protein interactions are involved in the binding of cell surface receptors and their specific ligands, as well as in signal transduction from intracellular cytoplasm to the nucleus. Therefore, the modulation and abnormality of the intracellular information transmission system are closely related to the development of many serious diseases. Therefore, there is an urgent need to create molecules that can control (promote or suppress) protein-protein interactions as targets. At present, research and development of bioactive peptides capable of interacting with target proteins are actively conducted as a means of elucidating protein-protein interactions such as ligand-receptors and as a means of treating diseases caused by abnormal signal cascades. ing.
[0003]
Biologically active peptides play an important role in the control of various physiological functions as information transmitters in vivo. However, since there are only a small amount of physiologically active peptides in nature and it is very difficult to purify, 100 species have not been found so far. On the other hand, it is speculated that there are a considerable number of orphan receptors that are considered to be physiologically active peptide receptors due to the accumulation of genome databases, and the search for their ligands is an important key for the development of new drugs. Examples of peptidic drugs currently in clinical application and those being developed include 1) hypothalamic hormone derivatives, 2) posterior pituitary hormone derivatives, 3) ANP derivatives, 4) calcium-regulating hormones, 5 ) Peptide antibiotics. Recently, a novel bioactive peptide has been discovered using cells in which orphan receptors are expressed. Using this technique, Takeda Pharmaceutical Industries discovered a peptide ligand “metastin” for orphan receptors that suppresses cancer metastasis [Reference 1]. It is expected that useful peptide drugs will be developed as bioactive peptides are further searched in the future.
[0004]
However, an effective methodology for predicting the amino acid sequence of a peptide that can bind to and interact with any amino acid sequence of a protein has not been established at present, and bioactive peptides are usually screened by biochemical techniques. Has been. For example, synthesizing a plurality of continuous peptides consisting of 10 to 20 amino acids from the N-terminal to the C-terminal from another protein known to bind to a certain protein, and selecting a bioactive peptide from them Alternatively, a method is used in which a bioactive peptide is selected from a randomized peptide library using a phage library. However, such a biochemical method has a problem of being expensive and time consuming. Therefore, development of a method for designing a physiologically active peptide in a theoretical, more economical and simple manner has been awaited instead of the conventional method.
[0005]
On the other hand, several theories for predicting a bioactive peptide sequence for a target amino acid sequence have been proposed so far. Watson-Crick published a DNA helical model and claimed that there were base pairs but no amino acid pairs, but a minority opinion that amino acid pairs may exist was originally present (e.g. Non-Patent Document 1).
[0006]
The sense-antisense theory proposed by Blalock et al. (See, for example, Non-Patent Document 2) is also premised on amino acid pairs. The contents of two peptides encoded by two complementary DNAs are the same as the bases. The hypothesis that they interact. Based on this theory, it has been experimentally confirmed that several antisense peptides interact with sense peptides.
[0007]
On the other hand, B1alock et al. Pointed out that the sense peptide and antisense peptide have high <hydrophobic complementarity>, and Fassina et al. Reported that the average hydrophobicity of five or more consecutive odd amino acids in a peptide In some experiments, it was shown that a complementary peptide having a complementary hydrophobicity (with the same absolute value and opposite positive and negative) binds to the original peptide (see, for example, Non-Patent Document 3). However, many theories have been reported in any theory, and it cannot be said that it is sufficient for application to prediction of general bioactive peptides. In any theory, multiple amino acid candidates can be considered for one amino acid in the target amino acid sequence. As a result, the number of peptide candidates predicted is enormous, and all of them are studied. To do still has a tremendous amount of time, money and effort.
[0008]
Further, even if it succeeds in obtaining a physiologically active peptide consisting of an amino acid sequence that interacts with a target amino acid sequence, there are further problems. Protein target sites for drug discovery include ligand binding sites (eg receptors), substrate binding sites (eg enzymes), protein interaction sites (eg transcription factors, multimers (eg , Dimer) in the case of forming protein), etc., but these target sites are separated from each other on the primary structure rather than composed of one continuous amino acid sequence. It is very often composed of the partial amino acid sequence. Therefore, even if a bioactive peptide comprising an amino acid sequence that interacts with a target amino acid sequence is obtained, the amino acid sequence is often not preferred for other amino acid sequences present at the target site.
[0009]
In addition, when the target site of the target protein is composed of a plurality of partial amino acid sequences that are located apart on the primary structure, conventionally, a specific peptide interacts with the target site of the target protein. Whether or not to do so has been performed, for example, by docking using molecular modeling and evaluating energetically. In order to evaluate more peptides by such a technique, practically, for example, in docking using a library containing thousands to hundreds of thousands of small molecules, the evaluation time per compound is 1 at most. It must be kept to a minute. However, even with 4 residues, there are as many as 20 variable sites on the side chain alone. For example, in Flexible Docking using AutoDock (see, for example, Non-Patent Document 4), the evaluation time on the Compac Alpha DS20E is: Approximately 10 minutes were required per peptide. For example, 20 for a 3-residue peptide^ThreeThat is, in the case of a 8000 times 6-residue peptide, it is necessary to carry out 64,000,000 times of docking, and comprehensive screening is extremely difficult in practice.
[0010]
For the above reasons, development of a rapid bioactive peptide design method having excellent binding ability to a target site of a protein has been desired.
[0011]
[Non-Patent Document 1]
"Journal of the Theoretical Biology", 1982, 94, p885-894.
[Non-Patent Document 2]
“Biochemical Biophysical Research Communication”, 1984, 121, p203-207.
[Non-Patent Document 3]
“Archives of Biochemistry and Biophysics”, 1992, 296, p137-143.
[Non-Patent Document 4]
“Journal of Computational Chemistry”, 1998, Vol. 19, p 1639-1662
[0012]
[Problems to be solved by the invention]
An object of the present invention is to provide a means for designing a bioactive peptide with higher certainty by a mathematical technique from the primary structure of a target amino acid sequence. A further object of the present invention is to provide means for designing a preferred bioactive peptide in consideration of not only the target amino acid sequence but also the target protein itself containing the target amino acid sequence.
[0013]
[Means for Solving the Problems]
As a result of diligent research to achieve the above object, the inventors of the present invention applied a profile waveform created by applying an arbitrary amino acid index to the target amino acid sequence, for example, an index based on hydrophobicity and electrical characteristics. We have successfully developed a new program that can rank and extract complementary amino acid sequences that satisfy the complementarity definition detailed below. In addition, the present inventors have designed an amino acid sequence that interacts with a target site when the target site of the target protein is composed of a plurality of partial amino acid sequences that are located apart on the primary structure. We have also succeeded in developing our own new programs that are particularly useful in Japan.
[0014]
The present inventors further provide a method, program, and computer-readable record that can predict whether or not the complementary amino acid sequence extracted above can act as a preferred bioactive peptide for the target protein itself including the target amino acid sequence. Media and apparatus were independently developed to complete the present invention.
[0015]
That is, the present invention has the following characteristics.
(1) A method for designing a bioactive peptide capable of interacting with a target amino acid sequence,
(A1) receiving sequence data input of a target amino acid sequence;
(B1) converting the target amino acid sequence into one or more moving average profile waveforms according to one or more predetermined amino acid indices;
(C1) generating a complementary amino acid sequence candidate of the target amino acid sequence and converting it into one or more complementary moving average profile waveforms using the same one or more amino acid indices as in step (b1);
(D1) calculating a complementarity parameter derived from the same amino acid index between at least one moving average profile waveform for the target amino acid sequence and at least one complementary moving average profile waveform of the complementary amino acid sequence candidate; ,
(E1) storing a complementary amino acid sequence candidate together with the complementation parameter in a storage means;
(F1) extracting a predetermined number of complementary amino acid sequences based on the information stored in step (e1);
(G1) displaying the extracted complementary amino acid sequence as a candidate for a physiologically active peptide.
[0016]
(2) The method according to (1) above, wherein the complementarity parameter is a correlation coefficient between a moving average profile waveform for the target amino acid sequence and a complementary moving average profile waveform of a complementary amino acid sequence candidate.
[0017]
(3) The amino acid index is based on an index based on hydrophobicity, an index based on electrical characteristics, an index representing the ease of taking α-helix and β-sheet, and an index representing the relative size of the side chain volume. The method of (1) or (2) above, which is one or more selected indicators.
[0018]
(4) Steps using one or more different amino acid indices for the predetermined number of complementary amino acid sequences extracted in steps (a1) to (f1) using one or more predetermined amino acid indices ( Any one of the above (1) to (3), wherein the complementary amino acid sequence candidates extracted as bioactive peptides are further narrowed down by repeating the steps b1) to (f1) one or more times. The method described.
[0019]
(5) A method of designing a physiologically active peptide capable of interacting with a target protein,
(A1 ') receiving sequence data input of a target amino acid sequence in the target protein;
(B1 ') converting the target amino acid sequence into one or more moving average profile waveforms according to one or more predetermined amino acid indices;
(C1 ′) generating a complementary amino acid sequence candidate of the target amino acid sequence and converting it to one or more complementary moving average profile waveforms using the same one or more amino acid indices as in step (b1 ′);
(D1 ′) calculating a complementation parameter derived from the same amino acid index between one or more moving average profile waveforms for the target amino acid sequence and one or more complementary moving average profile waveforms of the complementary amino acid sequence candidates, respectively When,
(E1 ') storing a complementary amino acid sequence candidate together with the complementation parameter in a storage means;
(F1 ') extracting a predetermined number of complementary amino acid sequence candidates based on the information stored in step (e1');
(G1 ') for the extracted complementary amino acid sequence candidates, calculating an intermolecular energy parameter with the target site of the target protein;
(H1 ') storing a complementary amino acid sequence candidate together with the intermolecular energy parameter in a storage means;
(I1 ') extracting a predetermined number of complementary amino acid sequences based on the information stored in step (h1');
(J1 ′) displaying the extracted complementary amino acid sequence as a candidate for a bioactive peptide.
[0020]
(6) The method according to (5) above, wherein the complementarity parameter is a correlation coefficient between a moving average profile waveform for the target amino acid sequence and a complementary moving average profile waveform of a complementary amino acid sequence candidate.
[0021]
(7) The amino acid index is based on an index based on hydrophobicity, an index based on electrical characteristics, an index representing the ease of taking α-helix and β-sheet, and an index representing the relative size of the side chain volume. The method of (5) or (6) above, which is one or more selected indicators.
[0022]
(8) One or more other amino acid indices are used for the predetermined number of complementary amino acid sequence candidates extracted in steps (a1 ′) to (f1 ′) using one or more predetermined amino acid indices. Step (b1 ′) to (f1 ′) is repeated one or more times to narrow down complementary amino acid sequence candidates, and then steps (g1 ′) to (i1 ′) are performed. The method according to any one of (5) to (7).
[0023]
(9) A program for designing a bioactive peptide capable of interacting with a target amino acid sequence,
(A1) receiving sequence data input of a target amino acid sequence;
(B1) converting the target amino acid sequence into one or more moving average profile waveforms according to one or more predetermined amino acid indices;
(C1) generating a complementary amino acid sequence candidate of the target amino acid sequence and converting it into one or more complementary moving average profile waveforms using the same one or more amino acid indices as in step (b1);
(D1) calculating a complementarity parameter derived from the same amino acid index between at least one moving average profile waveform for the target amino acid sequence and at least one complementary moving average profile waveform of the complementary amino acid sequence candidate; ,
(E1) storing a complementary amino acid sequence candidate together with the complementation parameter in a storage means;
(F1) extracting a predetermined number of complementary amino acid sequences based on the information stored in step (e1);
(G1) A program for causing a computer to execute the step of displaying the extracted complementary amino acid sequence as a candidate for a physiologically active peptide.
[0024]
(10) The program according to (9), wherein the complementarity parameter is a correlation coefficient between a moving average profile waveform for the target amino acid sequence and a complementary moving average profile waveform for a complementary amino acid sequence candidate.
[0025]
(11) The amino acid index is based on an index based on hydrophobicity, an index based on electrical characteristics, an index representing the ease of taking α-helix and β-sheet, and an index representing the relative size of the side chain volume. The program according to (9) or (10), which is one or more selected indices.
[0026]
(12) Steps using one or more different amino acid indexes for the predetermined number of complementary amino acid sequences extracted in steps (a1) to (f1) using one or more predetermined amino acid indexes ( Any one of (9) to (11) above, wherein the complementary amino acid sequence candidates extracted as the bioactive peptide are further narrowed down by repeating the steps b1) to (f1) one or more times. The listed program.
[0027]
(13) A program for designing a bioactive peptide capable of interacting with a target protein,
(A1 ') receiving sequence data input of a target amino acid sequence in the target protein;
(B1 ') converting the target amino acid sequence into one or more moving average profile waveforms according to one or more predetermined amino acid indices;
(C1 ′) generating a complementary amino acid sequence candidate of the target amino acid sequence and converting it to one or more complementary moving average profile waveforms using the same one or more amino acid indices as in step (b1 ′);
(D1 ′) calculating a complementation parameter derived from the same amino acid index between one or more moving average profile waveforms for the target amino acid sequence and one or more complementary moving average profile waveforms of the complementary amino acid sequence candidates, respectively When,
(E1 ') storing a complementary amino acid sequence candidate together with the complementation parameter in a storage means;
(F1 ') extracting a predetermined number of complementary amino acid sequence candidates based on the information stored in step (e1');
(G1 ') for the extracted complementary amino acid sequence candidates, calculating an intermolecular energy parameter with the target site of the target protein;
(H1 ') storing a complementary amino acid sequence candidate together with the intermolecular energy parameter in a storage means;
(I1 ') extracting a predetermined number of complementary amino acid sequences based on the information stored in step (h1');
(J1 ') A program for causing a computer to execute the step of displaying the extracted complementary amino acid sequence as a candidate for a physiologically active peptide.
[0028]
(14) The program according to (13), wherein the complementarity parameter is a correlation coefficient between a moving average profile waveform for the target amino acid sequence and a complementary moving average profile waveform of a complementary amino acid sequence candidate.
[0029]
(15) The amino acid index is based on an index based on hydrophobicity, an index based on electrical characteristics, an index representing the ease of taking α-helix and β-sheet, and an index representing the relative size of the side chain volume. The program according to (13) or (14), which is one or more selected indices.
[0030]
(16) One or more different amino acid indices are used for the predetermined number of complementary amino acid sequence candidates extracted in steps (a1 ′) to (f1 ′) using one or more predetermined amino acid indices. Step (b1 ′) to (f1 ′) is repeated one or more times to narrow down complementary amino acid sequence candidates, and then steps (g1 ′) to (i1 ′) are performed. (13) The program according to any one of (15).
[0031]
(17) A computer-readable recording medium on which the program according to any one of (9) to (16) is recorded.
[0032]
(18) A device for designing a physiologically active peptide capable of interacting with a target amino acid sequence, comprising (a) a data input unit, (b) a data editing unit, and (c) a complementary amino acid sequence candidate generation unit. , (D) a complementation degree calculation unit, (e) a complementary amino acid sequence candidate storage unit, (f) a complementary amino acid sequence search unit, and (g) a complementary amino acid sequence display unit,
The data input unit includes (a1) means for receiving sequence data input of a target amino acid sequence,
The data editing unit includes (b1) means for converting the target amino acid sequence into one or more moving average profile waveforms according to one or more predetermined amino acid indices.
The complementary amino acid sequence candidate generation unit (c1) generates a complementary amino acid sequence candidate of the target amino acid sequence, and generates one or more complementary moving average profile waveforms using the same one or more amino acid indices as in step (b1). Including means for converting,
The complementation degree calculation unit (d1) calculates a complementation parameter derived from the same amino acid index as one or more moving average profile waveforms for the target amino acid sequence and one or more complementary moving average profile waveforms of complementary amino acid sequence candidates. Including means for calculating each between
The complementary amino acid sequence candidate storage unit includes (e1) means for storing a complementary amino acid sequence candidate together with the complementation degree parameter,
The complementary amino acid sequence searching unit includes (f1) means for extracting a predetermined number of complementary amino acid sequences based on the information stored in the means (e1),
The complementary amino acid sequence display unit includes (g1) means for displaying the complementary amino acid sequence extracted by the means (f1) as a candidate for a physiologically active peptide.
[0033]
(19) The apparatus according to (18), wherein the complementarity parameter is a correlation coefficient between a moving average profile waveform for the target amino acid sequence and a complementary moving average profile waveform of a complementary amino acid sequence candidate.
[0034]
(20) The amino acid index includes an index based on hydrophobicity, an index based on electrical characteristics, an index indicating ease of taking α-helix and β-sheet, and an index indicating the relative size of side chain volume. The apparatus according to (18) or (19), which is one or more selected indicators.
[0035]
(21) A device for designing a physiologically active peptide capable of interacting with a target protein, comprising (a) a data input unit, (b) a data editing unit, (c) a complementary amino acid sequence candidate generation unit, (D) a complementation degree calculation unit, (e) a complementary amino acid sequence candidate storage unit, (f) a complementary amino acid sequence search unit, and (g) a complementary amino acid sequence display unit,
The data input unit includes (a1 ′) means for receiving sequence data input of a target amino acid sequence in the target protein,
The data editing unit includes (b1 ') means for converting the target amino acid sequence into one or more moving average profile waveforms according to one or more predetermined amino acid indices.
The complementary amino acid sequence candidate generation unit (c1 ′) generates a complementary amino acid sequence candidate of the target amino acid sequence, and uses one or more complementary moving average profiles using the same one or more amino acid indices as the means (b1 ′). Including means for converting to a waveform;
The complementation degree calculation unit includes (k1 ′) a complementation parameter derived from the same amino acid index, one or more moving average profile waveforms for the target amino acid sequence, and one or more complementary moving average profile waveforms of complementary amino acid sequence candidates. Means for calculating the intermolecular energy parameter with the target site of the target protein,
The complementary amino acid sequence candidate storage unit includes means for storing (11 ') complementary amino acid sequence candidates together with the complementarity parameter and the intermolecular energy parameter;
The complementary amino acid sequence search unit includes means for extracting a predetermined number of complementary amino acid sequences based on the information stored by the (m1 ′) means (k1 ′),
The complementary amino acid sequence display unit includes (n1 ′) means for displaying the complementary amino acid sequence extracted by the complementary amino acid sequence search unit as a candidate for a physiologically active peptide.
[0036]
(22) The apparatus according to (21), wherein the complementarity parameter is a correlation coefficient between a moving average profile waveform for the target amino acid sequence and a complementary moving average profile waveform of a complementary amino acid sequence candidate.
[0037]
(23) The amino acid index is based on an index based on hydrophobicity, an index based on electrical characteristics, an index representing the ease of taking α-helix and β-sheet, and an index representing the relative size of the side chain volume. The apparatus according to (21) or (22), which is one or more selected indices.
[0038]
(24) Furthermore, between step (i1 ') and step (j1'),
(I) generating an amino acid sequence in which an amino acid mutation is introduced into the amino acid sequence extracted in step (i1 ');
(II) calculating an intermolecular energy parameter between the amino acid sequence generated in step (I) and the target site of the target protein;
(III) The intermolecular energy parameter calculated in step (II) is compared with the intermolecular energy parameter between the amino acid sequence extracted in step (i1 ′) and the target site of the target protein as a control. And extracting an amino acid sequence having an intermolecular energy parameter that is more stable than the energy parameter, the program according to any one of (13) to (16) above.
[0039]
(25) A program for designing a physiologically active peptide capable of interacting with a target protein,
(A2) identifying an interaction region in the protein that interacts with the target site of the target protein;
(B2) A program for causing a computer to execute the step of extracting an amino acid sequence having an arbitrary length from the interaction region.
[0040]
(26) A program for designing a physiologically active peptide capable of interacting with a target protein,
(A2 ') identifying an interaction region in the protein that interacts with the target site of the target protein;
(B2 ') extracting an amino acid sequence of an arbitrary length from the interaction region;
(C2 ') calculating an intermolecular energy parameter with the target site of the target protein for the extracted amino acid sequence;
(D2 ') storing the amino acid sequence together with the intermolecular energy parameter in a storage means;
(E2 ') extracting a predetermined number of amino acid sequences based on the information stored in step (d2');
(F2 ') A program for causing a computer to execute the step of displaying the extracted amino acid sequence as a candidate for a physiologically active peptide.
[0041]
(27) Further, between step (e2 ') and step (f2'),
(I) generating an amino acid sequence in which an amino acid mutation is introduced into the amino acid sequence extracted in step (e2 ');
(II) calculating an intermolecular energy parameter between the amino acid sequence generated in step (I) and the target site of the target protein;
(III) The intermolecular energy parameter calculated in step (II) is compared with the intermolecular energy parameter between the amino acid sequence extracted in step (e2 ′) and the target site of the target protein as a control. And extracting an amino acid sequence having an intermolecular energy parameter that is more stable than the energy parameter.
[0042]
(28) A program for designing a bioactive peptide capable of interacting with a target protein,
(A3) comprehensively generating an amino acid sequence of a certain length, randomly selecting an amino acid sequence from the amino acid sequence and extracting it as an analysis library;
(B3) calculating an intermolecular energy parameter for each amino acid sequence extracted as an analysis library;
(C3) generating a score matrix based on the appearance frequency of amino acids using the intermolecular energy parameter calculated in step (b3);
(D3) calculating a score based on the amino acid appearance frequency using a score matrix based on the amino acid appearance frequency;
(E3) performing a correlation analysis between the intermolecular energy parameter calculated in step (b3) and the score to obtain a regression equation;
(F3) converting the score matrix based on the amino acid appearance frequency into a matrix based on the amino acid position-dependent intermolecular energy parameter using the regression equation;
(G3) calculating an amino acid position-dependent intermolecular energy parameter value from a matrix based on the amino acid position-dependent intermolecular energy parameter;
(H3) A program for causing a computer to execute a step of extracting an amino acid sequence that is equal to or less than a predetermined amino acid position-dependent intermolecular energy parameter value.
[0043]
(29) A program for designing a bioactive peptide capable of interacting with a target protein,
(A3 ′) comprehensively generating an amino acid sequence of a certain length, randomly selecting an amino acid sequence from the amino acid sequence, and extracting it as an analysis library;
(B3 ') calculating an intermolecular energy parameter for each amino acid sequence extracted as an analysis library;
(C3 ′) using the intermolecular energy parameter calculated in step (b3 ′) to generate a score matrix based on the appearance frequency of amino acids;
(D3 ') calculating a score based on the amino acid appearance frequency using a score matrix based on the amino acid appearance frequency;
(E3 ') performing a correlation analysis between the intermolecular energy parameter calculated in step (b3') and the score to obtain a regression equation;
(F3 ') using the regression equation, converting a score matrix based on the amino acid appearance frequency into a matrix based on an amino acid position-dependent intermolecular energy parameter;
(G3 ') calculating an amino acid position-dependent intermolecular energy parameter value from a matrix based on the amino acid position-dependent intermolecular energy parameter;
(H3 ') extracting an amino acid sequence equal to or lower than a predetermined amino acid position-dependent intermolecular energy parameter value;
(I3 ') calculating an intermolecular energy parameter with the target site of the target protein for the extracted amino acid sequence;
(J3 ') storing the amino acid sequence together with the intermolecular energy parameter in a storage means;
(K3 ') extracting a predetermined number of amino acid sequences based on the information stored in step (j3');
(L3 ') A program for causing a computer to execute the step of displaying the amino acid sequence extracted in step (k3') as a candidate for a bioactive peptide.
[0044]
(30) Further, between step (k3 ') and step (l3'),
(I) generating an amino acid sequence in which an amino acid mutation is introduced into the amino acid sequence extracted in step (k3 ');
(II) calculating an intermolecular energy parameter between the amino acid sequence generated in step (I) and the target site of the target protein;
(III) The intermolecular energy parameter calculated in step (II) is compared with the intermolecular energy parameter between the amino acid sequence extracted in step (k3 ′) and the target site of the target protein as a control. And extracting an amino acid sequence having an intermolecular energy parameter that is more stable than the energy parameter.
[0045]
(31) A device for designing a physiologically active peptide capable of interacting with a target protein, wherein (b) an interaction region specifying part, (b) an amino acid sequence first search part, and (c) 2) An intermolecular energy calculation unit; (d) an amino acid sequence storage unit; (e) an amino acid sequence second search unit; (f) an amino acid sequence display unit;
The interaction region specifying unit includes (a2 ′) means for specifying an interaction region in a protein molecule that interacts with a target site of a target protein,
The amino acid sequence first search unit includes (b2 ') means for extracting an amino acid sequence of an arbitrary length from the interaction region,
The intermolecular energy calculation unit includes (c2 ′) a means for calculating an intermolecular energy parameter between the extracted amino acid sequence and the target site of the target protein,
The amino acid sequence storage unit includes (d2 ′) means for storing the amino acid sequence together with the intermolecular energy parameter in a storage means,
The amino acid sequence second search unit includes means for extracting a predetermined number of amino acid sequences based on the information stored in (e2 ′) step (d2 ′),
The amino acid sequence display unit includes (f2 ′) means for displaying the extracted amino acid sequence as a candidate for a bioactive peptide.
[0046]
(32) An apparatus for designing a physiologically active peptide capable of interacting with a target protein, comprising (a) an amino acid sequence first search unit, (b) a first intermolecular energy first calculation unit, 3) a score matrix generation unit, (d) 3) a score calculation unit, (e) 3) a regression formula formation unit, (f) a matrix conversion unit, (g) an amino acid position-dependent energy calculation unit, (3) Amino acid sequence second search unit, (3) Intermolecular energy second calculation unit, (3) Amino acid sequence storage unit, (3) Amino acid sequence third search unit, (3) An amino acid sequence display unit,
The amino acid sequence first search unit includes (a3 ′) a means for comprehensively generating an amino acid sequence of a certain length, selecting an amino acid sequence randomly from among them, and extracting it as an analysis library,
The intermolecular energy first calculation unit includes (b3 ') means for calculating an intermolecular energy parameter for each amino acid sequence extracted as an analysis library,
The score matrix generator includes (c3 ′) means for generating a score matrix based on the appearance frequency of amino acids using the intermolecular energy parameter calculated in step (b3 ′),
The score calculator includes (d3 ′) means for calculating a score based on the appearance frequency of amino acids using a score matrix based on the appearance frequency of amino acids,
The regression equation forming unit includes means for performing a correlation analysis between the intermolecular energy parameter calculated in (e3 ′) step (b3 ′) and the score to obtain a regression equation,
The matrix conversion unit includes (f3 ′) means for converting a score matrix based on an amino acid appearance frequency into a matrix based on an amino acid position-dependent intermolecular energy parameter using the regression equation,
The amino acid position-dependent energy calculation unit includes (g3 ′) means for calculating an amino acid position-dependent intermolecular energy parameter value from a matrix based on the amino acid position-dependent intermolecular energy parameter,
The amino acid sequence second search unit includes (h3 ′) means for extracting an amino acid sequence that is equal to or lower than a predetermined amino acid position-dependent intermolecular energy parameter value,
The intermolecular energy second calculation unit includes (i3 ′) means for calculating an intermolecular energy parameter with the target site of the target protein for the extracted amino acid sequence,
The amino acid sequence storage unit includes (j3 ′) means for storing the amino acid sequence together with the intermolecular energy parameter in a storage means,
The amino acid sequence search unit includes means for extracting a predetermined number of amino acid sequences based on the information stored in (k3 ′) step (j3 ′),
The amino acid sequence display unit includes means for displaying the amino acid sequence extracted in step (l3 ′) in step (k3 ′) as a candidate for a bioactive peptide.
[0047]
DETAILED DESCRIPTION OF THE INVENTION
First, terms and methods used in this specification will be described in the order of primary screening, secondary screening, and tertiary screening. In addition, although several evaluation methods can be used as a primary screening, the more suitable evaluation method according to the target protein can be selected suitably. This selection is performed from the viewpoint of the type of the target protein, the characteristics of the target site of the target protein, whether or not a known ligand (protein) is present, and whether or not its interaction region is specified.
[0048]
I. Primary screening
As the primary screening, a method based on amino acid complementary profile waveform evaluation, amino acid interaction region evaluation, and amino acid position-dependent binding significance evaluation can be used. Amino acid complementation profile waveform assessment is primarily useful for designing bioactive peptides that interact with target sites consisting of contiguous amino acid sequences. On the other hand, amino acid interaction region evaluation and amino acid position-dependent binding significance evaluation are not only useful for designing a bioactive peptide that interacts with a target site consisting of a continuous amino acid sequence, but the target site of the target protein is When it is composed of a plurality of partial amino acid sequences that are located apart on the primary structure, it is also useful for designing a bioactive peptide that interacts with this target site. Specifically, the design method in the primary screening is selected according to the target protein. As a design method, the most appropriate one can be selected according to three judgment criteria including presence / absence of ligand information, continuity / discontinuity of the target site, and whether the enzyme has a pocket on the surface. An overview of each is shown in Table 1 below, and each is described in turn.
[0049]
[Table 1]

[0050]
A. Amino acid complementary profile waveform evaluation (generation of complementary peptide library)
The amino acid complementary profile waveform evaluation is a method for evaluating a peptide having an amino acid sequence that interacts with a target amino acid sequence based on a physicochemical amino acid index of the target amino acid sequence. This evaluation method is particularly useful when the target site of the target protein consists of one continuous amino acid sequence. The terms used in the evaluation of amino acid complementary profile waveforms and the outline of the evaluation are described below.
[0051]
The “target amino acid sequence” refers to an amino acid sequence that is a target in designing a complementary amino acid sequence. That is, an object of the present invention is to design a physiologically active peptide consisting of an amino acid sequence (complementary amino acid sequence) that interacts with this “target amino acid sequence”. Preferably, the target amino acid sequence is an amino acid sequence found in a target protein (eg, receptor, enzyme, etc.) that is a target for drug discovery.
[0052]
“Complementary amino acid sequence” refers to an amino acid sequence that satisfies the definition of complementarity in the present invention.
[0053]
“Complementary” refers to a complementary moving average profile waveform that has a negative correlation with a moving average profile waveform that has been passed through a low frequency by moving average with respect to a profile waveform in which an arbitrary amino acid index is applied to the target amino acid sequence. The relationship between an amino acid sequence having an approximate profile waveform and a target amino acid sequence. Therefore, when a moving average profile waveform of a certain amino acid sequence has a smaller correlation coefficient R (described below) with a complementary moving average profile waveform of a target amino acid sequence than a moving average profile waveform of another amino acid sequence, The sequence is “more complementary” to the target amino acid sequence.
[0054]
“Profile waveform” refers to a waveform generated by applying an amino acid index to an amino acid sequence.
[0055]
“Moving average profile waveform” refers to a waveform obtained by moving and averaging a profile waveform with a predetermined window width. A profile waveform obtained by applying an arbitrary amino acid index to a target amino acid sequence is represented by T_iThen, the moving average profile waveform x at an arbitrary (odd) window width w_jIs represented by [Equation 1].
[0056]
[Expression 1]

[0057]
In this specification, the moving average profile waveform of a complementary amino acid sequence is particularly referred to as a “complementary moving average profile waveform”. A profile waveform obtained by applying an arbitrary amino acid index to a complementary amino acid sequence candidate is represented by C_iThen, the complementary moving average profile waveform y with an arbitrary (odd) window width w_jIs represented by [Equation 2].
[0058]
[Expression 2]

[0059]
“Window width” refers to the width of a range in which profile waveforms are added when a moving average profile waveform is created. The window width can be set to any odd number, but is usually set to 1 to 13, preferably 3 to 13, and more preferably 5 to 11.
[0060]
The “amino acid index” refers to an index that quantifies the physicochemical characteristics of amino acids. More than 400 types of amino acid indices have been accumulated so far, and for example, these indices can be searched from a database AAindex provided by the Institute for Chemical Research, Kyoto University. The indicators of these amino acids are hydrophobicity, ease of β structure formation, ease of α helix formation and turn formation, and side chain physicochemical properties (eg, relative size of side chain volume). (See, for example, Tomii and Kanehisa, Protein Eng., 9, 27-36 (1996)). In the present invention, the amino acid index for generating the profile waveform can be selected from about 400 types. The database may be stored in a storage device of the device (described later) attached to the program, or stored in an external storage device that can be referred to by communication such as the Internet. It may be.
[0061]
Examples of preferable amino acid index used in the present invention include an index based on hydrophobicity, an index based on electrical characteristics, an index indicating ease of taking α-helix and β-sheet, and a relative size of side chain volume. The amino acid index is more preferably selected from an index based on hydrophobicity and an index based on electrical characteristics. Examples of the index based on the degree of hydrophobicity include Kyte-Doolittle hydropathy index, Jones et al. Hydrophobicity, and Consensus Normalized Hydrophobicity scale [Reference 3]. In particular, Consensus Normalized Hydrophobicity scale is preferable. The index based on electrical characteristics refers to an index representing the degree of polarization of a molecule and electrostatic interaction. For example, Fauchere et al. Localized electrical effect, Grantham et al. Polarity, and Electron-ion interaction potential ( EIIP) [Reference 4] and the like, and in particular, Electron-ion interaction potential (EIIP) is preferable [Reference 4].
[0062]
The “complementarity parameter” refers to a value indicating complementarity between the moving average profile waveform of the target amino acid sequence and the complementary moving average profile waveform of the complementary amino acid sequence. An example of the complementarity parameter is a correlation coefficient R shown in [Equation 3] below.
[0063]
[Equation 3]

[0064]
Here, the average value of the moving average profile waveform is represented by [Equation 4].
[0065]
[Expression 4]

[0066]
The average value of the complementary moving average profile waveform is expressed by [Formula 5].
[0067]
[Equation 5]

[0068]
A person skilled in the art can use, as a complementarity parameter, a value calculated from an equation derived from [Equation 3] in addition to the correlation coefficient of [Equation 3].
[0069]
“Average parameter (P_ave“)” Means (i) the average value of the profile waveform of the target amino acid sequence shown in [Equation 6] below
[0070]
[Formula 6]

[0071]
Or (ii) The average value of the used amino acid indices shown in [Formula 7] below
[0072]
[Expression 7]

[0073]
Say.
[0074]
“Filter value” refers to a value set to narrow down the number of complementary amino acid sequence candidates. In the present invention, in particular, a filter value related to a complementation parameter and a filter value related to an average value parameter are used.
[0075]
As a filter value related to the complementarity parameter, for example, a correlation coefficient filter value R_t(This value R_tR <R_tOnly candidate complementary amino acid sequences having a correlation coefficient R corresponding to the above are selected). Preferably, a negative correlation is required between the moving average profile waveform of the target amino acid sequence and the complementary moving average profile waveform of the complementary amino acid sequence. Therefore, the correlation coefficient filter value R_tCan be set to any negative number greater than or equal to -1, but preferably R_t≦ −0.9 is set. The filter value related to the complementarity parameter may be set in advance before the calculation of the complementarity parameter, or may be set as appropriate after the calculation.
[0076]
In addition, as a filter value related to the average value parameter, P_aveFilter values a and b are set. Based on these values a and b, a <P_ave<P corresponding to b_aveOnly candidate complementary amino acid sequences having are selected. P_aveSince the physiologically active peptide to be obtained becomes insoluble and experiments are difficult, the values of a and b are preferably set to values with high hydrophilicity. P_aveThe filter value is P_aveMay be preset before the calculation of P, or P_aveYou may set suitably after calculation.
[0077]
As a filter value, correlation coefficient filter value R_tMay be used, but preferably P_aveThe filter values a and b are further used. In this case, the conditional expression is as follows.
[0078]
[Equation 8]

[0079]
The amino acid sequence (complementary peptide library) selected by this evaluation method is preferably subjected to the secondary screening described later.
[0080]
B. Amino acid interaction region evaluation (generation of fragmented peptide library)
Amino acid interaction region evaluation refers to a method of designing a peptide that can bind to a target protein using information on the primary structure (amino acid sequence) of the protein that interacts with the target protein. This evaluation method is particularly useful when the primary structure (amino acid sequence) of a protein that interacts with or is predicted to interact with a target protein is known. The terms used in the evaluation of amino acid interaction regions and the outline of the evaluation are described below.
[0081]
Regarding the “interaction region” in the protein that interacts with the target site of the target protein, if there is a protein in which a region that can interact with a certain target protein already exists, that region is selected. When a plurality of interaction regions are present in one protein, the plurality of regions are selected as interaction regions. When a plurality of proteins that can interact with a certain target protein are known, a plurality of interaction regions can be obtained from these proteins. On the other hand, if the protein itself that interacts with the target protein is known, but the interacting region is not specified, this region is obtained by a method well known in the art (for example, the RBD method [reference document 5]). Can be selected. This evaluation method can also be applied when the protein itself that interacts with a certain target protein is unknown, but preferably this evaluation method is applied when the protein itself that interacts with a certain target protein is known. The
[0082]
As long as the length of the amino acid sequence extracted in this evaluation method (that is, the number of amino acid residues) is within the total length of the above interaction region, an amino acid sequence of any length can be extracted. However, preferably, an amino acid sequence consisting of 3 to 7 amino acid residues, more preferably an amino acid sequence consisting of 4 amino acid residues can be extracted. In addition, extraction of an amino acid sequence is performed exhaustively. For example, when extracting an amino acid sequence consisting of X amino acid residues, N−X + 1 amino acid sequences are extracted from the N-terminal to the C-terminal of the above interaction region and stored as a fragmented peptide library. The Moreover, the length of the extracted amino acid sequence may be unified, but may be varied. For example, an amino acid sequence consisting of X amino acid residues is comprehensively extracted from the interaction region by N−X + 1, and an amino acid sequence consisting of X ′ amino acid residues (number different from X) It is also possible to extract NX ′ + 1 comprehensively from the action region and store amino acid sequences having different lengths in the fragmented peptide library. The amino acid sequence (fragmented peptide library) extracted by this evaluation method is selected by secondary screening described later.
[0083]
C. Amino acid position-dependent binding significance evaluation (generation of position score peptide library)
Amino acid position-dependent binding significance evaluation is performed by obtaining only a few percent of amino acid sequences at random from a peptide library containing thousands to hundreds of thousands of amino acid sequences and evaluating the interaction energy with the target protein. A score matrix that enables high-speed ΔG calculation is constructed, and peptide design is performed. In this evaluation method, when there are multiple proteins with similar substrate specificities, one of these proteins can be selected as the target protein, and a peptide with high specificity can be designed for this target protein. it can.
[0084]
Although this evaluation method can be applied to any target protein, it is preferably used for an enzyme or the like (for example, peptidase) that has a pocket on the molecular surface as a target protein and is considered to have little structural change due to binding. It applies to. This is because the score matrix is created based on the position-dependent amino acid appearance frequency. That is, this evaluation method is preferable for those requiring a limited backbone and mode of conjugation upon conjugation with a peptide.
[0085]
The “analysis library” is composed of a set of amino acid sequences randomly extracted from a comprehensively generated amino acid sequence of a certain length (that is, an amino acid sequence consisting of a specific number of amino acid residues). The number of amino acid sequences included in the analysis library may be several percent with respect to the total number of amino acid sequences of a certain length that have been exhaustively generated. For example, when designing an amino acid sequence consisting of X amino acid residues,^XA comprehensive set of combinations of^XSeveral percent of amino acid sequences are randomly selected and used as a library for analysis. The number of amino acid sequences selected as the analysis library is not limited to a specific number and can be set as appropriate. The length of the amino acid sequence that can be designed in this evaluation is not particularly limited, but an amino acid sequence consisting of 2 to 10 amino acid residues is preferable, and an amino acid sequence consisting of 3 to 5 amino acid residues is more preferable. Further, in order to evaluate the validity of the “analysis library”, an “evaluation library” is also generated as necessary. The “evaluation library” is selected from the comprehensively generated amino acid sequences excluding the amino acid sequences used in the “analysis library”. The number of amino acid sequences prepared as the “evaluation library” is not particularly limited, but is set to a number smaller than the number of amino acid sequences prepared as the “analysis library”.
[0086]
The “intermolecular energy parameter” has the same meaning as that described in “II. Secondary screening” described later, and the calculation is performed in the same manner as described later.
[0087]
The “score matrix based on the appearance frequency of amino acids” means any matrix generated based on the appearance frequency of amino acids. An example of a “score matrix based on the appearance frequency of amino acids” is a PSS matrix (Positional Scanning Score-MATRIX) generated according to the following [Equation 9]. A_ijIndicates the number of occurrences of amino acid i at position j in all peptide groups included in the library for analysis, b_ijIs the number of occurrences of amino acid i at position j in the peptide group below the threshold ΔG included in the library for analysis. In the above, the threshold value ΔG may be set in advance, but may be set by a method described later.
[0088]
[Equation 9]

[0089]
The “score based on amino acid appearance frequency” means that calculated according to the above “score matrix based on amino acid appearance frequency”, and an example thereof is PSS (Positional Scanning Score) calculated by the following [Equation 10]. [Reference 6]. In the following [Formula 10], n represents the number of amino acids to be determined, and C_ijIs a 20 × n matrix and is composed of 0 or 1 values. In addition, an element that matches amino acid i at position j in an arbitrary amino acid sequence is 1 and an element that does not match is 0.
[0090]
[Expression 10]

[0091]
The “matrix based on amino acid position-dependent intermolecular energy parameters” refers to the relationship between the “intermolecular energy parameters” calculated for each amino acid sequence extracted as an analysis library and the “score based on amino acid appearance frequency”. “Score matrix based on amino acid appearance frequency” is converted using the regression equation obtained by correlation analysis. An example is a PSG matrix (Positional Scanning ΔG-MATRIX). In addition, when converting into the “score matrix based on the appearance frequency of amino acids”, preferably, the constant term is uniformly distributed to each position.
[0092]
The “amino acid position-dependent intermolecular energy parameter value” is calculated by the above “score matrix based on the appearance frequency of amino acids”, and an example thereof is PSG (Positional) calculated by the following formula [Formula 11]. Scanning [Delta] G), but parameters equivalent to free energy can also be used. In the following [Formula 11], PSG_ijIndicates an element of ij in the PSG matrix.
[0093]
## EQU11 ##

[0094]
The amino acid sequence (position score peptide library) selected by this evaluation method is preferably subjected to the secondary screening described later.
[0095]
II. Secondary screening
“Intermolecular energy parameter” refers to a parameter based on intermolecular energy between a complementary amino acid sequence and a target site of a target protein. The intermolecular energy parameter means a parameter related to intermolecular energy calculated by any method known in the art. Examples of the intermolecular energy parameter calculated by an arbitrary method known in the art include those calculated by MM3 [Reference 7], Amber [Reference 9], and Charmm [Reference 10] force field. Can be mentioned. Preferably, as the intermolecular energy parameter, the intermolecular energy (E_mol) And inhibition constant (K_i) Is used.
[0096]
“Intermolecular energy (E_mol) "Is calculated by the following [Equation 12] (using the Amber force field [references 9 and 11]).
[0097]
[Expression 12]

[0098]
Here, preferably, each coefficient of [Equation 12] is given as an empirical parameter by the Amber force field.
[0099]
“Inhibition constant (K_i) "Is calculated by the following [Equation 13].
[0100]
[Formula 13]

[0101]
ΔG is based on complex formation between the target site of the target protein and a peptide candidate consisting of a complementary amino acid sequence, and is calculated using an arbitrary energy function known in the art, preferably , Calculated using the AutoDock energy function [Ref 2].
[0102]
A preferred bioactive peptide for the target site of the target protein is required to satisfy a threshold condition set for any intermolecular energy parameter. Preferably, as the intermolecular energy parameter, E_molOr K_iWhen is used, it is necessary to satisfy either [Expression 14] or [Expression 15] below.
[0103]
[Expression 14]

[0104]
Or
[0105]
[Expression 15]

[0106]
The primary screening and the secondary screening have been described so far. Hereinafter, a peptide having an amino acid sequence obtained by the secondary screening may be referred to as a “lead peptide” for convenience.
[0107]
III. Third screening
As the third screening, mutation of the lead peptide can be performed by “amino acid mutation ΔE (eg, ΔG) evaluation”. In the amino acid mutation ΔE evaluation, each amino acid constituting the lead peptide is replaced with another amino acid to create a mutant peptide, and ΔE between the mutant peptide and the target protein_mutant(For example, ΔG_mutant) Is calculated and the mutant peptide is evaluated. For example, when the peptide obtained by the secondary screening is composed of 4 amino acid residues and only one amino acid residue is replaced with another natural amino acid, 4 × 19 mutant peptides are used. Comprehensive creation of ΔE between these mutant peptides and target proteins_mutant(For example, ΔG_mutant) Respectively. ΔE_mutant(For example, ΔG_mutant) Is calculated using any energy function known in the art, but is preferably calculated using the AutoDock energy function, similar to the calculation in the secondary screening. In one example, ΔG obtained for each mutant peptide when binding free energy is used as ΔE._mutantAnd lead peptide ΔG_leadΔΔG, which is a difference from the above, is calculated by the following [Equation 16].
[0108]
[Expression 16]

[0109]
From the above formula, the mutant peptide in which ΔΔG is negative forms a more stable complex with the target protein than the lead peptide, and the mutant peptide in which ΔΔG is positive is compared with the target peptide. And forms a more unstable complex. Therefore, in the tertiary screening, it is preferable to introduce amino acid mutations that make ΔΔG negative. In the above example, only one amino acid residue is substituted among the amino acid residues constituting the lead peptide, but by combining amino acid mutations that make ΔΔG negative, two or three amino acid residues are combined. Substitution may be performed. By performing the tertiary screening as described above, a more optimal physiologically active peptide can be designed.
[0110]
In amino acid substitution, each amino acid in the lead peptide can be substituted with 19 other natural amino acids. In addition to natural amino acids, any other non-natural amino acids such as α-amino acids, β-amino acids, and γ-amino acids can be substituted. However, since substitution with other than α-amino acid is likely to cause a change in the arrangement of the main chain, substitution with any unnatural α-amino acid is preferred. Moreover, it can be substituted with any amino acid of L-form and D-form.
[0111]
This amino acid substitution can also be understood from the viewpoint of “optimization of amino acid side chains”. For example, it is assumed that the above-mentioned “amino acid mutation ΔE (eg, ΔG) evaluation” is performed through primary screening and secondary screening, and the most preferable peptide Ala-Cys-Phe-Val is obtained for a target site of a certain target protein. To do. In such a case, in order to obtain a more preferable peptide based on this peptide Ala-Cys-Phe-Val, it is possible to re-verify the side chain of each amino acid constituting this peptide. Specifically, the Met side chain (-CH₂CH₂SCH_Three), The above “amino acid mutation ΔE (for example, ΔG) evaluation” can be performed on the side chain into which a halogen atom is introduced instead of a hydrogen atom or a side chain into which a further group is introduced, to obtain a more appropriate mutant peptide is there. In the above description, the side chain mutation of Met has been described as an example. Of course, the side chain of a natural amino acid other than Met and the side chain of a non-natural amino acid (preferably, a non-natural α-amino acid) are similarly revalidated. It is possible. In general, in the case of a somewhat large protein, amino acid substitutions are likely to be limited to natural amino acids. This is because when actually synthesizing such a protein, the coding region of DNA is mutated and a mutated (substituted) protein is synthesized by translation using a cell system or a cell-free system. However, in the case of a low molecular weight peptide, the type of amino acid constituting the peptide is not limited to a natural amino acid. This is because low-molecular peptides can be easily synthesized by solid-phase synthesis, and the polymerization reaction is not limited to natural amino acids and non-natural amino acids as long as amino acids as raw materials can be supplied. Therefore, the tertiary screening in which “evaluation of amino acid mutation ΔE (for example, ΔG)” optimizes the amino acid side chain and enables acquisition of a highly specific physiologically active peptide.
[0112]
The specific formulas listed in “I. Primary Screening”, “II. Secondary Screening”, and “III. Tertiary Screening” are merely examples, and formulas synonymous with them and formulas derived therefrom are also included. All are used in the present invention in each calculation.
[0113]
The present invention is described in detail below.
The method of the present invention only needs to have the steps (a1) to (g1) as shown in (1) above, and the above (a1 ′) to (i1) as shown in (5) above. It suffices if it has the step of ').
[0114]
Specific means and form for carrying out the method according to the present invention are not limited, but considering the huge amount of data to be processed, the above (9) to (12) and (13) The computer processing by the program according to the present invention listed in (16) is the most preferred embodiment. The processing steps included in the program according to the present invention are equal to the technical idea of each step of the method invention according to the present invention. Therefore, hereinafter, the program according to the present invention will be described in detail to simultaneously describe the method according to the present invention.
[0115]
The program of the present invention is designed to design a bioactive peptide that interacts with a target amino acid sequence and a bioactive peptide that interacts with a target protein (that is, not only the target amino acid sequence, From the viewpoint of designing a physiologically active peptide that also takes into account the interaction with the amino acid sequence of (2), it can be roughly divided into two. One is a program shown in the above (9) to (12), which designs a bioactive peptide that interacts with a target amino acid sequence. In particular, the steps (a1) to (f1) included in the program (9) can also be referred to as “primary screening”.
[0116]
The other of the programs according to the present invention is the program shown in the above (13) to (16), which designs a bioactive peptide that interacts with a target protein. In particular, the steps (a1 ′) to (f1 ′) included in (13) are referred to as “primary screening”, and the steps (g1 ′) to (i1 ′) are referred to as “secondary screening”. It can also be called. By combining these “primary screening” and “secondary screening”, it is possible to obtain a more appropriate physiologically active peptide for the target site of the target protein.
[0117]
Furthermore, the steps (I) to (III) included in the above (24) can be combined with the above programs (13) to (16). These steps (I) to (III) can also be referred to as “tertiary screening”. By further combining “tertiary screening” with “primary screening” and “secondary screening”, it becomes possible to obtain a physiologically active peptide with higher specificity for the target site of the target protein.
[0118]
In another aspect, the present invention provides the programs of (25) and (26) above. The steps (a2) to (b2) included in the program (25) can also be called “primary screening”. The steps (a2 ′) to (b2 ′) included in the program (26) can be called “primary screening”, and the steps (c2 ′) to (e2 ′) This can be called “secondary screening”. Note that the steps (I) to (III) (“tertiary screening”) included in (27) can be combined with the program (26).
[0119]
In still another aspect, the present invention provides the programs of (28) and (29) above. The steps (a3) to (h3) included in the program (28) can also be referred to as “primary screening”. The steps (a3 ′) to (h3 ′) included in the program (29) can be referred to as “primary screening”, and the steps (i2 ′) to (k2 ′) This can be called “secondary screening”. In addition, the steps (I) to (III) (“tertiary screening”) included in the above (30) can be combined with the above program (29).
[0120]
An example of the bioactive peptide design in the present invention is shown in FIG. 1, and the entire system of the present invention is shown in FIG. In addition, although the secondary screening is described in FIG.1 and FIG.2, this secondary screening is performed as needed, and also combined with the tertiary screening described in FIG. Also good.
[0121]
In the present invention, the primary screening is composed of three types of programs, which are properly used according to the properties of the target protein. On the other hand, the secondary screening is a common program, and the tertiary screening is a common program. First, selection of primary screening will be described.
[0122]
FIG. 3 is a flowchart showing a program flow in selection of primary screening. As the kind of primary screening, the most appropriate one can be selected according to three judgment criteria including presence / absence of ligand information, continuity / discontinuity of the target site, whether the enzyme has a pocket on the surface. Hereinafter, selection of primary screening will be described in detail with reference to steps 501 to 506 in FIG.
[0123]
Step 501 in FIG. 3 is a step of determining whether or not there is a peptide serving as a ligand for a certain target protein. That is, when the ligand for a certain target protein is unknown, the process proceeds to step 502 in FIG. 3, and when the ligand is known, the process proceeds to step 504 in FIG.
[0124]
Step 502 in FIG. 3 is a step of determining whether or not the target site of the target protein is mainly composed of a continuous amino acid sequence. When the target site of the target protein has been elucidated, for example, by analyzing the crystal structure, the determination is made based on the information. When the target site of the target protein is unknown, for example, it is determined by a three-dimensional structure prediction program known in the art. As a result, when the target site of the target protein is composed of a discontinuous amino acid sequence, amino acid position-dependent binding significance evaluation is performed as a primary screening. On the other hand, when the target site of the target protein is mainly composed of a continuous amino acid sequence, the process proceeds to step 503 in FIG.
[0125]
Step 503 in FIG. 3 is a step of determining whether the target protein is an enzyme or a protein having a pocket on the surface. An example of a protein having a pocket on the surface is a receptor. Further, when the type of the target protein (for example, enzyme, receptor, etc.) is unknown, the type of the target protein can be predicted by, for example, homology search. In step 503 of FIG. 3, when it is determined that the target protein is neither an enzyme nor a protein having a pocket on the surface, amino acid complementation profile waveform evaluation is used as the primary screening. On the other hand, when it is determined that the target protein is either an enzyme or a protein having a pocket on the surface, amino acid complementation profile waveform evaluation or amino acid position-dependent binding significance evaluation is used as the primary screening. Preferably, however, amino acid position dependent binding significance assessment is used.
[0126]
Step 504 in FIG. 3 is a step of determining whether or not the target site of the target protein is mainly composed of a continuous amino acid sequence. In step 504 in FIG. 3, the determination is made in the same manner as in step 502 in FIG. As a result, when it is determined that the target site of the target protein is composed of a discontinuous amino acid sequence, the process proceeds to step 505 in FIG. On the other hand, if it is determined that the target site of the target protein is mainly composed of a continuous amino acid sequence, the process proceeds to step 506 in FIG.
[0127]
Step 505 in FIG. 3 is a step for determining whether the target protein is an enzyme or a protein having a pocket on the surface. In step 505 of FIG. 3, the determination is made in the same manner as in step 503 of FIG. When it is determined that the target protein is neither an enzyme nor a protein having a pocket on the surface, amino acid interaction region evaluation is used as the primary screening. On the other hand, when the target protein is determined to be either an enzyme or a protein having a pocket on the surface, amino acid interaction region evaluation or amino acid position-dependent binding significance evaluation is used as the primary screening. Preferably, however, amino acid interaction region assessment is used.
[0128]
Step 506 in FIG. 3 is a step of determining whether the target protein is an enzyme or a protein having a pocket on the surface. In step 506 of FIG. 3, the determination is made in the same manner as in step 503 of FIG. When it is determined that the target protein is neither an enzyme nor a protein having a pocket on the surface, amino acid complementation profile waveform evaluation or amino acid interaction region evaluation is used as the primary screening. Region evaluation is used. On the other hand, when the target protein is determined to be either an enzyme or a protein having a pocket on the surface, as a primary screening, amino acid complementation profile waveform evaluation, amino acid interaction region evaluation, or amino acid position-dependent binding Any of significance evaluation may be used, but preferably, amino acid interaction region evaluation or amino acid position-dependent binding significance evaluation is used, and more preferably, amino acid interaction region evaluation is used.
[0129]
Hereinafter, with respect to the primary screening, steps 101 to 111 (amino acid complementary profile waveform evaluation) in FIG. 4, steps 301 to 302 (amino acid interaction region evaluation) in FIG. 6 and steps 401 to 408 in FIG. This will be described with reference to (joining significance evaluation). The secondary screening will be described with reference to step 112 and subsequent steps in FIG. The tertiary screening will be described with reference to steps 201 to 204 in FIG.
[0130]
FIG. 4 is a flowchart showing the program flow of the above (9) and (13). First, the program (9) will be described in detail below.
[0131]
Step (a1) corresponds to step 101 in the flowchart of FIG. Examples of the data input means include a touch panel, a keyboard, and a mouse. As other input means, a pen tablet, a voice input device, or the like can be used. As an input target amino acid sequence, an amino acid sequence such as a ligand binding site, a substrate binding site, a protein interaction site, and the like is appropriately selected. A person skilled in the art can appropriately select a target amino acid sequence and input the sequence data based on X-ray analysis data on a certain protein or based on a prediction such as a general protein three-dimensional structure prediction program regarding a certain protein. In addition, a virtually set amino acid sequence (that is, an arbitrary amino acid sequence) may be input as a target amino acid sequence regardless of the above method.
[0132]
The program (9) does not include a step corresponding to step 102 shown in the flowchart of FIG. 4, but may include a step corresponding to step 102 of FIG. 4 as necessary. In step 102 of FIG. 4, one or more amino acid indices and a window width can be input, and although not specifically described, a filter value of the complementarity parameter (for example, correlation coefficient filter value R_t), P_aveFilter values a and b, threshold values of intermolecular energy parameters (for example,
[0133]
[Expression 17]

[0134]
) Etc. can also be accepted. As the input means, the same input means used in step 101 of FIG. 4 can be used. These parameters may be in a format that the user selects and inputs one by one at the time of operation, or may have a format in which initial values are set in advance and can be changed by the user as desired.
[0135]
The step (b1) corresponds to step 103 in FIG. In step (b1) above, one or more profile waveforms are generated from the sequence data of the target amino acid sequence input in step 101 of FIG. 4 according to the one or more amino acid indices set in step 102 of FIG. Conversion into one or more moving average profile waveforms (step 103 in FIG. 4). Specifically, the arithmetic processing represented by the above [Formula 1] is performed on the obtained target amino acid sequence. The data of one or more moving average profile waveforms for the target amino acid sequence is sent to step 107 in FIG.
[0136]
Step (c1) corresponds to step 104 and step 105 in FIG. In the above step (c1), a complementary amino acid sequence candidate is generated (step 104 in FIG. 4), and one or more profiles are generated according to the one or more amino acid indices set in step 102 in FIG. A waveform is generated and then converted to one or more complementary moving average profile waveforms (step 105 of FIG. 4). Specifically, the arithmetic processing shown in the above [Formula 2] is executed for the obtained complementary amino acid sequence candidates. The one or more complementary moving average profile waveform data for the complementary amino acid sequence candidates is sent to step 107 of FIG. 4 for calculation of the complementarity parameter.
[0137]
The program (9) does not include a step corresponding to step 106 in FIG. 4, but may include a step corresponding to step 106 in FIG. 4 as necessary. When the number of amino acid residues in the target amino acid sequence is n, 20ⁿStep 106 in FIG. 4 instructs to repeat Step 104 and Step 105 in FIG. 4 until candidate complementary amino acid sequences are generated and each of them is converted into one or more complementary moving average profile waveforms. 20ⁿWhen complementary amino acid sequence candidates are generated and each of them is converted into one or more complementary moving average profile waveforms, generation of complementary amino acid sequence candidates (step 104 in FIG. 4) and the accompanying complementary moving average profile Waveform generation (step 105 in FIG. 4) ends.
[0138]
Step (d1) corresponds to step 107 in FIG. In the step (d1), one or more complementation parameters derived from the same amino acid index (for example, the correlation coefficient represented by [Equation 3]) are one or more moving average profile waveforms for the target amino acid sequence and complementary amino acids. Calculations are made between one or more complementary moving average profile waveforms of sequence candidates (step 107 in FIG. 4).
[0139]
For example, when only one amino acid index is used in the conversion of the moving average profile waveform of the target amino acid sequence and the complementary moving average profile waveform of the complementary amino acid sequence, only one complementarity parameter is the step of FIG. Calculated at 107.
[0140]
Further, when two or more amino acid indices are used in the conversion of the moving average profile waveform of the target amino acid sequence and the complementary moving average profile waveform of the complementary amino acid sequence, the two or more complementation parameters are the steps in FIG. Calculated at 107.
[0141]
The program (9) does not include a step corresponding to step 108 in FIG. 4, but may include a step corresponding to step 108 in FIG. 4 as necessary. In step 108 of FIG. 4, one or more average value parameters are calculated based on the one or more amino acid indices used.
[0142]
The step (e1) is not specifically shown in FIG. However, the step (e1) can be included between the step 108 and the step 109 in FIG. In the above step (e1), the complementary amino acid sequence candidates are converted into one or more of the complementation parameters calculated in step 107 of FIG. 4 (if necessary, one or more average value parameters calculated in step 108 of FIG. 4). ) And the storage means.
[0143]
The step (f1) corresponds to step 109 in FIG. In step (f1) above, complementary amino acid sequence candidates based on one or more complementation parameters calculated in step 107 of FIG. 4 (if necessary, one or more average value parameters calculated in step 108 of FIG. 4). To extract. The extraction is performed based on a condition based on the filter value. The filter value may be in a format that is selected and input by the user one by one at the time of operation, or may be in a format in which an initial value is set in advance and the user can change as desired.
[0144]
When only one complementarity parameter is calculated between the target amino acid sequence and the complementary amino acid sequence in the step (d1), the complementary amino acid sequence candidate is the one complementarity parameter in the step (f1). Extracted so as to satisfy the filter value condition for. In this extraction process, a filter value of one average value parameter can be used together.
[0145]
In the step (d1), when two or more complementarity parameters are calculated between the target amino acid sequence and the complementary amino acid sequence, the two or more complementarity parameters are comprehensively considered in the step (f1). Then, complementary amino acid sequence candidates are extracted. In this extraction process, two or more average value parameters can be used in a comprehensive manner. When a person skilled in the art calculates two or more complementarity parameters (an average value parameter of two or more if necessary), the parameter to be regarded as important is preferentially considered and a desired complementary amino acid sequence candidate is extracted. The conditions can be set as follows.
[0146]
The program (9) does not include a step corresponding to step 110 in FIG. 4, but may include a step corresponding to step 110 in FIG. 4 as necessary. In step 110 of FIG. 4, it is determined whether or not to further select complementary amino acid sequence candidates using another amino acid index. Specifically, with respect to the complementary amino acid sequence candidates extracted in the step (f1), according to the amino acid index (different from the previous amino acid index) set in the step 102 of FIG. It is determined whether (f1) is repeated one or more times. Whether or not the above steps (b1) to (f1) are repeated one or more times, the number of repetitions, and one or more amino acid indices used at that time may be selected in advance even if the user selects and inputs one by one at the time of operation. The format may be set and can be changed by the user as desired.
[0147]
If it is determined in step 110 of FIG. 4 that it is not necessary to further select complementary amino acid sequence candidates using another amino acid index, the process proceeds to step 111 of FIG. 4 to consider the interaction with the target protein. It is determined whether or not to do so. The program (9) is an embodiment for considering the interaction with the target amino acid sequence, and is an embodiment not considering the interaction with the target protein itself. Therefore, in step 111 of FIG. To be judged.
[0148]
The above step (g1) is not shown in FIG. 4, but can be included after N in step 111 in FIG. In the step (g1), a complementary amino acid sequence candidate for the target amino acid sequence is displayed together with its complementation parameter and the like. As the display means, a normal display device, a printer, or the like is used. Preferably, the extracted complementary amino acid sequence candidates are ranked for each parameter and displayed from the top or from the top considering each parameter comprehensively.
[0149]
Next, the program (13) will be described in detail below. This program considers the interaction with the target site of the target protein in addition to the interaction with the target amino acid sequence.
[0150]
Steps (a1 ') to (f1') of the program (13) correspond to steps (a1) to (f1) of the program (9). Accordingly, steps (a1 ') to (f1') of the program (13) are performed in the same manner as steps (a1) to (f1) of the program (9). However, in step 102 of FIG. 4, it is possible to input data on the target protein (for example, amino acid sequence data of the target protein, data on the target site of the target protein, etc.). In addition, since the said program (13) is embodiment which considers interaction with target protein itself, it is always judged as Y in step 111 of FIG.
[0151]
The step (g1 ') corresponds to step 112 in FIG. In step (g1 '), an intermolecular energy parameter between the target site of the target protein and the complementary amino acid sequence candidate is calculated. This calculation uses data on the target protein (for example, target protein amino acid sequence data, target protein target site data, etc.), complementary amino acid sequence candidate sequence data, etc., input in step 102 of FIG. Executed.
[0152]
Although the step (h1 ') is not shown in FIG. 4, it can be included between step 112 and step 113 in FIG. In the step (h1 ′), the complementary amino acid sequence candidates are stored in the storage unit together with the intermolecular energy parameter calculated in step 112 in FIG.
[0153]
Step (i1 ') corresponds to step 113 in FIG. In the step (i1 ′), complementary amino acid sequence candidates that satisfy the threshold value of the intermolecular energy parameter are extracted based on the information stored in the storage means.
[0154]
Although the step (j1 ') is not shown in FIG. 4, it can be included after Y in step 113 in FIG. In the step (j1 ′), complementary amino acid sequence candidates for the target amino acid sequence are displayed together with their complementation parameters and the like. As the display means, a normal display device, a printer, or the like is used. Preferably, the extracted complementary amino acid sequences are ranked and displayed based on good intermolecular energy parameters.
[0155]
Further, the steps (I) to (III) can be included between the step (i1 ') and the step (j1'). The steps (I) to (III) correspond to steps 201 to 203 in FIG. Hereinafter, the steps (I) to (III) will be described in detail.
[0156]
Step (I) corresponds to step 201 in FIG. In the step (I), an amino acid sequence in which an amino acid mutation is introduced into the amino acid sequence extracted in the step (i1 ′) is generated. When one amino acid is substituted for the amino acid sequence extracted in the above step (i1 '), an amino acid sequence substituted with 19 kinds of natural amino acids other than the original amino acid is comprehensively generated. Further, a plurality of (2 or 3 or more) amino acids are substituted into the amino acid sequence extracted in the above step (i1 ′), and these amino acid sequences are comprehensively generated (for example, two amino acids are When substituting with other natural amino acids, a 19 × 19 amino acid sequence is generated). Furthermore, not only natural amino acids but also non-natural amino acid sequences can be used for amino acid substitution. These amino acid data may be in a format that uses data stored in advance, or may be in a format that takes in necessary data by referring to an external database.
[0157]
Step (II) corresponds to step 202 in FIG. In the step (II), intermolecular energy parameters between all the amino acid sequences generated in the step (I) and the target site of the target protein are calculated. This calculation is performed in the same manner as in the above step (g1 ').
[0158]
The above step (III) corresponds to step 203 in FIG. In the step (III), the intermolecular energy parameter calculated in the step (II) is compared with the intermolecular energy parameter between the amino acid sequence extracted in the step (i1 ′) and the target site of the target protein as a control. Then, an amino acid sequence having an intermolecular energy parameter that is more stable than the control intermolecular energy parameter is extracted. As the intermolecular energy parameter between the amino acid sequence extracted in step (i1 ′) and the target site of the target protein, the value calculated in step (g1 ′) can be used. As a result of the comparison, an amino acid sequence having an intermolecular energy parameter that is more stable than the control intermolecular energy parameter is extracted.
[0159]
Note that step 204 in FIG. 5 may be included after step (III). In step 204 of FIG. 5, it is determined whether the steps (I) to (III) are repeated again for the amino acid sequence extracted in step (III). When it is determined in step 204 in FIG. 5 that the above steps (I) to (III) do not need to be repeated again, the extraction of the amino acid sequence is completed, and the process proceeds to step (j1 ′). However, a step of performing optimization processing of each amino acid side chain can also be provided after step 204 of FIG. In such a case, after the step of optimizing each amino acid side chain is completed, the process proceeds to step (j1 ′).
[0160]
FIG. 6 is a flowchart showing a program flow for the above (25) and a part of the above (26). This program is particularly useful when the primary structure (amino acid sequence) of a protein that interacts with or is predicted to interact with a target protein is known. First, the program (25) will be described in detail below.
[0161]
Step (a2) in the program (25) corresponds to step 301 in FIG. In the step (a2), the interaction region in the protein that interacts with the target site of the target protein is identified. When there is a protein in which a region that can interact with the target protein already exists, that region is selected. When a plurality of interaction regions are present in one protein, the plurality of regions are selected as interaction regions. When a plurality of proteins that can interact with the target protein are known, a plurality of interaction regions can be selected from each of these proteins. On the other hand, if the protein itself that interacts with the target protein is known, but the interacting region is not specified, this region is obtained by a method well known in the art (for example, the RBD method [reference document 5]). Can be selected.
[0162]
Step (b2) corresponds to step 302 in FIG. In the step (b2), an amino acid sequence having an arbitrary length is extracted from the interaction region. FIG. 7 shows an outline of the amino acid sequence extraction performed in the above step (b2). As long as the length of the amino acid sequence extracted in the step (b2) (that is, the number of amino acid residues) is within the total length of the interaction region, an amino acid sequence having an arbitrary length can be extracted. Is possible. In addition, extraction of an amino acid sequence is performed exhaustively. For example, when extracting an amino acid sequence consisting of X amino acid residues, N−X + 1 amino acid sequences are extracted from the N-terminal to the C-terminal of the interaction region. Further, the lengths of the extracted amino acid sequences may be unified, but amino acid sequences having different lengths may be comprehensively extracted.
[0163]
Next, the program (26) will be described in detail below.
[0164]
The steps (a2 ') to (b2') of the program (26) correspond to the steps (a2) to (b2) of the program (25). Accordingly, the steps (a2 ') to (b2') of the program (26) are performed in the same manner as the steps (a2) to (b2) of the program (25).
[0165]
The steps (c2 ') to (f2') of the program (26) correspond to the steps (g1 ') to (j1') of the program (13). Therefore, the steps (c2 ') to (f2') of the program (26) are performed in the same manner as the steps (g1 ') to (j1') of the program (13).
[0166]
Further, the steps (I) to (III) can be included between the step (e2 ') and the step (f2'). The steps (I) to (III) are performed in the same manner as described above.
[0167]
FIG. 8 is a flowchart showing the program flow for part (28) and part (29). This program is particularly useful for an enzyme or the like (for example, peptidase) that has a pocket on the surface of a molecule as a target protein and is considered to have little structural change due to binding. An outline of the process performed in (28) is shown in FIG. Hereinafter, the program (28) will be described in detail, but in order to deepen the understanding, caspases will be sequentially described as an example of the target protein.
[0168]
First, a protein serving as a caspase substrate will be described, and its amino acid position will be defined. The amino acid sequence of the cleavage site of the protein serving as a caspase substrate is the XXXDD motif. The position of D in the motif indicates the P1 site on the N-terminal side of the substrate cleavage site. Aspartic acid is essential at the P1 position, and the difference in inhibitory peptide recognized by each caspase is considered to depend on the remaining amino acid sequences at the P2 to P4 positions. Therefore, hereinafter, in order to clearly show each amino acid position in the XXXXD motif, it is expressed as P4-P3-P2-D.
[0169]
Step (a3) in the program (28) corresponds to step 401 in FIG. In the step (a3), an amino acid sequence having a certain length is comprehensively generated, and an amino acid sequence is randomly selected from the amino acid sequences and extracted as an analysis library. Although not included in the above step (a3), a step of extracting an evaluation library can be further included in step 401 of FIG. The “constant length” (that is, the number of amino acid residues) of the amino acid sequence comprehensively generated in the step (a3) is not particularly limited, but preferably about 2 to 10 amino acid residues. The length is more preferably about 3 to 5 amino acid residues.
[0170]
For example, to comprehensively analyze the P4-P3-P2-D motif, the amino acid sequence of 4 amino acid residues is 20^ThreeThat is, 8000 combinations must be considered (D is fixed). Here, for example, 400 amino acid sequences corresponding to 5% thereof are randomly selected, and 360 amino acid sequences can be extracted as an analysis library and 40 amino acid sequences can be extracted as an evaluation library. In addition, for caspases, the inhibitory peptides are known, but the inhibitory peptides in the complex crystal structure of caspase inhibitory peptides retain almost the same main chain structure in any caspase. That is, it is considered that the shape of the caspase active site and the catalytic mechanism limit it. Such knowledge can also be used in generating peptide conformations. For example, when generating a peptide conformation, the structure of the main chain is the structure in the crystal structure, and any peptide can be constructed by adding a side chain. Energy optimization can also be performed using TINKER [Ref 12] to exclude side chain VDW contact. At this time, the main chain may be fixed using the INACTIVE command.
[0171]
The step (b3) corresponds to step 402 in FIG. In the step (b3), an intermolecular energy parameter is calculated for each amino acid sequence extracted as the analysis library. This calculation is performed in the same manner as the step (g1 ') of the program (13). For example, the calculation shown by the following [Equation 18] can also be performed using AutoDock for 8000 amino acid sequences exhaustively generated for the P4-P3-P2-D motif.
[0172]
[Expression 18]

[0173]
Each coefficient of the above formula is a value empirically determined by regression analysis using 30 protein-ligand complex structures and their measured Ki values. In AutoDock 3.0, a genetic algorithm based on Lamarck's evolution theory is newly adopted for location search. Here, the main chain of the 4-residue peptide is fixed, and the side chain is variable. Examples of parameter values to be set are shown in Table 2. As described above, the details of the calculation conditions can be set as appropriate.
[0174]
[Table 2]

[0175]
In addition, although not included in the above step (b3), the arrangement of the extracted amino acid sequence (eg, based on RMS (main chain)) is compared with the arrangement of the control (eg, peptide in the crystal structure). Thus, the step (b3) may include a step of excluding the improper arrangement from the subsequent calculation. For example, the arrangement of the amino acid sequence of the P4-P3-P2-D motif is confirmed by RMS (main chain) with the inhibitory peptide in the crystal structure. And what has a large RMS can be excluded from the subsequent calculations because it is considered not to be properly placed in the caspase active site and function as a substrate. It may seem that there is no problem if it is strongly bound in any arrangement, but in order for a modifying group such as FMK and CHO to be arranged at the caspase active center, proper arrangement of the peptide is an essential factor. Because.
[0176]
Step (c3) corresponds to step 403 in FIG. In the step (c3), a score matrix based on the appearance frequency of amino acids is generated using the intermolecular energy parameter calculated in the step (b3). The intermolecular energy parameter threshold (for example, ΔG threshold) may be set in advance or may be set as described later. For example, a PSS matrix based on the appearance frequency of 20 types of amino acids at each position P4, P3, and P2 is generated using an analysis library including 360 amino acid sequences. PSS of amino acid i at position j_ijIs calculated by the above [Equation 9]. In this case, the range of position j is 1 to 3, corresponding to P4 to P2, respectively, and the range of amino acid i is 1 to 20, corresponding to each amino acid type.
[0177]
The step (d3) corresponds to step 404 in FIG. In the step (d3), a score based on the amino acid appearance frequency is calculated using a score matrix based on the amino acid appearance frequency. For example, by using a PSS matrix, the strength of the binding force to a caspase of an arbitrary amino acid sequence consisting of four amino acid residues (P4-P3-P2-D motif: P1 is not fixed since D1 is fixed) [Formula 10] can be calculated as PSS. In this case, C_ijIs a 20 × 3 matrix and is composed of 0 or 1 values. The element that matches amino acid i at position j of any amino acid sequence is 1 and the element that does not match is 0.
[0178]
Step (e3) corresponds to step 405 in FIG. In the step (e3), a correlation analysis is performed between the intermolecular energy parameter calculated in the step (b3) and the score to obtain a regression equation. For each amino acid sequence included in the library for analysis, if there is a high correlation between the PSS and the intermolecular energy parameter (for example, binding free energy), the intermolecular energy parameter is predicted at high speed for a new amino acid sequence. Is possible. Of course, since PSS can only be evaluated independently for each position, the influence of amino acid combinations between positions cannot be considered. Note that the intermolecular energy parameter threshold (eg, ΔG threshold) described in step (c3) above maximizes the correlation coefficient R between the PSS and the intermolecular energy parameter threshold (eg, ΔG threshold). It may be set to be. In this case, the threshold value set to maximize the correlation coefficient R is fed back to the step (c3) again, and the step (c3), the step (d3), and the step (e3) are performed. Again.
[0179]
Step (f3) corresponds to step 406 in FIG. In the step (f3), the regression matrix is used to convert the score matrix based on the amino acid appearance frequency into a matrix based on the amino acid position-dependent intermolecular energy parameter. In this conversion, for example, the above-described PSG matrix is created. The constant term in the regression equation may be distributed unevenly to each position, but is preferably distributed uniformly to each position.
[0180]
Step (g3) corresponds to step 407 in FIG. In the step (g3), an amino acid position-dependent intermolecular energy parameter value is calculated from a matrix based on the amino acid position-dependent intermolecular energy parameter. For example, by using a PSG matrix, the free energy of binding between an arbitrary 4-residue peptide (P4-P3-P2-D motif: P1 is fixed because D1 is fixed) and caspase can be calculated at high speed as PSG. .
[0181]
Step (h3) corresponds to step 408 in FIG. In the step (h3), an amino acid sequence that is not more than a predetermined amino acid position-dependent intermolecular energy parameter value, that is, an amino acid sequence that is not more than a threshold value is extracted. This value may be a preset value or may be set at the time of extraction.
[0182]
Next, the program (29) will be described in detail below.
[0183]
The steps (a3 ') to (h3') of the program (29) correspond to the steps (a3) to (h3) of the program (28). Accordingly, the steps (a3 ') to (h3') of the program (29) are performed in the same manner as the steps (a3) to (h3) of the program (28).
[0184]
The steps (i3 ′) to (l3 ′) of the program (29) correspond to the steps (c2 ′) to (f2 ′) of the program (26). Accordingly, the steps (i3 ') to (l3') of the program (29) are performed in the same manner as the steps (c2 ') to (f2') of the program (26).
[0185]
Further, the steps (I) to (III) can be included between the step (k3 ′) and the step (l3 ′). The steps (I) to (III) are performed in the same manner as described above.
[0186]
In addition, when a plurality of proteins having similar substrate specificities are present, one of these proteins is selected as a target protein and a peptide specific to this protein is designed (28 ) And (29) are useful. As an example, the above program (28) will be described. First, the steps (a3) to (h3) of the program (28) are performed for each protein having similar substrate specificity. In addition, about each protein, the said step (a3)-(h3) may be performed in parallel, and the said step (a3)-(h3) may be performed in order. Next, a difference between amino acid position-dependent intermolecular energy parameter values (for example, PSG) is calculated for the target protein and other proteins, and a step of filtering by the difference is provided. This difference can be set as appropriate. For example, when the Ki value for the target protein is desired to be two orders of magnitude lower than that of other proteins, ΔG corresponds to 2.728 kcal / mol, and this can be used as a filter. Of course, a similar step can be provided after the step (h3 ') of the program (29), and then the process can proceed to the step (i3'). As briefly described, the present invention may further include the steps described above in the programs (28) and (29). Such a program is also included in the scope of the present invention. Further specific examples are described in Example 3 and will be helpful in understanding such programs.
[0187]
The recording medium (17) of the present invention is a recording medium in which the above-described programs (9) to (16) of the present invention are recorded on a computer-readable recording medium. Here, the “computer-readable recording medium” refers to any recording medium that can record electronic data and can be read by a computer as necessary. For example, a magnetic tape, a magnetic disk, a magnetic drum And portable information recording media such as an IC card and an optically readable disc (eg, CD, DVD).
[0188]
The extraction processing devices (18) to (20), the extraction processing devices (21) to (23), and the devices (31) and (32) according to the present invention each include a central processing unit and a memory. This is a dedicated machine for the bioactive peptide extraction process, which is mainly composed of a computer and has the above-described program of the present invention so that it can be executed.
[0189]
The devices (18) to (23) can be roughly divided into two from the viewpoints of designing a bioactive peptide that interacts with a target amino acid sequence and designing a bioactive peptide that interacts with a target protein. One is an apparatus shown in the above (18) to (20), which designs a bioactive peptide that interacts with a target amino acid sequence. The other is the apparatus shown in the above (21) to (23), which is designed to design a bioactive peptide in consideration of the interaction with the target protein itself in addition to the interaction with the target amino acid sequence. is there.
[0190]
As shown in FIG. 10, the apparatus of (18) to (20) includes a data input unit A, a data editing unit B, a complementary amino acid sequence candidate generation unit C, a complementation degree calculation unit D, and a complementary amino acid sequence candidate storage unit E. , A complementary amino acid sequence search unit, and a complementary amino acid sequence display unit.
In the apparatus of (18) to (20), the data input unit (a) includes means for executing the step (a1), and the data editing unit (b) includes means for executing the step (b1), The complementary amino acid sequence candidate generation unit c includes means for executing the step (c1), and the complementation degree calculation unit d includes means for executing the step (d1). Includes means for executing the step (e1), the complementary amino acid sequence search unit includes means for executing the step (f1), and the complementary amino acid sequence display unit performs the step (g1). Including means for performing.
[0191]
The devices (21) to (23) are similar to the devices (18) to (20) described above, as shown in FIG. 10, the data input unit A, the data editing unit B, the complementary amino acid sequence candidate generation unit C, Complement degree calculation unit D, complementary amino acid sequence candidate storage unit E, complementary amino acid sequence search unit F, and complementary amino acid sequence display unit G are included.
In the devices (21) to (23), the data input unit a includes means for executing the step (a1 ′), and the data editing unit B includes means for executing the step (b1 ′). The complementary amino acid sequence candidate generation unit c includes means for executing the step (c1 ′), and the complementation degree calculation unit D includes a complementation parameter derived from the same amino acid index in the step (k1 ′), Means for calculating between one or more moving average profile waveforms for the target amino acid sequence and one or more complementary moving average profile waveforms of candidate complementary amino acid sequences, and for calculating an intermolecular energy parameter with the target site of the target protein (Means for executing the above steps (d1 ′) and (g1 ′)), the complementary amino acid sequence candidate storage unit e Means for storing a sequence candidate together with the complementarity parameter and the intermolecular energy parameter (means for executing the above steps (e1 ′) and (h1 ′)), and the complementary amino acid sequence search unit includes a step (m1 ′ ) Including means for extracting a predetermined number of complementary amino acid sequences based on the information stored by the means (k1 ′) (means for executing the steps (f1 ′) and (i1 ′)), and displaying the complementary amino acid sequence The section includes a step (n1 ′) means for displaying the complementary amino acid sequence extracted by the complementary amino acid sequence searching section as a candidate for a physiologically active peptide (means for executing the step (i1 ′)).
[0192]
The devices (31) and (32) are the same as the devices (18) to (23) described above, and are included in the device (31) “(A2) interaction region specifying unit, ( (B) 2) amino acid sequence first search unit, (c2) intermolecular energy calculation unit, (d2) amino acid sequence storage unit, (e2) amino acid sequence second search unit, (f2) amino acid sequence display unit " And (b) a first amino acid sequence search unit, (b) a first intermolecular energy calculation unit, (c) a score matrix generation unit, and (d) a score included in the apparatus of (32) above. Calculation unit, (e 3) regression equation formation unit, (f 3) matrix conversion unit, (g 3) amino acid position-dependent energy calculation unit, (q 3) amino acid sequence second search unit, (li 3) intermolecular energy Second calculation unit, (nu 3) amino acid sequence storage unit, (le 3) amino acid sequence “3 search unit, (e 3) amino acid sequence display unit” are all the programs (26) and (29) and a computer (central processing unit (CPU), storage device) configured to execute the program. (Memory)) and other peripheral devices (external storage device, input device, display device, etc.) that are further added as necessary, and a network with another computer may be added to the configuration.
The parts (A2) to (F2) included in the device (31) and the parts (A3) to (O3) included in the device (32) are respectively (26) and (26). This is as described in detail in the description of the program 29).
[0193]
The device of the present invention is a physiologically active device such as an output device such as a printer for printing the displayed data, an external storage device for storing data, an external storage device storing a database necessary for executing the program of the present invention, etc. Other devices that provide convenience to the user in peptide design can further be included in the configuration.
[0194]
【Example】
EXAMPLES Hereinafter, the present invention will be described with reference to examples. However, the present invention is not limited to the following specific examples.
[0195]
Example 1 Design of Bioactive Peptide (Inhibitory Peptide) for Caspase-3 Amino acid sequence WRNS (SEQ ID NO: 109) at positions 206 to 209 of caspase-3 is used as a target amino acid sequence to bind to and inhibit caspase-3 activity Peptide candidates were predicted using the program of the present invention. The index based on hydrophobicity [reference 3] was used as the amino acid index, and the window width was set to 1. P_aveRange from -0.39 to -0.37, R_tWas set to −0.9, and peptide candidates having 105 complementary amino acid sequences were obtained by primary screening (Table 3). In addition, the arrangement | sequences of the ranks 1-105 of Table 3 are made into arrangement number 4-108 in order, respectively.
[0196]
[Table 3]

[0197]
Subsequently, a complementary amino acid sequence DNLD (SEQ ID NO: 97) (K_i= 0.0309 nM: 1st), DEVD (SEQ ID NO: 88) (K_i= 0.149 nM: 2nd position) (Table 3).
[0198]
DEVD (SEQ ID NO: 88) is an amino acid sequence known as a caspase-3 inhibitory peptide_iThe measured value and crystal structure of are known. FIG. 11 shows a comparison between the complementary amino acid sequence DEVD predicted by the present invention and the crystal structure. RMS (root mean squar, average deviation between atoms) (all atoms) is 2.4Å, and the structure prediction is very close to the crystal structure. The K predicted by the present invention_iMeasured value [Ref. 13] K for a value of 0.149 nM_iWas 0.23 nM.
[0199]
In addition, the currently unknown DNLD (SEQ ID NO: 97) K_iThe lowest 0.0309 nM complementary amino acid sequence was obtained. DEVD was originally obtained from the amino acid sequence of the protein used as a substrate, and also has the property of binding strongly to caspase-7 and caspase-8 in addition to caspase-3 [Ref. 13] and inhibiting it. It has also been pointed out as a problem as a chemical inhibitor. Since the peptide sequence DNLD presented in this system is a completely new sequence, the possibility of solving this specificity problem is also given.
[0200]
From the above results, it was confirmed that the program of the present invention is very useful in designing complementary amino acid candidates.
[0201]
Example 2 Design of Bioactive Peptide for Fas (Receptor)
Using the amino acid sequence FSSKCRRCRLCDEG (SEQ ID NO: 1) at positions 97 to 110 of Fas (Receptor) as a target amino acid sequence, candidates for physiologically active peptides that can bind to and interact with the target amino acid sequence were predicted using the program of the present invention. An index based on hydrophobicity [reference 3] was used as the amino acid index, and the window width was set to 5. P_aveRange from -0.15 to +0.15, R_tWas set to −0.9, and peptide candidates having complementary amino acid sequences were obtained by primary screening. The intermolecular energy was then calculated in the secondary screen. Finally, the complementary amino acid sequence EPPMTFISIHTMCH (SEQ ID NO: 2) was obtained.
[0202]
Test Example 1 Induction of apoptosis by a peptide consisting of a complementary amino acid sequence (SEQ ID NO: 2)
A peptide consisting of the complementary amino acid sequence (SEQ ID NO: 2) obtained in Example 1 (hereinafter abbreviated as Fas complementary peptide) was chemically synthesized. Subsequently, using a human ovarian cancer cell line NOS4 expressing Fas, the ability of the Fas complementary peptide to induce apoptosis was compared with that of the scrambled peptide TFIHPSMMTMPEI (SEQ ID NO: 3). NOS4 has been established and maintained in our laboratory from cancer cells excised from severe ovarian cancer patients. Human ovarian cancer cell line NOS4 is 5% CO 2 in RPMI medium containing 10% fetal calf serum.₂Culturing was performed at 37 ° C. under humidity. 5 × 10⁶One human ovarian cancer cell line NOS4 was treated for 24 hours at 37 ° C. in the presence of 100 μg / ml complementary peptide, and then DNA fragmentation was measured. DNA fragmentation was measured by flow cytometry using a FACS apparatus (FACSCalibur, Jose., CA) after staining the cell nucleus with propidium iodide. As a result, the Fas complementary peptide induced apoptosis in about 40% of cells at a concentration of 100 μg / ml. From the above results, it was confirmed that the Fas complementary peptide functions as a physiologically active peptide for Fas.
[0203]
[Table 4]

[0204]
Test Example 2 Induction of apoptosis by Fas complementary peptide tetramer
Tetramer of Fas complementary peptide in which Fas complementary peptide (hereinafter sometimes abbreviated as FRP-2) is bound to 4 branches of lysine polymer (MAP) [(FRP-2)_Four-MAP] was chemically synthesized. Next, (FRP-2)_Four-The apoptosis-inducing ability of MAP was examined using the same method as in Test Example 1. As a result, the tetramer of Fas-complementary peptide induced apoptosis in about 50% of human ovarian cancer cell line NOS4 at a concentration of 5 mg / ml (FIG. 12). From the above results, it was found that Fas-complementary peptide has about 30 times stronger apoptosis-inducing ability than monomer by using MAP as a maltimer.
[0205]
Test Example 3 In vivo apoptosis induction by tetramers of Fas complementary peptides
Using a tumor-bearing animal experimental system in which a human glioma cell line (U251-SP) was transplanted into the brain of a nude mouse, (FRP-2)_Four-The anti-tumor effect of MAP in vivo was examined. One week after transplantation of U251-SP into the brain of nude mice, (FRP-2)_Four-MAP was injected locally at 2 μg / 2 μl into cancerous tissue in the brain. After 30 days, the patient was dissected and a brain tissue cut preparation prepared by formalin fixation was prepared by a conventional method and observed under an optical microscope. As a result, in the tetramer-treated group of Fas-complementary peptide, cancer atrophy due to cancer cell death due to induction of apoptosis was observed (FIG. 13). From the above series of results, it was confirmed that the program of the present invention is very useful in designing bioactive peptides.
[0206]
Example 3 Evaluation of existing caspase peptide inhibitors
First, primary screening was performed using caspase-3, -7, -8, -9. The results are shown in FIGS. PSS and ΔG in any caspase_calcMean correlation coefficient R (FIG. 14D, 15D, 16D, 17D) was obtained.
[0207]
Next, in order to evaluate the predictive ability of PSS, ΔG was used using 40 peptides of PSS included in the library for evaluation._calc(FIGS. 14E, 15E, 16E, and 17E). A correlation coefficient R having the same value as the analysis library and an average of −0.66 was obtained. Therefore, it can be said that it can be sufficiently used as a primary screening of a huge peptide library.
[0208]
PSG evaluation of each caspase is based on the inhibitory peptides Ac-WEHD-CHO (SEQ ID NO: 110), Ac-YVAD-CHO (SEQ ID NO: 111), Ac-DEVD-, whose known Ki values (Table 5) [Reference 13] are known. This was carried out using CHO (SEQ ID NO: 112), Boc-IETD-CHO (SEQ ID NO: 113), and Boc-AEVD-CHO (SEQ ID NO: 114).
[0209]
[Table 5]

[0210]
A Ki value of 10,000 nM or more was set as 10,000 nM, and the Ki value was converted to ΔG by [Equation 19] (Table 6).
[0211]
[Equation 19]

[0212]
[Table 6]

[0213]
In addition, the predicted ΔG of the five inhibitory peptides was calculated by [Equation 4] using the PSG matrices of caspase-3, -7, -8, and -9, respectively. Table 6 shows a comparison with actual measurement values.
[0214]
Overall, the predicted value is lower than the measured value, but the correlation coefficient R_pepThe average value is 0.93, which is high. This indicates that it is possible to predict the relative affinity of the inhibitory peptide for caspase. Correlation coefficient R_caspThe average of the values was 0.71. This shows that the specificity of the inhibitory peptide for caspase can be predicted.
[0215]
Example 4 Design of caspase-3 specific inhibitory peptide
The PSG matrix enables fast affinity and specificity prediction. ΔG for each caspase of 8000 peptides that can be expressed by P4-P3-P2-D can be calculated at high speed using PSG. The design of caspase-3 specific inhibitory peptide will be described as an example. The system configuration is shown in FIG.
[0216]
First, one peptide is extracted from the Virtual Library containing 8000 peptides, and the PSG matrix of caspase-3, -7, -8, -9 is used._casp-3, PSG_casp-7, PSG_casp-8, PSG_casp-9Calculate Next, the difference in PSG between caspase-7, -8, -9 and caspase-3 is calculated using the following [Equation 20].
[0217]
[Expression 20]

[0218]
In this system, caspase-3 specific inhibitory peptide candidates were defined as peptides having Ki values two orders of magnitude lower than all of caspase-7, -8, and -9. A difference of two digits in Ki value corresponds to a difference of about 2.728 kcal / mol in ΔG. Therefore, [Formula 21] was used as a filter for each caspase.
[0219]
[Expression 21]

[0220]
The above operation is performed on all 8000 peptides, and only the peptides that have passed through all the caspase filters are evaluated by the secondary screening. The inhibitory peptide finally selected in the secondary screening is evaluated in vitro and in vivo as a designed peptide.
[0221]
Peptide sequences satisfying [Formula 20] in all caspases-7, -8, and -9 were selected from 8000 peptide sequences. The results are shown in Table 7.
[0222]
[Table 7]

[0223]
In Table 7, PSG for each caspase and ΔGcalc which is the secondary screening result are shown. For the three peptides PPVD (SEQ ID NO: 115), QPVD (SEQ ID NO: 116), and TPVD (SEQ ID NO: 117), a high correlation of 0.93 or higher was observed between PSG and ΔGcalc. On the other hand, SPVD (SEQ ID NO: 118) showed a low correlation of 0.73 compared to the above three peptides. The binding free energy of SPVD to caspase-8 was predicted to be -9.99kcal / mol for PSG, whereas it was -10.92kcal / mol for ΔGcalc. Overlapping with the fact that ΔGcalc for caspase-3 was highly evaluated as -11.20 kcal / mol, the difference in ΔGcalc between caspase-3 and -8 is only 0.28 kcal / mol. This result suggests that it does not function as a caspase-3 specific inhibitory peptide.
[0224]
In PPVD, QPVD, and TPVD, ΔGcalc for caspase-3 is predicted to be higher than that of PSG, so although it cannot be expected to have a two-digit difference in Ki value, it is considered to function well as a caspase-3 inhibitory peptide. .
[0225]
As a result, the following three candidate peptides are presented as caspase-3 specific inhibitory peptides.
[0226]
[Table 8]

[0227]
Example 5 Design of apoptosis-inducing peptide using Fas binding region in FasL
For the preparation of a fragmented peptide library, first, the 144 to 281 positions in the extracellular region of Fas Ligand were subjected to the RBD method, and the binding region to Fas was identified as the 151 to 176 positions. Next, this limited region was extracted 4 residues from the N-terminal side to obtain a total of 23 fragmented peptides.
[0228]
For Fas (Receptor), the binding region with FasL was identified at positions 99-102 by the RBD method. Using the amino acid sequence SKCR (SEQ ID NO: 119) at positions 99 to 102 as a target region, peptides capable of binding and interacting with the target region were selected by secondary screening using a fragmented peptide library.
[0229]
As a result, the amino acid sequence WEDT (SEQ ID NO: 120) (Ki = 0.19 μM) in regions 162 to 165 on Fas Ligand was the most binding sequence (FIG. 19). This region has also been confirmed to be a FAS binding region from the Fas-Fas Ligand complex model.
[0230]
Next, as the third screening, WEDT was used as a lead peptide, and amino acid substitution was performed for one residue from the first residue to the fourth residue, and mutation was performed (Table 9).
[0231]
[Table 9]

[0232]
From Table 9, the Ki value of WEWT (SEQ ID NO: 121) was 0.083 μM. WEWT was predicted to be a peptide having a higher binding force with a ΔG of 0.52 kcal / mol lower than WEDT, and WEWT was designed as a bioactive peptide candidate.
[0233]
Test Example 4 Induction of apoptosis by FasL-like peptide tetramer
Since Fas acts as a trimer, in order to allow the WEWT peptide (SEQ ID NO: 121) to fit well into the space 37Å formed by the Fas trimer, this peptide is used as the 4-amino group of MAP-8. Was combined with the remaining four protected branches to create a candidate peptide (hereinafter referred to as (FLLP-1)_Four-MAP₈Abbreviated. (FIG. 20). Then (FLLP-1)_Four-MAP₈The apoptosis-inducing ability of was examined using the same method as in Test Example 1 above. As a result, (FLLP-1)_Four-MAP₈Induced apoptosis in about 50% of human ovarian cancer cell line NOS4 at a concentration of about 0.1 μg / ml (Table 10).
[0234]
[Table 10]

[0235]
From the above results, it was confirmed that this method is extremely useful.
[0236]
References
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[3] Eisenberg D, et al., J. Ann. Rev. Biochem., 53, 596-623 (1984)
[4] Cosic I, et al., J IEEE Trans. Biomed. Eng., 32, 337-341 (1985)
[5] Gallet X. et al, J. Mol. Biol., 302, 917-926 (2000)
[6] Zhao, Y. et al., J. Immunol. 167, 2130-2141 (2001)
[7] Eisenberg D, et al., Proc. Natl. Acad. Sci. USA, 81, 140 (1984)
[8] Allinger NL, et al. J. Am. Chem. Soc., 99, 8127-8134 (1977)
[9] Weiner SJ, et al., J. Am. Chem. Soc., 106, 765-784 (1984)
[10] Brooks BR, et al., J. Comput. Chem., 4, 187 (1983)
[11] Wang J, et al., Proteins, 36, 1-19 (1999)
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[13] Garcia-Calvo M, et al., J. Biol. Chem., 273, 32608-32613 (1998)
[0237]
【The invention's effect】
According to the present invention, an economical, rapid and efficient physiological activity can be achieved without using costly and time-consuming biochemical techniques or conventional biologically active peptide prediction theory that is not reliable and cannot be used to narrow down candidates. Peptide design is made possible by mathematical calculations. Further, according to the present invention, a plurality of evaluation methods can be appropriately selected as primary screening depending on the properties of the target protein. Furthermore, according to the present invention, it is possible to obtain a physiologically active peptide having an optimized amino acid sequence by performing amino acid substitution in the tertiary screening and evaluating them.
[0238]
[Sequence Listing Free Text]
SEQ ID NO: 1: amino acid sequence at positions 97 to 110 of Fas.
SEQ ID NO: 2: Amino acid sequence complementary to the amino acid sequence at positions 97 to 110 of Fas.
SEQ ID NO: 3: Amino acid sequence obtained by scrambling the amino acid sequence of SEQ ID NO: 2.
SEQ ID NOs: 4 to 108: Amino acid sequence candidates complementary to the amino acid sequences at positions 206 to 209 of caspase-3.
SEQ ID NO: 109: amino acid sequence at positions 206 to 209 of caspase-3.
SEQ ID NO: 110: amino acid sequence of caspase inhibitor.
SEQ ID NO: 111: Amino acid sequence of caspase inhibitor.
SEQ ID NO: 112: amino acid sequence of caspase inhibitor.
SEQ ID NO: 113: amino acid sequence of caspase inhibitor.
SEQ ID NO: 114: amino acid sequence of caspase inhibitor.
SEQ ID NO: 115: amino acid sequence of caspase-3 specific inhibitor.
SEQ ID NO: 116 amino acid sequence of a caspase-3 specific inhibitor.
SEQ ID NO: 117: amino acid sequence of caspase-3 specific inhibitor.
SEQ ID NO: 118: amino acid sequence of caspase-3 non-specific inhibitor.
SEQ ID NO: 119: Amino acid sequence at positions 99 to 102 of Fas.
SEQ ID NO: 120: amino acid sequence at positions 162 to 165 of Fas ligand.
SEQ ID NO: 121: amino acid sequence of apoptosis-inducing peptide.
[0239]
[Sequence Listing]

[Brief description of the drawings]
FIG. 1 shows an example of bioactive peptide design.
FIG. 2 shows the entire system of the present invention in bioactive peptide design.
FIG. 3 shows a flowchart of a program used for selection of primary screening.
FIG. 4 shows a flowchart of a program used for designing a bioactive peptide. This flowchart corresponds to the case where the amino acid complement profile waveform evaluation is performed as the primary screening and the secondary screening is performed.
FIG. 5 shows a flowchart of a program for tertiary screening in bioactive peptide design.
FIG. 6 shows a flowchart of a program for amino acid interaction region evaluation (primary screening).
FIG. 7 shows the extraction of fragmented peptides from the amino acid sequence of a ligand (protein).
FIG. 8 shows a flowchart of a program for amino acid position-dependent binding significance evaluation (primary screening).
FIG. 9 shows an outline of amino acid position-dependent binding significance evaluation (primary screening).
FIG. 10 shows an example of the configuration of an apparatus for designing a bioactive peptide.
FIG. 11 shows the superposition of the complementary amino acid sequence DEVD and the crystal structure.
FIG. 12 shows the ability of Fas-complementary peptide tetramers to induce apoptosis.
FIG. 13 shows statistical data of mouse brain tissue and tumor volume treated with Fas complementary peptide tetramer.
FIG. 14 shows the results of primary screening (amino acid position-dependent binding significance evaluation) using caspase 3 as a target protein. FIG. 14A shows the PSS at each position of the motif. FIG. 14B shows the PSS matrix at each position of the motif. FIG. 14C shows the PSG matrix at each position of the motif. FIG. 14D shows a correlation analysis using the analysis library. FIG. 14E shows a correlation analysis using the evaluation library.
FIG. 15 shows the results of primary screening (amino acid position-dependent binding significance evaluation) using caspase 7 as a target protein. FIG. 15A shows the PSS at each position of the motif. FIG. 15B shows the PSS matrix at each position of the motif. FIG. 15C shows the PSG matrix at each position of the motif. FIG. 15D shows a correlation analysis using the analysis library. FIG. 15E shows a correlation analysis using the evaluation library.
FIG. 16 shows the results of primary screening (amino acid position-dependent binding significance evaluation) using caspase 8 as a target protein. FIG. 16A shows the PSS at each position of the motif. FIG. 16B shows the PSS matrix at each position of the motif. FIG. 16C shows the PSG matrix at each position of the motif. FIG. 16D shows a correlation analysis using the analysis library. FIG. 16E shows a correlation analysis using the evaluation library.
FIG. 17 shows the results of primary screening (amino acid position-dependent binding significance evaluation) using caspase 9 as a target protein. FIG. 17A shows the PSS at each position of the motif. FIG. 17B shows the PSS matrix at each position of the motif. FIG. 17C shows the PSG matrix at each position of the motif. FIG. 17D shows the correlation analysis using the analysis library. FIG. 17E shows a correlation analysis using the evaluation library.
FIG. 18 shows the system configuration in designing caspase-3 specific inhibitory peptides.
FIG. 19 shows binding free energy of Fas Ligand 4-residue peptide to Fas (99-102).
FIG. 20 shows the peptide obtained by conjugating 4 WEWT peptides to MAP-8.

Claims (15)

A method for designing a bioactive peptide capable of interacting with a target protein, comprising:
(A3) in the amino acid sequence first search part, comprehensively generating an amino acid sequence consisting of 2 to 10 amino acid residues, randomly selecting an amino acid sequence from the amino acid sequence and extracting it as an analysis library;
(B3) In the intermolecular energy first calculation unit, calculating an intermolecular energy parameter between each amino acid sequence extracted as the analysis library and the target site of the target protein;
(C3) In the score matrix generation unit, using the intermolecular energy parameter calculated in step (b3),
[Expression 1]
[Formula 9]

[ _Where a _ij represents the number of occurrences of amino acid i at position j in all peptide groups included in the library for analysis, and b _ij represents position j in peptide groups equal to or less than the threshold ΔG included in the library for analysis. Generating a score matrix PSS _ij based on the appearance frequency of amino acids generated by
(D3) In the score calculation unit, using the score matrix PSS _ij based on the appearance frequency of amino acids,
[Expression 2]
[Formula 10]

[In the formula, n represents the number of amino acids to be determined, and C _ij is a 20 × n matrix, and is composed of 0 or 1 values. And calculating a score PSS based on the frequency of appearance of amino acids calculated by: 1 for an element that matches amino acid i at position j in any amino acid sequence, and 0 for a mismatched element;
(E3) The regression equation forming unit performs a correlation analysis between the intermolecular energy parameter calculated in step (b3) and the score PSS,

Obtaining a regression equation expressed as [where a and b are constants determined by correlation analysis];
(F3) In the matrix conversion unit, the regression matrix is used to calculate the score matrix PSS _ij based on the amino acid appearance frequency,

Where a and b are the same constants as in the regression equation of step (e3) and n is the number of amino acids to be determined matrix based on amino acid position dependent intermolecular energy parameters converted by Converting to PSG _ij ;
(G3) In the amino acid position-dependent energy calculation unit, from the matrix PSG _ij based on the amino acid position-dependent intermolecular energy parameter,
[Equation 5]
[Formula 11]

[In the formula, n represents the number of amino acids to be determined, and C _ij is a 20 × n matrix, and is composed of 0 or 1 values. In addition, an element that matches amino acid i at position j in an arbitrary amino acid sequence is 1 and an element that does not match is 0. PSG _ij represents an element of ij in the matrix based on the amino acid position-dependent intermolecular energy parameter] calculating the amino acid position-dependent intermolecular energy parameter PSG value calculated by:
(H3) including a step of extracting an amino acid sequence having an amino acid position-dependent intermolecular energy parameter PSG value of −8.83 or less in an amino acid sequence second search unit. A profile that approximates a complementary average profile waveform that has a negative correlation with a moving average profile waveform that has been passed through a low-frequency range by moving average with respect to a profile waveform in which an arbitrary amino acid index is applied to the target amino acid sequence of the target protein by Method 1. Extracting an amino acid sequence (complementary amino acid sequence) characterized by having a waveform and / or extracting an amino acid sequence of an arbitrary length from the interaction region for the target protein by the method 2 (a3 ) And step (b3), wherein the method 1 comprises:
(A1) accepting sequence data input of the target amino acid sequence in the data input unit;
(B1) In the data editing unit, the target amino acid sequence is expressed as follows according to one or more predetermined amino acid indices:
[Formula 1]

[Wherein w is an odd number from 1 to 13, s is an integer part of w / 2, j is in the range of s to (ns-1), n is the target sequence length, T _i is a profile waveform obtained by applying an arbitrary amino acid index to the target amino acid sequence], and converting to one or more moving average profile waveforms x _j converted by
(C1) The complementary amino acid sequence candidate generation unit generates a complementary amino acid sequence candidate of the target amino acid sequence,
[Expression 7]
[Formula 2]

[Wherein w is an odd number from 1 to 13, s is an integer part of w / 2, j is in the range of s to (ns-1), n is the target sequence length, C _i is a profile waveform obtained by fitting one or more amino acid indices identical to those in step (b1) to complementary amino acid sequence candidates] to convert to one or more complementary moving average profile waveforms y _j converted by
(D1) Complement degree calculation unit,
[Equation 8]
[Formula 3]

Where s is an integer part of w / 2, j is in the range of s to (ns-1), and n is the target sequence length. Calculating a complementarity parameter R;
(E1) In the complementary amino acid sequence candidate storage unit, storing the complementary amino acid sequence candidate together with the complementarity parameter R in the storage means;
(F1) a complementary amino acid sequence search unit, based on the information stored in step (e1), extracting a complementary amino acid sequence having a complementarity parameter R of less than −0.9, and the method 2 Is
(A2) selecting an interaction region that has already been identified to interact in a protein that is known to interact with the target site of the target protein in the interaction region identification unit;
(B2) including a step of extracting an amino acid sequence consisting of 3 to 7 amino acid residues from the interaction region in the amino acid sequence first search part.   The amino acid index is selected from an index based on hydrophobicity, an index based on electrical characteristics, an index representing ease of taking α-helix and β-sheet, and an index representing the relative size of side chain volume The method according to claim 2, which is the above index.   For a predetermined number of complementary amino acid sequences extracted in steps (a1) to (f1) using one or more predetermined amino acid indexes, steps (b1) to 4. The method according to claim 2 or 3, further comprising further narrowing down complementary amino acid sequence candidates extracted as physiologically active peptides by repeating (f1) one or more times. (I) a step of calculating an intermolecular energy parameter with the target site of the target protein for each extracted amino acid sequence in the intermolecular energy second calculation unit;
(Ii) in the amino acid sequence storage unit, storing the amino acid sequence together with the intermolecular energy parameter in a storage means;
(Iii) a step of extracting an amino acid sequence having an intermolecular energy parameter PSG value of −12.0 or less based on the information stored in step (ii) in the amino acid sequence third search unit;
The method according to any one of claims 1 to 4, further comprising: (iv) a step of displaying the amino acid sequence extracted in step (iii) as a candidate for a bioactive peptide in an amino acid sequence display unit. Between step (iii) and step (iv),
(I) generating an amino acid sequence in which an amino acid mutation is introduced into the amino acid sequence extracted in step (iii);
(II) calculating an intermolecular energy parameter between the amino acid sequence generated in step (I) and the target site of the target protein;
(III) The intermolecular energy parameter calculated in step (II) is compared with the intermolecular energy parameter between the amino acid sequence extracted in step (iii) and the target site of the target protein as a control, and the intermolecular energy of the control is compared. And extracting the amino acid sequence having an intermolecular energy parameter that is more stable than the parameter. A program for designing a bioactive peptide capable of interacting with a target protein,
(A3) in the amino acid sequence first search part, comprehensively generating an amino acid sequence consisting of 2 to 10 amino acid residues, randomly selecting an amino acid sequence from the amino acid sequence and extracting it as an analysis library;
(B3) In the intermolecular energy first calculation unit, calculating an intermolecular energy parameter between each amino acid sequence extracted as the analysis library and the target site of the target protein;
(C3) In the score matrix generation unit, using the intermolecular energy parameter calculated in step (b3),
[Equation 9]
[Formula 9]

[ _Where a _ij represents the number of occurrences of amino acid i at position j in all peptide groups included in the library for analysis, and b _ij represents position j in peptide groups equal to or less than the threshold ΔG included in the library for analysis. Generating a score matrix PSS _ij based on the appearance frequency of amino acids generated by
(D3) In the score calculation unit, using the score matrix PSS _ij based on the appearance frequency of amino acids,
[Expression 10]
[Formula 10]

[In the formula, n represents the number of amino acids to be determined, and C _ij is a 20 × n matrix, and is composed of 0 or 1 values. And calculating a score PSS based on the frequency of appearance of amino acids calculated by: 1 for an element that matches amino acid i at position j in any amino acid sequence, and 0 for a mismatched element;
(E3) The regression equation forming unit performs a correlation analysis between the intermolecular energy parameter calculated in step (b3) and the score PSS,

Obtaining a regression equation expressed as [where a and b are constants determined by correlation analysis];
(F3) In the matrix conversion unit, the regression matrix is used to calculate the score matrix PSS _ij based on the amino acid appearance frequency,

Where a and b are the same constants as in the regression equation of step (e3) and n is the number of amino acids to be determined matrix based on amino acid position dependent intermolecular energy parameters converted by Converting to PSG _ij ;
(G3) In the amino acid position-dependent energy calculation unit, from the matrix PSG _ij based on the amino acid position-dependent intermolecular energy parameter,
[Formula 13]
[Formula 11]

[In the formula, n represents the number of amino acids to be determined, and C _ij is a 20 × n matrix, and is composed of 0 or 1 values. In addition, an element that matches amino acid i at position j in an arbitrary amino acid sequence is 1 and an element that does not match is 0. Calculating an amino acid position-dependent intermolecular energy parameter PSG value calculated by PSG _ij indicates an element of ij in the PSG matrix;
(H3) A program for causing a computer to execute the step of extracting an amino acid sequence having an amino acid position-dependent intermolecular energy parameter PSG value of −8.83 or less in an amino acid sequence second search unit. The following steps (a1) to (f1):
(A1) receiving a sequence data input of the target amino acid sequence of the target protein in the data input unit;
(B1) In the data editing unit, the target amino acid sequence is expressed as follows according to one or more predetermined amino acid indices:
[Formula 1]

[Wherein w is an odd number from 1 to 13, s is an integer part of w / 2, j is in the range of s to (ns-1), n is the target sequence length, T _i is a profile waveform obtained by applying an arbitrary amino acid index to the target amino acid sequence], and converting to one or more moving average profile waveforms x _j converted by
(C1) A complementary average profile waveform having a negative correlation with a moving average profile waveform that has been passed through a low frequency by moving average with respect to a profile waveform in which an arbitrary amino acid index is applied to a target amino acid sequence in a complementary amino acid sequence candidate generation unit An amino acid sequence (complementary amino acid sequence) candidate characterized by having a profile waveform that approximates
[Expression 15]
[Formula 2]

[Wherein w is an odd number from 1 to 13, s is an integer part of w / 2, j is in the range of s to (ns-1), n is the target sequence length, C _i is a profile waveform obtained by fitting one or more amino acid indices identical to those in step (b1) to complementary amino acid sequence candidates] to convert to one or more complementary moving average profile waveforms y _j converted by
(D1) Complement degree calculation unit,
[Expression 16]
[Formula 3]

Where s is an integer part of w / 2, j is in the range of s to (ns-1), and n is the target sequence length. Calculating a complementarity parameter R;
(E1) In the complementary amino acid sequence candidate storage unit, storing the complementary amino acid sequence candidate together with the complementarity parameter R in the storage means;
(F1) The complementary amino acid sequence search unit extracts a complementary amino acid sequence having a complementarity parameter R less than −0.9 based on the information stored in step (e1), and / or the following steps ( a2) to (b2):
(A2) selecting an interaction region that has already been identified to interact in a protein that is known to interact with the target site of the target protein in the interaction region identification unit;
(B2) The step of extracting an amino acid sequence consisting of 3 to 7 amino acid residues from the interaction region in the amino acid sequence first search unit is performed between step (a3) and step (b3). The program according to claim 7 for further execution.   The amino acid index is selected from an index based on hydrophobicity, an index based on electrical characteristics, an index representing ease of taking α-helix and β-sheet, and an index representing the relative size of side chain volume The program according to claim 8, which is the above index.   For a predetermined number of complementary amino acid sequences extracted in steps (a1) to (f1) using one or more predetermined amino acid indexes, steps (b1) to The program according to claim 8 or 9, wherein the candidate of complementary amino acid sequence extracted as a physiologically active peptide is further narrowed down by repeating (f1) one or more times. (I) a step of calculating an intermolecular energy parameter with the target site of the target protein for the extracted amino acid sequence in the intermolecular energy second calculation unit;
(Ii) in the amino acid sequence storage unit, storing the amino acid sequence together with the intermolecular energy parameter in a storage means;
(Iii) in the amino acid sequence third search unit, based on the information stored in step (ii), extracting an amino acid sequence having an amino acid position-dependent intermolecular energy parameter PSG value of −12.0 or less;
(Iv) In the amino acid sequence display unit, the computer further executes a step of displaying the amino acid sequence extracted in step (iii) as a candidate for a bioactive peptide. Program described in the section. Furthermore, between step (iii) and step (iv),
(I) generating an amino acid sequence in which an amino acid mutation is introduced into the amino acid sequence extracted in step (iii);
(II) calculating an intermolecular energy parameter between the amino acid sequence generated in step (I) and the target site of the target protein;
(III) The intermolecular energy parameter calculated in step (II) is compared by comparing the intermolecular energy parameter between the amino acid sequence extracted in step (iii) and the target site of the target protein. And extracting an amino acid sequence having an intermolecular energy parameter that is more stable than the parameter.   The computer-readable recording medium which recorded the program of any one of Claims 7-12. A device for designing a physiologically active peptide capable of interacting with a target protein, comprising (a) an amino acid sequence first search unit, (b) a first intermolecular energy calculation unit, and (c) a score. A matrix generation unit; (d) 3) a score calculation unit; (e) 3) a regression equation formation unit; (f) a matrix conversion unit; (d) 3) an amino acid position-dependent energy calculation unit; Amino acid sequence second search unit, (Li 3) intermolecular energy second calculation unit, (nu 3) amino acid sequence storage unit, (le 3) amino acid sequence third search unit, and (e 3) amino acid sequence display With
The amino acid sequence first search unit (a3) includes means for comprehensively generating an amino acid sequence consisting of 2 to 10 amino acid residues, selecting an amino acid sequence randomly from among them, and extracting it as an analysis library The intermolecular energy first calculation unit includes (b3) means for calculating an intermolecular energy parameter between each amino acid sequence extracted as the analysis library and the target site of the target protein,
The score matrix generation unit uses the intermolecular energy parameter calculated in (c3) step (b3),
[Expression 17]
[Formula 9]

[ _Where a _ij represents the number of occurrences of amino acid i at position j in all peptide groups included in the library for analysis, and b _ij represents position j in peptide groups equal to or less than the threshold ΔG included in the library for analysis. Means for generating a score matrix PSS _ij based on the frequency of occurrence of amino acids generated by
The score calculation unit uses (d3) score matrix PSS _ij based on the appearance frequency of amino acids,
[Formula 18]
[Formula 10]

[In the formula, n represents the number of amino acids to be determined, and C _ij is a 20 × n matrix, and is composed of 0 or 1 values. An element that matches amino acid i at position j in any amino acid sequence is 1 and an element that does not match is 0.
The regression equation forming unit (e3) performs a correlation analysis between the intermolecular energy parameter calculated in step (b3) and the score PSS,

[Wherein, a and b are constants determined by correlation analysis], and the matrix conversion unit (f3) uses the regression expression to determine the frequency of occurrence of amino acids. Score matrix PSS _ij

Where a and b are the same constants as in the regression equation of step (e3) and n is the number of amino acids to be determined Means for converting to PSG _ij ,
The amino acid position-dependent energy calculation unit (g3) from the matrix PSG _ij based on the amino acid position-dependent intermolecular energy parameter,
[Expression 21]
[Formula 11]

[In the formula, n represents the number of amino acids to be determined, and C _ij is a 20 × n matrix, and is composed of 0 or 1 values. In addition, an element that matches amino acid i at position j in an arbitrary amino acid sequence is 1 and an element that does not match is 0. PSG _ij indicates an element of ij in the PSG matrix] Means for calculating the interstitial energy parameter PSG value,
The amino acid sequence second search unit includes (h3) means for extracting an amino acid sequence having an amino acid position-dependent intermolecular energy parameter PSG value of −8.83 or less,
The intermolecular energy second calculation unit includes (i) a means for calculating an intermolecular energy parameter with the target site of the target protein for the extracted amino acid sequence,
The amino acid sequence storage unit includes (ii) means for storing the amino acid sequence together with the intermolecular energy parameter;
The amino acid sequence third search part includes (iii) means for extracting a predetermined number of amino acid sequences based on the information stored in step (ii),
The amino acid sequence display unit includes: (iv) means for displaying the amino acid sequence extracted in step (iii) as a candidate for a bioactive peptide. The following configuration 1:
(B) a data input unit; (b) a data editing unit; (c) a complementary amino acid sequence candidate generation unit; (d) a complementation degree calculation unit; (e) a complementary amino acid sequence candidate storage unit; Complementary amino acid sequence search part and / or the following configuration 2:
(B) The interaction region specifying part, (b) The mutual region amino acid sequence first search part, (b) The amino acid sequence first search part, and (b) The intermolecular energy first calculation part. 15. The apparatus of claim 14, further comprising:
In the configuration 1,
The data input unit includes (a1) means for receiving sequence data input of a target amino acid sequence,
The data editing unit (b1) determines the target amino acid sequence according to a predetermined amino acid index of 1 or more as follows:
[Formula 1]

[Wherein w is an odd number from 1 to 13, s is an integer part of w / 2, j is in the range of s to (ns−s−1), n is the target sequence length, T _i is a profile waveform obtained by applying an arbitrary amino acid index to the target amino acid sequence], and includes means for converting into one or more moving average profile waveforms x _j converted by
The complementary amino acid sequence candidate generation unit (c1) is a complementary average profile having a negative correlation with a moving average profile waveform that has been passed through a low frequency by moving average with respect to a profile waveform in which an arbitrary amino acid index is applied to a target amino acid sequence Generate an amino acid sequence candidate (complementary amino acid sequence) characterized by having a profile waveform that approximates the waveform,
[Expression 23]
[Formula 2]

[Wherein w is an odd number from 1 to 13, s is an integer part of w / 2, j is in the range of s to (ns−s−1), n is the target sequence length, C _i is a profile waveform obtained by applying one or more amino acid indices identical to those in step (b1) to complementary amino acid sequence candidates], and includes means for converting into one or more complementary moving average profile waveforms y _j converted by
The degree of complementation calculation part is (d1)
[Expression 24]
[Formula 3]

Where s is the integer part of w / 2, j is in the range of s to (ns-1), and n is the target sequence length. Means for calculating a complementarity parameter R;
The complementary amino acid sequence candidate storage unit includes (e1) means for storing a complementary amino acid sequence candidate together with the complementation degree parameter,
The complementary amino acid sequence search unit includes: (f1) means for extracting a complementary amino acid sequence having a complementarity parameter R of less than −0.9 based on the information stored in the means (e1),
In the configuration 2,
The interaction region specifying part includes (a2) means for selecting an interaction region that has already been specified to interact in a protein molecule that is known to interact with a target site of a target protein,
The amino acid sequence first search unit includes (b2) means for extracting an amino acid sequence consisting of 3 to 7 amino acid residues from the interaction region,
The amino acid sequences extracted by the means (f1) and / or (b2) are processed by the intermolecular energy second calculation unit, the amino acid sequence storage unit, the amino acid sequence third search unit, and the amino acid sequence display unit. Equipment.

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