JP2007265268A - Program, device and method for predicting antigenic determinant - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To shorten a time for creating an antibody and to reduce cost of the antibody by improving the prediction accuracy of an antigenic determinant. <P>SOLUTION: Graph is displayed in an antigenic determinant prediction device. A horizontal axis shows sequence numbers j of amino acid residue. A vertical axis shows standardized predicted values. A waveform W1 is a waveform of a predicted surface abundance ratio values Pbj, and a waveform W2 is a waveform of complex predicted values Pabj between a secondary structure and a surface abundance ratio. On the graph, primary structure sequence data AS1, secondary structure sequence data AS2 and surface abundance ratio sequence data BS are displayed in association with the sequence numbers j. When a threshold of an antigenic determinant is defined as 1.0, an antigenic determinant is not determined in the waveform W1. Meanwhile in the waveform W2, since complex predicted value Pab(100) of amino acid residue of sequence numbers j=100 is Pab(100)=1.0, the amino acid residue of sequence numbers j=100 is determined as an antigenic determinant. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an antigenic determinant prediction program, an antigenic determinant prediction device, and an antigenic determinant prediction method for predicting an antigenic determinant that is a partial structure of a protein recognized by an antibody.

  In order to produce an antibody against an antigen such as a pathogen, an antibody is usually produced by injecting a partial amino acid of the antigen into a small animal such as a rabbit. However, small animals often do not produce antibodies even when random partial amino acids are injected.

  A partial sequence capable of producing an antibody is called an antigenic determinant, and is a partial structure recognized by an antibody in a protein that is an antigen. It is important to accurately find this antigenic determinant. When an antigenic determinant is easily specified, a partial amino acid sequence that is much easier to produce an antibody can be found than a partially extracted partial amino acid sequence, which facilitates antibody production.

  Thus, in order to facilitate antibody production, an antigenic determinant prediction system capable of reliably predicting an antigenic determinant from a primary sequence of amino acid residues representing a protein has been proposed (for example, the following patent document) 1).

Japanese Patent Laid-Open No. 9-171017

  In order to facilitate antibody production, it is necessary to improve the accuracy of antigenic determinant prediction. By using the tertiary structure of the protein, that is, the three-dimensional structure, it is possible to improve the accuracy of antigenic determinant prediction. However, in the antigenic determinant prediction using a tertiary structure, the amount of data becomes enormous, so that the calculation process takes time, the preparation time of reagents such as antibiotics is prolonged, and the reagents are expensive. is there.

  In addition, antibodies tend to bind to easily attacked parts with soft structures such as turns and coils, rather than those with hard structures such as α-helices and β-strands of antigenic proteins. . There are also various secondary structure prediction methods (algorithms) representing α-helix, β-strand, turn, and coil.

  However, there is a problem that the secondary structure predicted by the prediction method differs, and the accuracy of antigenic determinant prediction varies depending on the employed prediction method. In addition, since data (symbols) representing the secondary structure is different for each prediction method, there is a problem that data representing secondary structures obtained from a plurality of prediction methods cannot be collected.

  In addition, the antibody tends to easily bind to a portion on the surface of a protein serving as an antigen. There are also various methods (algorithms) for predicting the surface abundance of amino acid residues. However, the surface presence rate predicted by the prediction method varies, and there is a problem that the accuracy of antigenic determinant prediction varies depending on the employed prediction method.

  In addition, since the data (symbols) representing the surface presence rate are different for each prediction method, there is a problem that data representing the surface presence rate obtained from a plurality of prediction methods cannot be collected.

  In addition, the antibody tends to easily bind to a more hydrophilic part of the protein serving as an antigen. However, since there is no prediction method (algorithm) for predicting the hydrophilicity of a protein, there is a problem that hydrophilicity cannot be taken into account for antigenic determinant prediction.

  In addition, since the secondary structure and surface abundance prediction methods differ in the secondary structure and surface abundance prediction methods, it is possible to perform antigenic determinant prediction that takes into account the secondary structure and surface abundance. There was a problem that I could not. Similarly, since hydrophilicity is not taken into account, there is a problem that it is impossible to perform antigenic determinant prediction with further added hydrophilicity.

  In order to solve the above-described problems caused by the prior art, the present invention improves the prediction accuracy of antigenic determinants, thereby reducing the production time and cost of the antibody, and the antigenic determinant prediction program. It is an object of the present invention to provide a group prediction apparatus and an antigenic determinant prediction method.

  In order to solve the above-described problems and achieve the object, an antigenic determinant prediction program, an antigenic determinant prediction device, and an antigenic determinant prediction method according to the first invention include a secondary structure of an amino acid sequence representing an arbitrary protein. A plurality of secondary structure sequence data relating to the secondary structure data of the same amino acid residue selected from the obtained secondary structure sequence data, the secondary representing the hardness of the amino acid residue Converted to a structure index value, based on each converted secondary structure index value, to calculate a predicted value (secondary structure predicted value) regarding the hardness of the amino acid residue, based on the calculated predicted value, The amino acid residue is determined as an antigenic determinant, and the determined determination result is output.

  According to the present invention, an easily attackable portion having a soft structure such as a turn or a coil comprehensively predicted by a plurality of secondary structure prediction methods can be predicted as an antigenic determinant.

  Further, in the first invention, a plurality of surface abundance ratio data relating to the surface abundance ratio of the amino acid sequence is obtained, and the surface presence of the amino acid residue selected from each of the obtained surface abundance arrangement data Rate data is converted into a surface presence index value representing the surface presence rate of the amino acid residue, and a predicted value related to the surface presence rate of the amino acid residue is calculated based on each converted surface presence index value. The calculated predicted value is normalized to the same standard, and based on each normalized predicted value, a composite predicted value related to the secondary structure and the surface presence rate is calculated, and based on the calculated composite predicted value The amino acid residue may be determined as an antigenic determinant.

  According to the present invention, it is an easy-to-attack part having a soft structure such as a turn or a coil that is comprehensively predicted by a plurality of secondary structure prediction methods, and a plurality of surface presence rate prediction methods. Thus, the surface of the protein predicted comprehensively can be predicted as an antigenic determinant.

  Further, in the first invention, primary structure sequence data for obtaining the amino acid residue selected from the primary structure sequence data obtained by acquiring primary structure sequence data of an amino acid sequence representing the protein, Conversion to a hydrophobicity index value representing the hydrophobicity of an amino acid residue, and based on the converted hydrophobicity index value, the predicted value for the hydrophobicity of the amino acid residue is the same standard as the normalized predicted value The calculated predicted value is converted into a predicted value related to the hydrophilicity of the amino acid residue, and the secondary value is calculated based on the normalized predicted value and the predicted value converted to hydrophobic / hydrophilicity. A composite prediction value regarding the structure and the hydrophilicity may be calculated, and the amino acid residue may be determined as an antigenic determinant based on the calculated composite prediction value.

  According to the present invention, a highly attackable portion having a soft structure such as a turn or a coil comprehensively predicted by a plurality of secondary structure prediction methods and having a high hydrophilicity is designated as an antigen. Can be predicted by determinants.

  Further, in the first invention, primary structure sequence data for obtaining the amino acid residue selected from the primary structure sequence data obtained by acquiring primary structure sequence data of an amino acid sequence representing the protein, Converted to a hydrophobicity index value representing the hydrophobicity of an amino acid residue, and based on the converted hydrophobicity index value, a predicted value related to the hydrophobicity of the amino acid residue is predicted by the normalization step The calculated predicted value is converted into a predicted value related to the hydrophilicity of the amino acid residue, and is based on the normalized predicted value and the predicted value converted to hydrophobic / hydrophilicity. Calculating a composite prediction value related to the secondary structure, the surface abundance ratio and the hydrophilicity, and determining the amino acid residue as an antigenic determinant based on the calculated composite prediction value It may be.

  According to the present invention, it is an easy-to-attack part having a soft structure such as a turn or a coil that is comprehensively predicted by a plurality of secondary structure prediction methods, and a plurality of surface presence rate prediction methods. Thus, it is possible to predict an antigenic determinant that is a protein surface that is comprehensively predicted by the above-described method and that is highly hydrophilic.

  Moreover, the antigen determinant prediction program, the antigen determinant prediction apparatus, and the antigen determinant prediction method according to the second invention acquire a plurality of surface abundance sequence data relating to the surface abundance of an amino acid sequence representing an arbitrary protein. The surface presence rate data of the same amino acid residue selected from the obtained surface presence rate sequence data is converted into a surface presence rate index value representing the surface presence rate of the amino acid residue, and converted. Based on each surface abundance index value, a predicted value related to the surface abundance of the amino acid residue is calculated, and based on the calculated predicted value, the amino acid residue is determined as an antigenic determinant, and the determined decision The result is output.

  According to this invention, the surface of the protein comprehensively predicted by a plurality of methods for predicting the surface abundance can be predicted as an antigenic determinant.

  Further, in the second invention, primary structure sequence data for acquiring the primary structure sequence data of the amino acid sequence representing the protein and identifying the amino acid residue selected from the acquired primary structure sequence data, Converted to a hydrophobicity index value representing the hydrophobicity of an amino acid residue, and based on the converted hydrophobicity index value, a predicted value related to the hydrophobicity of the amino acid residue is predicted by the normalization step The calculated predicted value is converted into a predicted value related to the hydrophilicity of the amino acid residue, and is based on the normalized predicted value and the predicted value converted to hydrophobic / hydrophilicity. Then, a composite prediction value related to the surface presence rate and the hydrophilicity may be calculated, and the amino acid residue may be determined as an antigenic determinant based on the calculated composite prediction value.

  According to the present invention, it is possible to predict, as an antigenic determinant, a protein surface that is comprehensively predicted by a plurality of surface abundance prediction methods and has a high hydrophilicity.

  Moreover, the antigen determinant prediction program, the antigen determinant prediction device, and the antigen determinant prediction method according to the third invention acquire primary structure sequence data of an amino acid sequence representing an arbitrary protein, and the acquired primary structure sequence Primary structure data specifying any amino acid residue selected from the data is converted into a hydrophobicity index value representing the hydrophobicity of the amino acid residue, and the amino acid is converted based on the converted hydrophobicity index value. A predicted value related to the hydrophobicity of the residue is calculated, the calculated predicted value is converted into a predicted value related to the hydrophilicity of the amino acid residue, and the amino acid residue is converted into an antigenic determinant based on the converted predicted value. And the determined decision result is output.

  According to this invention, it is possible to predict a highly hydrophilic portion as an antigenic determinant by utilizing the tendency that an antibody easily binds to a more hydrophilic portion in a protein serving as an antigen.

  According to the antigenic determinant prediction program, the antigenic determinant prediction device, and the antigenic determinant prediction method according to the present invention, it is possible to improve the prediction accuracy of the antigenic determinant, thereby shortening the production time and cost of the antibody. There is an effect that can be.

  Exemplary embodiments of an antigenic determinant prediction program, a recording medium recording the program, an antigenic determinant prediction device, and an antigenic determinant prediction method according to the present invention will be described below in detail with reference to the accompanying drawings. .

(Hardware configuration of antigenic determinant prediction device)
First, the hardware configuration of the antigen determinant prediction device according to the embodiment of the present invention will be described. FIG. 1 is a block diagram showing a hardware configuration of an antigenic determinant prediction device according to an embodiment of the present invention.

  In FIG. 1, an antigenic determinant prediction apparatus includes a CPU 101, a ROM 102, a RAM 103, an HDD (hard disk drive) 104, an HD (hard disk) 105, an FDD (flexible disk drive) 106, and a removable recording medium. As an example, an FD (flexible disk) 107, a display 108, an I / F (interface) 109, a keyboard 110, a mouse 111, a scanner 112, and a printer 113 are provided. Each component is connected by a bus 100.

  Here, the CPU 101 governs overall control of the antigen determinant prediction device. The ROM 102 stores a program such as a boot program. The RAM 103 is used as a work area for the CPU 101. The HDD 104 controls reading / writing of data with respect to the HD 105 according to the control of the CPU 101. The HD 105 stores data written under the control of the HDD 104.

  The FDD 106 controls reading / writing of data with respect to the FD 107 according to the control of the CPU 101. The FD 107 stores data written under the control of the FDD 106, or causes the antigen determinant prediction device to read the data stored in the FD 107.

  In addition to the FD 107, the removable recording medium may be a CD-ROM (CD-R, CD-RW), MO, DVD (Digital Versatile Disk), memory card, or the like. The display 108 displays data such as a document, an image, and function information as well as a cursor, an icon, or a tool box. As this display 108, for example, a CRT, a TFT liquid crystal display, a plasma display, or the like can be adopted.

  The I / F 109 is connected to a network 114 such as the Internet through a communication line, and is connected to other devices via the network 114. The I / F 109 controls an internal interface with the network 114 and controls data input / output from an external device. For example, a modem or a LAN adapter may be employed as the I / F 109.

  The keyboard 110 includes keys for inputting characters, numbers, various instructions, and the like, and inputs data. Moreover, a touch panel type input pad or a numeric keypad may be used. The mouse 111 performs cursor movement, range selection, window movement, size change, and the like. A trackball or a joystick may be used as long as they have the same function as a pointing device.

  The scanner 112 optically reads an image and takes in the image data into the antigen determinant prediction device. The scanner 112 may have an OCR function. The printer 113 prints image data and document data. For example, a laser printer or an ink jet printer can be employed as the printer 113.

(Functional configuration of antigenic determinant prediction device)
Next, a functional configuration of the antigen determinant prediction device according to the embodiment of the present invention will be described. FIG. 2 is a block diagram showing a functional configuration of the antigenic determinant prediction device according to the embodiment of the present invention. In FIG. 2, the antigen determinant prediction device 200 includes an acquisition unit 201, an index value conversion unit 202, a prediction value calculation unit 203, a normalization unit 204, a hydrophobicity / hydrophilicity conversion unit 205, and a composite prediction value. The calculation unit 206 includes a contribution rate calculation unit 207, a determination unit 208, and an output unit 209.

  First, the acquisition part 201 acquires the data regarding protein. Specifically, for example, primary structure sequence data, secondary structure sequence data, and surface abundance sequence data of an amino acid sequence representing an arbitrary protein are acquired. Here, each data will be described.

  FIG. 3 is an explanatory diagram showing an example of primary structure sequence data of amino acid sequences. The primary structure sequence data AS1 is character string data representing the primary structure of an amino acid sequence, and symbols (S, Y, I, J, etc.) relating to the primary structure of 20 types of amino acid residues are represented by SEQ ID NO: j (j = 1). N) Data arranged in order n.

  Here, the amino acid residue of SEQ ID NO: j is denoted as Aj. In FIG. 3, for example, the primary structure data of amino acid residue A1 is “S”. Further, names corresponding to the symbols of amino acid residues are as shown in FIG.

  FIG. 4 is an explanatory diagram showing an example of secondary structure array data. The secondary structure sequence data AS2 is character string data representing the secondary structure of the amino acid sequence, such as a hard structure such as α-helix or β-strand of a protein that is an antigen, or a soft structure such as a turn or coil. This is data in which character data (secondary structure data) representing various structural features are arranged in the order of array element number j (j = 1 to n).

  In FIG. 4, two types of secondary structure array data AS2 (first secondary structure array data AS2-1 and second secondary structure array data AS2-2) are prepared. The first secondary structure array data AS2-1 and the second secondary structure array data AS2-2 are secondary structure data having different prediction accuracy or prediction algorithm.

  In FIG. 4, the symbol “H” represents an α helix, and “E” represents a β strand. Further, in the first secondary structure array data AS2-1, the blank portion represents a structure other than the α helix and the β strand. Further, “.” Is a symbol unique to the second secondary structure array data AS2-2, and represents unpredictability. “L” is a symbol unique to the second secondary structure array data AS2-2, and represents a loop (turn or coil).

  For example, the secondary structure data (first secondary structure data) of amino acid residue A20 (“P”) in the first secondary structure sequence data AS2-1 is predicted to be “H”, that is, an α helix. However, the secondary structure data (second secondary structure data) of amino acid residue A20 (“P”) in the second secondary structure sequence data AS2-2 is predicted to be “L”, that is, a loop. Yes.

  As a method for obtaining the secondary structure array data AS2, the secondary structure array data AS2 may be given to the antigenic determinant prediction device 200 by a user operation. When the primary structure array data AS1 is given, the secondary structure array data AS1 is given. The primary structure array data AS1 may be transmitted to an external server that calculates the data AS2, and the secondary structure array data AS2 that is the calculation result may be received.

  FIG. 5 is an explanatory diagram showing an example of surface presence rate array data. The surface abundance ratio is the probability that the amino acid residues of the amino acid sequence constituting the protein are present on the surface of the protein, and the surface abundance data is data representing the surface abundance ratio in characters. The array data BS is data in which surface presence rate data is arrayed in the order of array number j (j = 1 to n).

  In FIG. 5, two types of surface presence rate array data BS (first surface presence rate array data BS-1 and second surface presence rate array data BS-2) are prepared. The first surface presence rate array data BS-1 and the second surface presence rate array data BS-2 are surface presence rate data having different prediction accuracy or prediction algorithm.

  In FIG. 5, the symbol “e” represents the surface of the protein, and “b” represents the inside of the protein. Further, in the first surface presence rate array data BS-1, a blank portion represents an unpredictable state. Further, “.” Is a symbol unique to the second surface presence rate array data BS-2, and represents unpredictability. Similarly, “i” is a symbol unique to the second surface presence rate array data BS-2 and represents an intermediate position between “e” and “b”.

  For example, in the first surface abundance sequence data BS-1, the surface abundance data (first surface abundance data) of amino acid residue A15 is predicted to be “e”, that is, the surface. In the surface presence rate array data BS-2 (second surface presence rate data), “.”, Ie, unpredictable.

  As a method for obtaining the surface abundance array data BS, the surface abundance array data BS may be given to the antigenic determinant prediction device 200 by a user operation. When the primary structure array data AS1 is given, the surface abundance array data BS1 is given. The primary structure array data AS1 may be transmitted to an external server that calculates the data BS, and the surface presence ratio array data BS that is the calculation result may be received.

  In FIG. 2, the index value conversion unit 202 converts the symbols in the data acquired by the acquisition unit 201 into numerical values by converting them into index values. Specifically, for example, when the secondary structure array data AS2 is acquired, each secondary structure data in the secondary structure array data AS2 is converted into a secondary structure index value representing the hardness of the secondary structure. It is digitized.

  Similarly, when the surface presence rate sequence data BS is acquired, the surface presence rate sequence data BS is converted into a numerical value by converting the surface presence rate sequence data BS into a surface presence rate index value representing the surface presence rate of amino acid residues. Specifically, for example, when the surface presence rate array data BS is acquired, each surface presence rate data in the surface presence rate array data BS is converted into a secondary structure index value representing the surface presence rate. Digitize.

  When the primary structure sequence data AS1 is acquired, the primary structure sequence data AS1 is converted into a numerical value by converting the primary structure sequence data AS1 into a hydrophobicity index value representing the hydrophobicity of the amino acid residues constituting the amino acid sequence.

  FIG. 6 is an explanatory diagram showing a conversion table for the first secondary structure data. In the conversion table 600 shown in FIG. 6, a secondary structure index value is assigned to each secondary structure type. In FIG. 6, there are two types of secondary structure index values, “0.0” and “1.0”. That is, in the numerical range of 0.0 to 1.0, “0.0” is the hardest and “1.0” is the softest.

  FIG. 7 is an explanatory diagram showing a conversion table for the second secondary structure data. Also in the conversion table 700 shown in FIG. 7, the secondary structure index value is assigned to each secondary structure type as in FIG. In FIG. 7, there are three types of secondary structure index values: “0.0”, “0.5”, and “1.0”. For an unpredictable value, an intermediate value “0.5” is assigned. That is, in the numerical range of 0.0 to 1.0, “0.0” is the hardest and “1.0” is the softest.

  FIG. 8 is an explanatory diagram showing a conversion table for the first surface presence rate data. In the conversion table 800 shown in FIG. 8, a surface presence rate index value is assigned to each amino acid residue position in the protein. In FIG. 8, there are two types of surface presence rate index values, “0.0” and “1.0”. That is, in the numerical value range 0.0 to 1.0, “0.0” is located most inside, and “1.0” is located on the surface.

  FIG. 9 is an explanatory diagram showing a conversion table for the second surface presence rate data. Also in the conversion table 900 shown in FIG. 9, the surface presence rate index value is assigned to each position as in FIG. In FIG. 9, there are four types of surface presence rate index values of “0.0”, “0.3”, “0.5”, and “1.0”.

  “0.3” is assigned to an intermediate position “i” between the interior “b” and the surface “e”. For unpredictability, an intermediate value “0.5” is assigned. That is, in the numerical value range 0.0 to 1.0, “0.0” is located most inside, and “1.0” is located on the surface.

  FIG. 10 is an explanatory diagram showing a conversion table for primary structure data. In the conversion table 1000 shown in FIG. 10, a hydrophobicity index value is assigned to each amino acid residue. In FIG. 10, the numerical value range of the hydrophobicity index value takes a value of −4.5 to 4.5. The height of the numerical value indicates hydrophobicity.

  In FIG. 2, the predicted value calculation unit 203 calculates a predicted value based on the index value converted by the index value conversion unit 202. Specifically, when the secondary structure data in the obtained plurality of secondary structure sequence data AS2 is converted into secondary structure index values, each secondary structure index of amino acid residue Aj of the same sequence number j The secondary structure predicted value is calculated by integrating the values.

  For example, since the amino acid residue Aj (“S” in FIG. 4) is predicted to be “E”, that is, β strand, according to the first secondary structure data, the conversion table 600 of FIG. Is converted into a secondary structure index value “0.0”.

  Similarly, according to the second secondary structure data, since it is predicted to be “L”, that is, a loop, it is converted to the secondary structure index value “1.0” by the conversion table 700 of FIG. Therefore, the numerical value “1.0” obtained by integrating the secondary structure index values “0.0” and “1.0” is the predicted secondary structure value for the amino acid residue “S” of SEQ ID NO: j.

  In addition, the predicted value calculation unit 203, specifically, when the surface presence rate data in the obtained plurality of surface presence rate array data BS is converted into the surface presence rate index value, the amino acid of the same sequence number j A surface presence rate prediction value is calculated by integrating each surface presence rate index value of the residue Aj.

  For example, since the amino acid residue Aj (“S” in FIG. 5) is predicted to be “e”, that is, the surface according to the first surface presence rate data, the conversion table 800 of FIG. The surface presence rate index value is converted to “1.0”.

  Similarly, according to the second surface presence rate data, “.”, Ie, unpredictable, is converted to the surface presence rate index value “0.5” by the conversion table 900 of FIG. Accordingly, the numerical value “1.5” obtained by integrating the surface presence rate index values “1.0” and “0.5” is the predicted value of the surface presence rate at the amino acid residue Aj (“S” in the case of FIG. 5). It becomes.

  Further, the predicted value calculation unit 203, specifically, when the obtained primary structure sequence data AS1 is converted into a hydrophobic index value, the hydrophobic index value of the amino acid residue Aj of interest and the surrounding amino acids Based on the hydrophobicity index value of the residue, the predicted hydrophobicity value of the focused amino acid residue Aj is calculated. Here, the sequence number j is determined by the following formula (1).

j = (m + 1) / 2 (1)

  Here, m is the number of amino acid residues centered on the amino acid residue Aj. For example, when m = 7, amino acid residue A4 of SEQ ID NO: 4 is the center, and the total of the hydrophobicity index values of amino acid residues A1 to A7 of SEQ ID NO: 1 to 7 is the hydrophobicity prediction of amino acid residue A4 Value. That is, the formula for calculating the predicted hydrophobicity of amino acid residue Aj is as shown in the following formula (2).

  In the above formula (2), Scj is the hydrophobicity prediction value of amino acid residue Aj, and σj is the hydrophobicity index value of the amino acid residue of SEQ ID NO: j ((m−1) / 2 ≦ j ≦ m). .

  Further, the normalization unit 204 normalizes the prediction value calculated by the prediction value calculation unit 203. The normalization unit 204 is a process performed to unify the same scale when using a plurality of types of data, for example, the secondary structure array data AS2 and the surface presence ratio array data BS. In addition, the predicted hydrophobicity is standardized alone.

  For example, regarding the secondary structure sequence data AS2, when the secondary structure predicted value of the amino acid residue Aj is Saj (j = 1 to n), the normalization parameter Fa may be expressed by the following equation (3). it can. Sa (max) is the maximum value of the secondary structure predicted value Saj, and Sa (min) is the minimum value of the secondary structure predicted value Saj.

Fa = 1.0 / {Sa (max) -Sa (min)} (3)

  And if the normalized secondary structure predicted value is Paj, the secondary structure predicted value Paj can be expressed by the following equation (4). By this equation (4), the secondary structure predicted value Saj can be normalized to a secondary structure predicted value Paj in which the numerical range is 0 ≦ Paj ≦ 1.

Paj = {Saj−Sa (min)} × Fa (4)

  Similarly, for example, regarding the surface presence rate sequence data BS, when the predicted surface presence rate of the amino acid residue Aj is Sbj (j = 1 to n), the normalization parameter Fb is expressed by the following equation (5). I can do it. Sb (max) is the maximum value of the predicted surface presence rate Sbj, and Sb (min) is the minimum value of the predicted surface presence rate Sbj.

Fb = 1.0 / {Sb (max) −Sb (min)} (5)

  If the normalized surface presence rate predicted value is Pbj, the surface presence rate predicted value Pbj can be expressed by the following equation (6). By this equation (6), the surface presence rate predicted value Sbj can be normalized to the surface presence rate predicted value Pbj in which the numerical range is 0 ≦ Pbj ≦ 1.

Pbj = {Sbj−Sb (min)} × Fb (6)

  For example, for the hydrophobicity prediction value Scj of the amino acid residue Aj, the normalization parameter Fc can be expressed by the following equation (7). Note that Sc (max) is the maximum value of the predicted hydrophobicity value Scj, and Sc (min) is the minimum value of the predicted hydrophobicity value Scj.

Fc = 1.0 / {Sc (max) -Sc (min)} (7)

  Then, assuming that the normalized hydrophobicity prediction value is Pcj, the hydrophobicity prediction value Pcj can be expressed by the following equation (8). By this equation (8), the hydrophobicity predicted value Scj can be normalized to the hydrophobicity predicted value Pcj where the numerical range is 0 ≦ Pcj ≦ 1.

Pcj = {Scj−Sc (min)} × Fc (8)

  In FIG. 2, the hydrophobicity / hydrophilicity conversion unit 205 converts the normalized hydrophobicity prediction value Pcj into a hydrophilicity prediction value Hj. The hydrophobicity predicted value Pcj is standardized as a numerical value range of 0 ≦ Pcj ≦ 1, and is thus converted into a standardized hydrophilic predicted value Hj by the following equation (9).

Hj = 1-Pcj (9)

  The composite prediction value calculation unit 206 calculates a composite prediction value from a plurality of types of standardized prediction values Paj, Pbj, and Hj for the amino acid residue Aj of the same sequence number j. Specifically, for example, when antigenic determinant prediction is performed by secondary structure prediction and surface presence rate prediction, the secondary structure prediction value Paj and the surface presence rate prediction value Pbj for the amino acid residue Aj of the same SEQ ID NO: j Is multiplied to calculate a composite prediction value of the secondary structure prediction value Paj and the surface presence rate prediction value Pbj.

  Similarly, when performing antigenic determinant prediction by secondary structure prediction (or surface presence rate prediction) and hydrophilicity prediction, secondary structure prediction value Paj (or surface presence rate) for amino acid residue Aj of the same SEQ ID NO: j By multiplying the predicted value Pbj) and the hydrophilicity predicted value Hj, a composite predicted value of the secondary structure predicted value Paj (or the surface presence rate predicted value Pbj) and the hydrophilicity predicted value Hj is calculated.

  Similarly, when performing antigenic determinant prediction by secondary structure prediction, surface presence rate prediction, and hydrophilicity prediction, secondary structure prediction value Paj, surface presence rate prediction value Pbj for amino acid residue Aj of the same sequence number j , And the hydrophilicity prediction value Hj, a composite prediction value of the secondary structure prediction value Paj, the surface presence rate prediction value Pbj, and the hydrophilicity prediction value Hj is calculated.

  Further, the contribution rate calculation unit 207 calculates the contribution rate of each prediction method (secondary structure prediction, surface presence rate prediction, and hydrophilicity prediction). Specifically, a weight value is set for each prediction method, and a contribution rate for each prediction method is calculated from the weight value. For example, when the weight value of secondary structure prediction is Wa, the weight value of surface presence rate is Wb, and the weight value of hydrophilicity prediction is Wc, the contribution rate CRx of a certain prediction method Wx (x = a, b, c) is The following equation (10) can be obtained.

CRx = [Wx / (Wa + Wb + Wc)] × 100 (10)

  The calculated contribution rate CRx is multiplied by each predicted value Paj, Pbj, Hj. Thereby, each prediction value Paj, Pbj, Hj can be adjusted according to the precision of a prediction method.

  Further, the determination unit 208 determines the amino acid residue Aj as an antigenic determinant based on the predicted values Paj, Pbj, and Hj. Specifically, when the antigenic determinant is predicted by the secondary structure sequence data AS2 alone, the amino acid residue Aj is determined as the antigenic determinant using the secondary structure predicted value Paj (or Saj).

  For example, a threshold value is set for the secondary structure predicted value Paj (or Saj), and if the secondary structure predicted value Paj (or Saj) is equal to or greater than the threshold value, amino acid residue Aj is used as an antigenic determinant. If it is determined and is less than the threshold, it is not determined as an antigenic determinant.

  Similarly, when an antigenic determinant is predicted by the surface presence rate array data BS alone, a threshold is set for the surface presence rate predicted value Pbj (or Sbj) in the same manner as described above, and the surface presence rate predicted value Pbj is set. If (or Sbj) is greater than or equal to the threshold value, amino acid residue Aj is determined as an antigenic determinant, and if it is less than the threshold value, it is not determined as an antigenic determinant.

  Similarly, when an antigenic determinant is predicted by hydrophilicity prediction, a threshold value is set for the hydrophilicity prediction value Hj in the same manner as described above, and if the hydrophilicity prediction value Hj is equal to or greater than the threshold value, an amino acid Residue Aj is determined as an antigenic determinant, and if it is less than the threshold, it is not determined as an antigenic determinant.

  The same applies to the composite prediction value calculated by the composite prediction value calculation unit 206. If the composite predicted value of amino acid residue Aj is greater than or equal to a threshold value, amino acid residue Aj is determined as an antigenic determinant, and if it is less than the threshold value, it is not determined as an antigenic determinant.

  The determination unit 208 may determine the antigen determinant based on a difference between an arbitrary predicted value and a composite predicted value of the predicted value and another predicted value. In this case, it is determined as an antigenic determinant by the influence of other predicted values.

  For example, when the combined predicted value of the secondary structure predicted value Paj and the surface presence rate predicted value Pbj is Pabj, if the difference Pabj−Pbj is equal to or greater than the threshold value, the amino acid residue Aj is determined as the antigenic determinant. If it is less than the threshold value, it is not determined as an antigenic determinant. Thereby, it can be seen that the amino acid residue Aj of SEQ ID NO: j was determined as an antigenic determinant by the influence of the secondary structure.

  The output unit 209 outputs the determination result determined by the determination unit 208. The determination result only needs to include at least information on the antigenic determinant. Therefore, the predicted values Paj, Pbj, Hj of the amino acid residues A1 to An of all the sequence numbers j (1 to n) may be output, or these combined predicted values may be output. In addition, the determination result may be list information of predicted values Paj, Pbj, Hj for each sequence number j of amino acid residue Aj, or may be displayed as a graph.

  FIG. 11 is a graph illustrating an example of output information. In the graph of FIG. 11, the horizontal axis indicates the amino acid residue sequence position, that is, SEQ ID NO: j. The vertical axis is a standardized predicted value. A waveform W1 is a waveform of the surface presence rate prediction value Pbj, and a waveform W2 is a waveform of a composite prediction value Pabj of the secondary structure and the surface presence rate.

  On the graph, the primary structure array data AS1, the secondary structure array data AS2, and the surface abundance array data BS are displayed in association with the array element number j.

  Here, for example, when the threshold value of the antigen determinant is 1.0, the antigen determinant is not determined in the waveform W1. On the other hand, for waveform W2, since the composite predicted value Pab [100] of the amino acid residue of SEQ ID NO: j = 100 is Pab [100] = 1.0, the amino acid residue of SEQ ID NO: j = 100 is the antigenic determinant. To be determined.

  Note that the acquisition unit 201, index value conversion unit 202, prediction value calculation unit 203, normalization unit 204, hydrophobicity / hydrophilicity conversion unit 205, composite prediction value calculation unit 206, contribution rate calculation unit 207, determination unit 208 described above. Specifically, for example, the output unit 209 causes the CPU 101 to execute a program recorded on a recording medium such as the ROM 102, the RAM 103, and the HD 105 shown in FIG. Realize the function.

(Antigen determinant prediction processing procedure)
Next, an antigenic determinant prediction processing procedure according to the embodiment of the present invention will be described. FIG. 12 is a flowchart showing an antigenic determinant prediction processing procedure (No. 1) according to the embodiment of the present invention. FIG. 12 shows an antigenic determinant prediction processing procedure when secondary structure prediction is used.

  First, the acquisition unit 201 waits for the primary structure array data AS1 to be input (step S1201: No), and when the primary structure array data AS1 is input (step S1201: Yes), a plurality of secondary structure array data. AS2 is acquired (step S1202). Then, the index value conversion process is performed by the index value conversion unit 202, whereby the secondary structure data in each secondary structure array data AS2 is converted into a secondary structure index value (step S1203).

  Then, the sequence number j is set to j = 1 (step S1204), and the secondary structure index values of the amino acid residues Aj are integrated (step S1205). This integrated value becomes the secondary structure predicted value Saj. If j = n is not satisfied (step S1206: NO), j is incremented (step S1207), and the process returns to step S1205.

  On the other hand, when j = n (step S1206: Yes), the determining unit 208 determines an antigenic determinant from the n amino acid residues A1 to An using the secondary structure predicted value Saj (step S1208). ). In this case, the normalization unit 204 may normalize the predicted value Saj.

  Thereafter, the determination result is output by the output unit 209 (step S1209). As a result, the series of processes is completed. According to this antigenic determinant prediction processing procedure, it is possible to predict the antigenic determinant with high accuracy by suppressing variations in different secondary structure data.

  FIG. 13: is a flowchart which shows the antigenic determinant prediction process procedure (the 2) concerning embodiment of this invention. FIG. 13 shows an antigenic determinant prediction processing procedure when surface presence rate prediction is used.

  First, the acquisition unit 201 waits for the primary structure array data AS1 to be input (step S1301: No), and when the primary structure array data AS1 is input (step S1301: Yes), a plurality of surface presence rate array data. A BS is acquired (step S1302). Then, by performing index value conversion processing by the index value conversion unit 202, the surface presence rate data in each surface presence rate array data BS is converted into a surface presence rate index value (step S1303).

  Then, the sequence number j is set to j = 1 (step S1304), and the surface presence rate index values of amino acid residues Aj are integrated (step S1305). This integrated value is the surface presence rate prediction value Sbj. If j = n is not satisfied (step S1306: NO), j is incremented (step S1307), and the process returns to step S1305.

  On the other hand, when j = n (step S1306: Yes), the determination unit 208 determines an antigenic determinant from n amino acid residues A1 to An using the surface presence rate predicted value Sbj (step S1308). ). In this case, the normalization unit 204 may normalize the predicted value Sbj.

  Thereafter, the determination result is output by the output unit 209 (step S1309). As a result, the series of processes is completed. According to this antigenic determinant prediction processing procedure, it is possible to predict the antigenic determinant with high accuracy by suppressing variations in different surface presence rate data.

  FIG. 14 is a flowchart showing an antigenic determinant prediction processing procedure (No. 3) according to the embodiment of the present invention. FIG. 14 shows an antigenic determinant prediction processing procedure when hydrophilicity prediction is used.

  First, the acquisition unit 201 waits for the primary structure array data AS1 to be input (step S1401: No). When the primary structure array data AS1 is input (step S1401: Yes), the index value conversion unit 202 sets the index. By performing value conversion processing, amino acid residues are converted into hydrophobicity index values (step S1402), and the sequence number j is set to j = (m + 1) / 2 (step S1403). Then, the predicted value calculation unit 203 calculates the predicted hydrophobicity value Scj of the amino acid residue Aj (step S1404).

  Next, the normalization unit 204 normalizes the hydrophobicity predicted value Scj to obtain the hydrophobicity predicted value Pcj (step S1405). Then, the normalized hydrophobicity predicted value Pcj is converted into the hydrophilicity predicted value Hj by the hydrophobicity / hydrophilicity conversion unit 205 (step S1406).

  Thereafter, it is determined whether or not the array element number j satisfies the following formula (10) (step S1407).

j = n− (m + 1) / 2 (10)

  If the above equation (10) is not satisfied (step S1407: No), j is incremented (step S1408), and the process returns to step S1404. On the other hand, when the above formula (10) is satisfied (step S1407: Yes), the determining unit 208 determines an amino acid residue that becomes an antigenic determinant (step S1409).

  Thereafter, the determination result is output by the output unit 209 (step S1410). As a result, the series of processes is completed. According to this antigenic determinant prediction processing procedure, antigenic determinant prediction taking into account the hydrophilicity of amino acid residue Aj can be performed with high accuracy by a new prediction method of hydrophilicity prediction.

  FIG. 15 is a flowchart showing an antigenic determinant prediction processing procedure (No. 4) according to the embodiment of the present invention. FIG. 15 shows an antigenic determinant prediction processing procedure when secondary structure prediction, surface presence rate prediction, and hydrophilicity prediction are used.

  First, the acquisition unit 201 waits for the primary structure array data AS1 to be input (step S1501: No). If the primary structure array data AS1 is input (step S1501: Yes), the secondary structure predicted value calculation process is performed. Is performed (step S1502). The secondary structure predicted value calculation process is a process of steps S1202 to S1207 shown in FIG. 12 and a normalization process of the secondary structure predicted value Saj by the normalization unit 204.

  Next, a surface presence rate predicted value calculation process is performed (step S1503). The surface presence rate predicted value calculation processing is the processing of steps S1302 to S1307 shown in FIG. 13 and the normalization processing of the surface presence rate predicted value Sbj by the normalization unit 204.

  Thereafter, a hydrophilicity predicted value calculation process is performed (step S1504). The hydrophilicity predicted value calculation process is the process of steps S1402 to S1408 shown in FIG. Then, the contribution rate of each prediction method is calculated by the contribution rate calculation unit 207 (step S1505). The calculation of the contribution rate is executed as necessary.

  Then, for each amino acid residue Aj, a composite predicted value is calculated by multiplying the secondary structure predicted value Paj, the surface presence rate predicted value Pbj, and the hydrophilicity predicted value Hj (step S1506). When the contribution rate calculation process (step S1505) is executed, when the contribution rate is calculated, each prediction value is multiplied by the contribution rate for each prediction method. For example, the secondary structure predicted value Paj is multiplied by the contribution rate Wa, the surface presence rate predicted value Pbj is multiplied by the contribution rate Wb, and the hydrophilicity predicted value Hj is multiplied by the contribution rate Wc. Then, for each amino acid residue Aj, by multiplying the secondary structure predicted value Paj after the contribution rate multiplication, the surface presence rate predicted value Pbj after the contribution rate multiplication, and the hydrophilicity predicted value Hj after the contribution rate multiplication, A composite prediction value is calculated.

  Then, the determination unit 208 performs antigen determinant determination processing (step S1506). Specifically, an amino acid residue that becomes an antigenic determinant is determined from amino acid residues A1 to An based on the composite prediction value. Thereafter, the determination result is output by the output unit 209 (step S1508). As a result, the series of processes is completed.

  According to this antigenic determinant prediction processing procedure, since it is a prediction method that takes into account the secondary structure, surface abundance and hydrophilicity, it has a soft amino acid sequence and is present on the surface of the protein. High amino acid residues can be predicted as antigenic determinants.

  In FIG. 15, the antigenic determinant is determined using all of the secondary structure prediction, the surface presence rate prediction, and the hydrophilicity prediction. However, the antigenic determinant is predicted using at least two of these prediction methods. It is good. In addition, any prediction method may be executed first in order of execution, or may be executed in parallel.

  For example, when secondary structure prediction and surface presence rate prediction are used, an antigenic determinant is predicted without executing the hydrophilicity predicted value calculation processing in step S1504. Thus, the prediction method takes into account the secondary structure and the surface abundance, and an amino acid residue having a soft structure and existing on the surface of the protein can be predicted as an antigenic determinant.

  As described above, according to the above-described embodiment, it is possible to shorten the antibody production time and the cost by improving the prediction accuracy of the antigenic determinant.

  The antigen determinant prediction method described in this embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. This program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, and a DVD, and is executed by being read from the recording medium by the computer. The program may be a transmission medium that can be distributed via a network such as the Internet.

(Supplementary Note 1) Secondary structure sequence data acquisition step for acquiring a plurality of secondary structure sequence data related to the secondary structure of an amino acid sequence representing an arbitrary protein;
Secondary structure data of the same amino acid residue selected from each secondary structure sequence data acquired by the secondary structure sequence data acquisition step, a secondary structure index value representing the hardness of the amino acid residue Secondary structure index value conversion step to convert to
Based on each secondary structure index value converted by the secondary structure index value conversion step, a secondary structure predicted value calculation step for calculating a predicted value related to the hardness of the amino acid residue;
A determination step of determining the amino acid residue as an antigenic determinant based on the predicted value calculated by the secondary structure predicted value calculation step;
An output step for outputting the determination result determined by the determination step;
A program for predicting an antigenic determinant characterized by causing a computer to execute.

(Appendix 2) A surface abundance sequence data acquisition step for acquiring a plurality of surface abundance sequence data relating to the surface abundance of an amino acid sequence representing an arbitrary protein;
Surface presence rate data indicating the surface presence rate of the same amino acid residue selected from the surface presence rate sequence data acquired by the surface presence rate sequence data acquisition step, and representing the surface presence rate of the amino acid residue A surface abundance index value conversion step for converting to a value;
A surface presence rate predicted value calculation step for calculating a predicted value related to the surface presence rate of the amino acid residue based on each surface presence rate index value converted by the surface presence rate index value conversion step;
A determination step for determining the amino acid residue as an antigenic determinant based on the prediction value calculated by the surface presence rate prediction value calculation step;
An output step for outputting the determination result determined by the determination step;
A program for predicting an antigenic determinant characterized by causing a computer to execute.

(Additional remark 3) The primary structure sequence data acquisition process of acquiring the primary structure sequence data of the amino acid sequence showing arbitrary proteins,
The primary structure data specifying any amino acid residue selected from the primary structure sequence data acquired in the primary structure sequence data acquisition step is converted into a hydrophobicity index value representing the hydrophobicity of the amino acid residue. A hydrophobicity index value conversion step;
Based on the hydrophobicity index value converted by the hydrophobicity index value conversion step, a predicted hydrophobicity value calculation step for calculating a predicted value related to the hydrophobicity of the amino acid residue,
A hydrophobicity / hydrophilicity conversion step of converting the prediction value calculated by the hydrophobicity prediction value calculation step into a prediction value related to the hydrophilicity of the amino acid residue;
A determination step of determining the amino acid residue as an antigenic determinant based on the predicted value converted by the hydrophobic / hydrophilic conversion step;
An output step for outputting the determination result determined by the determination step;
A program for predicting an antigenic determinant characterized by causing a computer to execute.

(Appendix 4) A surface abundance sequence data acquisition step for obtaining a plurality of surface abundance sequence data relating to the surface abundance of the amino acid sequence;
The surface presence rate data representing the surface presence rate of the amino acid residues, the surface presence rate data of the amino acid residues selected from the surface presence rate sequence data acquired by the surface presence rate sequence data acquisition step A surface abundance index value conversion step to convert to,
A surface presence rate predicted value calculation step for calculating a predicted value related to the surface presence rate of the amino acid residue based on each surface presence rate index value converted by the surface presence rate index value conversion step;
A normalization step for normalizing the predicted value calculated by the secondary structure predicted value calculating step and the predicted value calculated by the surface presence rate predicted value calculating step to the same standard;
Based on each prediction value normalized by the normalization step, a composite prediction value calculation step of calculating a composite prediction value related to the secondary structure and the surface presence rate is executed by the computer,
The determination step includes
The antigen determinant prediction program according to appendix 1, wherein the amino acid residue is determined as an antigenic determinant based on the composite prediction value calculated by the composite prediction value calculation step.

(Additional remark 5) The primary structure sequence data acquisition process of acquiring the primary structure sequence data of the amino acid sequence showing the said protein,
Hydrophobic for converting primary structure data specifying the amino acid residue selected from the primary structure sequence data acquired in the primary structure sequence data acquisition step into a hydrophobicity index value representing the hydrophobicity of the amino acid residue Sex index value conversion process,
Based on the hydrophobicity index value converted by the hydrophobicity index value conversion step, the predicted value related to the hydrophobicity of the amino acid residue is calculated to be the same standard as the predicted value normalized by the normalization step A hydrophobicity predicted value calculation step of
A hydrophobicity / hydrophilicity conversion step of converting the prediction value calculated by the hydrophobicity prediction value calculation step into a prediction value related to the hydrophilicity of the amino acid residue;
A composite prediction value calculation step for calculating a composite prediction value related to the secondary structure and the hydrophilicity based on the prediction value normalized by the normalization step and the prediction value converted by the hydrophobic / hydrophilic conversion step. And causing the computer to execute
The determination step includes
The antigen determinant prediction program according to appendix 1, wherein the amino acid residue is determined as an antigenic determinant based on the composite prediction value calculated by the composite prediction value calculation step.

(Additional remark 6) The primary structure sequence data acquisition process of acquiring the primary structure sequence data of the amino acid sequence showing the said protein,
Hydrophobic for converting primary structure data specifying the amino acid residue selected from the primary structure sequence data acquired in the primary structure sequence data acquisition step into a hydrophobicity index value representing the hydrophobicity of the amino acid residue Sex index value conversion process,
Based on the hydrophobicity index value converted by the hydrophobicity index value conversion step, the predicted value related to the hydrophobicity of the amino acid residue is calculated to be the same standard as the predicted value normalized by the normalization step A hydrophobicity predicted value calculation step of
A hydrophobicity / hydrophilicity conversion step of converting the prediction value calculated by the hydrophobicity prediction value calculation step into a prediction value related to the hydrophilicity of the amino acid residue;
A composite prediction value calculation step of calculating a composite prediction value related to the surface presence rate and the hydrophilicity based on the prediction value normalized by the normalization step and the prediction value converted by the hydrophobic / hydrophilic conversion step And causing the computer to execute
The determination step includes
The antigenic determinant prediction program according to appendix 2, wherein the antigenic determinant is determined based on the composite prediction value calculated by the composite prediction value calculation step.

(Appendix 7) A primary structure sequence data acquisition step for acquiring primary structure sequence data of an amino acid sequence representing the protein;
Hydrophobic for converting primary structure data specifying the amino acid residue selected from the primary structure sequence data acquired in the primary structure sequence data acquisition step into a hydrophobicity index value representing the hydrophobicity of the amino acid residue Sex index value conversion process,
Based on the hydrophobicity index value converted by the hydrophobicity index value conversion step, the predicted value related to the hydrophobicity of the amino acid residue is calculated to be the same standard as the predicted value normalized by the normalization step A hydrophobicity predicted value calculation step of
A hydrophobicity / hydrophilicity conversion step of converting the prediction value calculated by the hydrophobicity prediction value calculation step into a prediction value related to the hydrophilicity of the amino acid residue;
Based on the predicted value normalized by the normalization step and the predicted value converted by the hydrophobic / hydrophilic conversion step, a composite predicted value related to the secondary structure, the surface presence rate, and the hydrophilicity is calculated. A composite prediction value calculation step, and causing the computer to execute,
The determination step includes
5. The antigenic determinant prediction program according to appendix 4, wherein the amino acid residue is determined as an antigenic determinant based on the composite prediction value calculated by the composite prediction value calculation step.

(Additional remark 8) The computer-readable recording medium which recorded the antigenic determinant prediction program as described in any one of additional marks 1-7.

(Appendix 9) Secondary structure sequence data acquisition means for acquiring a plurality of secondary structure sequence data relating to the secondary structure of an amino acid sequence representing an arbitrary protein;
Secondary structure index value representing the hardness of the amino acid residue, secondary structure data of the same amino acid residue selected from each secondary structure sequence data acquired by the secondary structure sequence data acquisition means Secondary structure index value conversion means for converting to
Based on each secondary structure index value converted by the secondary structure index value converting means, a secondary structure predicted value calculating means for calculating a predicted value related to the hardness of the amino acid residue;
Determining means for determining the amino acid residue as an antigenic determinant based on the predicted value calculated by the secondary structure predicted value calculating means;
Output means for outputting the determination result determined by the determination means;
An antigenic determinant prediction device comprising:

(Supplementary Note 10) Surface presence rate sequence data acquisition means for acquiring a plurality of surface presence rate sequence data relating to the surface abundance rate of an amino acid sequence representing an arbitrary protein;
Surface presence rate data representing surface presence rate data of the same amino acid residue selected from the surface presence rate sequence data acquired by the surface presence rate sequence data acquisition means, and representing the surface presence rate of the amino acid residue Surface presence rate index value conversion means for converting to a value;
Surface presence rate predicted value calculation means for calculating a predicted value related to the surface presence rate of the amino acid residue based on each surface presence rate index value converted by the surface presence rate index value conversion means;
Determining means for determining the amino acid residue as an antigenic determinant based on the predicted value calculated by the surface presence rate predicted value calculating means;
Output means for outputting the determination result determined by the determination means;
An antigenic determinant prediction device comprising:

(Appendix 11) Primary structure sequence data acquisition means for acquiring primary structure sequence data of an amino acid sequence representing an arbitrary protein;
The primary structure data specifying any amino acid residue selected from the primary structure sequence data acquired by the primary structure sequence data acquisition means is converted into a hydrophobicity index value representing the hydrophobicity of the amino acid residue. A hydrophobic index value conversion means;
Based on the hydrophobicity index value converted by the hydrophobicity index value conversion unit, a predicted hydrophobicity value calculation unit that calculates a predicted value related to the hydrophobicity of the amino acid residue;
A hydrophobicity / hydrophilicity conversion means for converting the prediction value calculated by the hydrophobicity prediction value calculation means into a prediction value relating to the hydrophilicity of the amino acid residue;
Determining means for determining the amino acid residue as an antigenic determinant based on the predicted value converted by the hydrophobic / hydrophilic converting means;
Output means for outputting the determination result determined by the determination means;
An antigenic determinant prediction device comprising:

(Supplementary note 12) Secondary structure sequence data acquisition step of acquiring a plurality of secondary structure sequence data related to the secondary structure of an amino acid sequence representing an arbitrary protein;
Secondary structure data of the same amino acid residue selected from each secondary structure sequence data acquired by the secondary structure sequence data acquisition step, a secondary structure index value representing the hardness of the amino acid residue A secondary structure index value conversion step of converting to
Based on each secondary structure index value converted by the secondary structure index value conversion step, a secondary structure predicted value calculation step for calculating a predicted value related to the hardness of the amino acid residue;
A determination step of determining the amino acid residue as an antigenic determinant based on the prediction value calculated by the secondary structure prediction value calculation step;
An output step of outputting the determination result determined by the determination step;
An antigenic determinant prediction method characterized by comprising:

(Supplementary note 13) A surface abundance sequence data acquisition step of obtaining a plurality of surface abundance sequence data relating to the surface abundance of an amino acid sequence representing an arbitrary protein;
Surface presence rate data indicating the surface presence rate of the same amino acid residue selected from the surface presence rate sequence data acquired by the surface presence rate sequence data acquisition step, and representing the surface presence rate of the amino acid residue A surface abundance index value conversion step for converting to a value;
Based on each surface presence index value converted by the surface presence index value conversion step, a surface presence ratio predicted value calculation step for calculating a predicted value related to the surface presence ratio of the amino acid residue,
A determination step of determining the amino acid residue as an antigenic determinant based on the prediction value calculated by the surface presence rate prediction value calculation step;
An output step of outputting the determination result determined by the determination step;
An antigenic determinant prediction method characterized by comprising:

(Additional remark 14) The primary structure sequence data acquisition process of acquiring the primary structure sequence data of the amino acid sequence showing arbitrary proteins,
The primary structure data specifying any amino acid residue selected from the primary structure sequence data acquired by the primary structure sequence data acquisition step is converted into a hydrophobicity index value representing the hydrophobicity of the amino acid residue. A hydrophobicity index value conversion step;
Based on the hydrophobicity index value converted by the hydrophobicity index value conversion step, a predicted hydrophobicity value calculation step for calculating a predicted value related to the hydrophobicity of the amino acid residue,
A hydrophobicity / hydrophilicity conversion step of converting the prediction value calculated by the hydrophobicity prediction value calculation step into a prediction value relating to the hydrophilicity of the amino acid residue;
A determination step of determining the amino acid residue as an antigenic determinant based on the predicted value converted by the hydrophobic / hydrophilic conversion step;
An output step of outputting the determination result determined by the determination step;
An antigenic determinant prediction method characterized by comprising:

  As described above, the antigenic determinant prediction program, the antigenic determinant prediction apparatus, and the antigenic determinant prediction method according to the present invention are useful in all fields in which pharmacology, biochemistry, and protein three-dimensional structure are used and studied. .

It is a block diagram which shows the hardware constitutions of the antigenic determinant prediction apparatus concerning embodiment of this invention. It is a block diagram which shows the functional structure of the antigenic determinant prediction apparatus concerning embodiment of this invention. It is explanatory drawing which shows an example of the primary structure sequence data of an amino acid sequence. It is explanatory drawing which shows an example of secondary structure arrangement | sequence data. It is explanatory drawing which shows an example of surface presence rate arrangement | sequence data. It is explanatory drawing which shows the conversion table with respect to 1st secondary structure data. It is explanatory drawing which shows the conversion table with respect to 2nd secondary structure data. It is explanatory drawing which shows the conversion table with respect to 1st surface presence rate data. It is explanatory drawing which shows the conversion table with respect to 2nd surface presence rate data. It is explanatory drawing which shows the conversion table with respect to primary structure data. It is a graph which shows an example of output information. It is a flowchart which shows the antigenic determinant prediction process procedure (the 1) concerning embodiment of this invention. It is a flowchart which shows the antigenic determinant prediction process procedure (the 2) concerning embodiment of this invention. It is a flowchart which shows the antigenic determinant prediction process procedure (the 3) concerning embodiment of this invention. It is a flowchart which shows the antigenic determinant prediction process procedure (the 4) concerning embodiment of this invention.

Explanation of symbols

200 Antigenic Determinant Prediction Device 201 Acquisition Unit 202 Index Value Conversion Unit 203 Predicted Value Calculation Unit 204 Normalization Unit 205 Hydrophobic / Hydrophilicity Conversion Unit 206 Combined Predictive Value Calculation Unit 207 Contribution Rate Calculation Unit 208 Determination Unit 209 Output Unit 600, 700, 800, 900, 1000 Conversion table AS1 Primary structure array data AS2 Secondary structure array data BS Surface abundance array data CRx Contribution rate Fa, Fb, Fc Normalization parameter Hj Hydrophilicity prediction value (standardized)
j SEQ ID NO: Paj Secondary structure predicted value (normalized)
Predicted value of Pbj surface presence rate (standardized)
Pcj Prediction of hydrophobicity (standardized)
Saj Secondary structure prediction value Sbj Surface presence rate prediction value Scj Hydrophobicity prediction value

Claims (5)

A secondary structure sequence data acquisition step for acquiring a plurality of secondary structure sequence data relating to the secondary structure of an amino acid sequence representing an arbitrary protein;
Secondary structure data of the same amino acid residue selected from each secondary structure sequence data acquired by the secondary structure sequence data acquisition step, a secondary structure index value representing the hardness of the amino acid residue Secondary structure index value conversion step to convert to
Based on each secondary structure index value converted by the secondary structure index value conversion step, a secondary structure predicted value calculation step for calculating a predicted value related to the hardness of the amino acid residue;
A determination step of determining the amino acid residue as an antigenic determinant based on the predicted value calculated by the secondary structure predicted value calculation step;
An output step for outputting the determination result determined by the determination step;
A program for predicting an antigenic determinant characterized by causing a computer to execute. A surface abundance sequence data acquisition step of acquiring a plurality of surface abundance sequence data relating to the surface abundance of an amino acid sequence representing an arbitrary protein;
Surface presence rate data indicating the surface presence rate of the same amino acid residue selected from the surface presence rate sequence data acquired by the surface presence rate sequence data acquisition step, and representing the surface presence rate of the amino acid residue A surface abundance index value conversion step for converting to a value;
A surface presence rate predicted value calculation step for calculating a predicted value related to the surface presence rate of the amino acid residue based on each surface presence rate index value converted by the surface presence rate index value conversion step;
A determination step for determining the amino acid residue as an antigenic determinant based on the prediction value calculated by the surface presence rate prediction value calculation step;
An output step for outputting the determination result determined by the determination step;
A program for predicting an antigenic determinant characterized by causing a computer to execute. A primary structure sequence data acquisition step for acquiring primary structure sequence data of an amino acid sequence representing an arbitrary protein;
The primary structure data specifying any amino acid residue selected from the primary structure sequence data acquired in the primary structure sequence data acquisition step is converted into a hydrophobicity index value representing the hydrophobicity of the amino acid residue. A hydrophobicity index value conversion step;
Based on the hydrophobicity index value converted by the hydrophobicity index value conversion step, a predicted hydrophobicity value calculation step for calculating a predicted value related to the hydrophobicity of the amino acid residue,
A hydrophobicity / hydrophilicity conversion step of converting the prediction value calculated by the hydrophobicity prediction value calculation step into a prediction value related to the hydrophilicity of the amino acid residue;
A determination step of determining the amino acid residue as an antigenic determinant based on the predicted value converted by the hydrophobic / hydrophilic conversion step;
An output step for outputting the determination result determined by the determination step;
A program for predicting an antigenic determinant characterized by causing a computer to execute. Secondary structure sequence data acquisition means for acquiring a plurality of secondary structure sequence data relating to the secondary structure of an amino acid sequence representing an arbitrary protein;
Secondary structure index value representing the hardness of the amino acid residue, secondary structure data of the same amino acid residue selected from each secondary structure sequence data acquired by the secondary structure sequence data acquisition means Secondary structure index value conversion means for converting to
Based on each secondary structure index value converted by the secondary structure index value converting means, a secondary structure predicted value calculating means for calculating a predicted value related to the hardness of the amino acid residue;
Determining means for determining the amino acid residue as an antigenic determinant based on the predicted value calculated by the secondary structure predicted value calculating means;
Output means for outputting the determination result determined by the determination means;
An antigenic determinant prediction device comprising: A secondary structure sequence data acquisition step of acquiring a plurality of secondary structure sequence data relating to the secondary structure of an amino acid sequence representing an arbitrary protein;
Secondary structure data of the same amino acid residue selected from each secondary structure sequence data acquired by the secondary structure sequence data acquisition step, a secondary structure index value representing the hardness of the amino acid residue A secondary structure index value conversion step of converting to
Based on each secondary structure index value converted by the secondary structure index value conversion step, a secondary structure predicted value calculation step for calculating a predicted value related to the hardness of the amino acid residue;
A determination step of determining the amino acid residue as an antigenic determinant based on the prediction value calculated by the secondary structure prediction value calculation step;
An output step of outputting the determination result determined by the determination step;
An antigenic determinant prediction method characterized by comprising:

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