CN107609342A - A kind of protein conformation searching method based on the constraint of secondary structure space length - Google Patents

A kind of protein conformation searching method based on the constraint of secondary structure space length Download PDF

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CN107609342A
CN107609342A CN201710683896.1A CN201710683896A CN107609342A CN 107609342 A CN107609342 A CN 107609342A CN 201710683896 A CN201710683896 A CN 201710683896A CN 107609342 A CN107609342 A CN 107609342A
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CN107609342B (en
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张贵军
王小奇
马来发
周晓根
谢腾宇
王柳静
孙科
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Zhejiang University of Technology ZJUT
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Abstract

A kind of protein conformation searching method based on the constraint of secondary structure space length, under the basic framework of genetic algorithm, space length information structure characteristic vector by the use of in target protein between the space length of each secondary structure and two neighboring secondary structure center residue is used as space restrictive condition, so that under conditions of given energy function, solution space is searched in a less conformational space, space length information is added in selection opertor simultaneously, the inexactness of energy function is compensate for, and then effectively increases the accuracy of structural modeling.The present invention proposes that a kind of sampling efficiency is higher, precision of prediction is higher, the low protein conformation searching method based on the constraint of secondary structure space length of calculation cost.

Description

A kind of protein conformation searching method based on the constraint of secondary structure space length
Technical field
The present invention relates to a kind of biological information, artificial intelligence optimization, computer application field, more particularly to one Protein conformation searching method of the kind based on the constraint of secondary structure space length.
Background technology
Protein is the large biological molecule formed by amino acid dehydrating condensation, and decisive role is played to the health of the mankind, The 26S Proteasome Structure and Function of accurate grasp protein is all significant to disease research, bio-pharmaceuticals etc..Protein knot at present The method of structure prediction mainly has two kinds：Experimental method and theoretical prediction.Experimental method includes X-ray crystallography, nuclear magnetic resonance light Spectrum and electron microscope etc.；Although these methods can determine the three-dimensional structure of some protein exactly, pass through experiment Method be time-consuming and expensive to determine structure, while the structure of some protein can not be obtained at all by experimental method. So predict that protein structure has turned into the focus in bioinformatics research using the method for calculating.Theoretic prediction methods Protein three-dimensional structure mainly is predicted from amino acid primary sequences using computer technology and intelligent optimization algorithm, so as to effectively Saved forecast cost, reduce predicted time, therefore this kind of method can more be used widely compared to experimental method.But Due to the complexity of protein structure in itself, up to the present the forecasting problem of protein three-dimensional structure be still one have it is to be solved Problem.
In the method for ab initio prediction protein structure, evolution algorithm is the important side for studying Molecular Conformation of Proteins optimization Method, such as genetic algorithm, differential evolution scheduling algorithm, these algorithms possess fast convergence rate, simple in construction and strong robustness etc. Advantage.However, when protein sequence alignment is long, because conformational space is too big, if searched for according to specific energy function, by In the inexactness of energy function, the minimum conformation of found energy is not ensured that closest to native state structure, thus it is past It is past to form correct folding.
Therefore, existing conformational space searching method in terms of precision of prediction and sampling efficiency there is defect, it is necessary to change Enter.
The content of the invention
In order to overcome existing protein structure prediction conformational space searching method to exist, sampling efficiency is relatively low, precision of prediction Relatively low deficiency, the present invention are proposed that a kind of sampling efficiency is higher, precision of prediction is higher and constrained based on secondary structure space length Protein conformation searching method.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of protein conformation searching method based on the constraint of secondary structure space length, methods described include following step Suddenly：
1) list entries information is given；
2) parameter initialization：Population scale NP, maximum genetic algebra G are setmax, determine crossover probability Pc, initial population changes Generation number iteration, intersect fragment length frag_length, assemble counter reject_number, maximum assembling number Reject_max, space in priori between the space length of secondary structure and two neighboring secondary structure center residue away from From the characteristic vector D={ d of composition1,…,dm,d1,2,…,dk,k+1, wherein dmIt is m-th of secondary structure block of target protein Length, dk,k+1It is the space length of+1 secondary structure center residue of k-th of secondary structure block and kth, ultimate range constrains model Enclose δ, select probability Ps；
3) population is initialized：Start NP bar Monte Carlo tracks, every track search iteration times, that is, generate NP Individual initial individuals；
4) to each target individual xiWith the individual x randomly selectedjProceed as follows, i, j ∈ (1 ..., NP) and j ≠ i：
4.1) probability P is pressedcTo individual xiAnd xjCrossover operation is carried out, process is as follows：
4.1.1) random selection intersects starting point in allowed band [1, total_residue-frag_length] Begin_position, while cross termination point end_position=begin_position+frag_length is calculated, Wherein total_residue is total number of residues；
4.1.2) reversed at each intersection site position ∈ [begin_position, end_position] place Angle exchanges, generation new individual x 'i,x′j, that is, intersect individual x 'i,x′j；
4.2) to intersecting individual x 'i,x′jFollowing mutation operation is carried out, process is as follows：
4.2.1) using fragment package technique to intersecting individual x 'iSpace conformation search is carried out, calculates and intersects individual x 'i Space length between the length of secondary structure after fragment assembling and two neighboring secondary structure center residue, and form distance VectorWhereinIt is to intersect individual x 'iIn m-th of secondary structure block length, It is the space length of+1 secondary structure block center residue of k-th of secondary structure block center residue and kth；
4.2.2) according to formulaCalculating there emerged a Body x 'iCharacteristic vectorWith the characteristic vector D={ d in priori1,…,dm, d1,2,…,dk,k+1Manhattan distances, the individual x " for the generation that made a variation if similarity_mutation_1≤δiMeet Secondary structure space length constrains, and goes to step 4.2.4), otherwise go to 4.2.3)；
4.2.3) counter reject_number is started counting up, if reject_number≤reject_max successively Perform step 4.2.1) and 4.2.2) generate new individual x "i, until meeting that similarity_mutation_1≤δ stops；Otherwise Perform step 4.2.1) generation new individual x "i；
4.2.4) with step 4.2.1) and 4.2.2) similarly to individual x 'jCarry out fragment assembling and calculate corresponding Manhattan distance value similarity_mutation_2, finally obtain new individual x "j；
4.2.5) according to formulaCalculate target Body xiDistance vectorWith the characteristic vector D={ d in priori1,…,dm, d1,2,…,dk,k+1Manhattan distances；
5) according to target individual xiWith the individual x " that makes a variationi、x″jEnergy and Distance conformability degree selected, select advantage Body and Population Regeneration, process are as follows：
5.1) according to Rosetta Score3 function E (xi) target individual x is calculated respectivelyiWith the individual x " that makes a variationi、x″jEnergy Measure E (xi)、E(x″i) and E (x "j)；
5.2) in target individual xiWith the individual x " that makes a variationi、x″jIn, if a certain individual X, X ∈ { xi,x″i,x″jEnergy value Less than other two individual energy values, while corresponding Manhattan distance values are also than corresponding to other two individuals Manhattan distance values are small, then the individual is advantage individual；If a certain individual X ', X ' ∈ { xi,x″i,x″jThere was only energy value ratio Other two individual energy values are small, then by select probability PsThe individual is set to advantage individual；Similarly, if a certain individual X ", X″∈{xi,x″i,x″jThere was only corresponding Manhattan distance values than Manhattan distance values corresponding to other two individuals It is small, then by select probability PsThe individual is set to advantage individual；Finally, advantage individual substitutes target individual, Population Regeneration；
6) judge whether to reach maximum genetic algebra GmaxIf meeting end condition, output result, step is otherwise gone to 4)。
The present invention technical concept be：Under the basic framework of genetic algorithm, each secondary structure in target protein is utilized Space length and two neighboring secondary structure center residue between space length information structure characteristic vector as space limit Condition processed so that under conditions of given energy function, solution space is searched in a less conformational space, while selecting Space length information is added in operator, compensate for the inexactness of energy function, and then effectively increases the essence of structural modeling Exactness.
Beneficial effects of the present invention are shown：On the one hand the space length by secondary structure and two neighboring two level knot Space length constitutive characteristic vector between structure center residue is used as space restrictive condition, reduces stable conformation space, drops simultaneously The error that low energy function is inaccurately brought, and then substantially increase precision of prediction；On the other hand, in the framework of genetic algorithm Under, by the information exchange between individual, the variation selection operation of parent individuality, accelerate convergence rate, add the more of population Sample.
Brief description of the drawings
Fig. 1 is the basic flow sheet of the protein conformation searching method based on the constraint of secondary structure space length.
Fig. 2 is that the protein conformation searching method based on the constraint of secondary structure space length carries out structure to protein 1AIL Conformation renewal schematic diagram during prediction.
Fig. 3 is that the protein conformation searching method based on the constraint of secondary structure space length carries out structure to protein 1AIL Predict obtained tomograph.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.
1~Fig. 3 of reference picture, a kind of protein conformation searching method based on the constraint of secondary structure space length, the side Method comprises the following steps：
1) list entries information is given；
2) parameter initialization：Population scale NP, maximum genetic algebra G are setmax, determine crossover probability Pc, initial population changes Generation number iteration, intersect fragment length frag_length, assemble counter reject_number, maximum assembling number Reject_max, space in priori between the space length of secondary structure and two neighboring secondary structure center residue away from From the characteristic vector D={ d of composition1,…,dm,d1,2,…,dk,k+1, wherein dmIt is m-th of secondary structure block of target protein Length, dk,k+1It is the space length of+1 secondary structure center residue of k-th of secondary structure block and kth, ultimate range constrains model Enclose δ, select probability Ps；
3) population is initialized：Start NP bar Monte Carlo tracks, every track search iteration times, that is, generate NP Individual initial individuals；
4) to each target individual xiWith the individual x randomly selectedjProceed as follows, i, j ∈ (1 ..., NP) and j ≠ i：
4.1) probability P is pressedcTo individual xiAnd xjCrossover operation is carried out, process is as follows：
4.1.1) random selection intersects starting point in allowed band [1, total_residue-frag_length] Begin_position, while cross termination point end_position=begin_position+frag_length is calculated, Wherein total_residue is total number of residues；
4.1.2) reversed at each intersection site position ∈ [begin_position, end_position] place Angle exchanges, generation new individual x 'i,x′j, that is, intersect individual x 'i,x′j；
4.2) to intersecting individual x 'i,x′jFollowing mutation operation is carried out, process is as follows：
4.2.1) using fragment package technique to intersecting individual x 'iSpace conformation search is carried out, calculates and intersects individual x 'i Space length between the length of secondary structure after fragment assembling and two neighboring secondary structure center residue, and form distance VectorWhereinIt is to intersect individual x 'iIn m-th of secondary structure block length, It is the space length of+1 secondary structure block center residue of k-th of secondary structure block center residue and kth；
4.2.2) according to formulaCalculating there emerged a Body x 'iCharacteristic vectorWith the characteristic vector D={ d in priori1,…,dm, d1,2,…,dk,k+1Manhattan distances, the individual x " for the generation that made a variation if similarity_mutation_1≤δiMeet Secondary structure space length constrains, and goes to step 4.2.4), otherwise go to 4.2.3)；
4.2.3) counter reject_number is started counting up, if reject_number≤reject_max successively Perform step 4.2.1) and 4.2.2) generate new individual x "i, until meeting that similarity_mutation_1≤δ stops；Otherwise Perform step 4.2.1) generation new individual x "i；
4.2.4) with step 4.2.1) and 4.2.2) similarly to individual x 'jCarry out fragment assembling and calculate corresponding Manhattan distance value similarity_mutation_2, finally obtain new individual x "j；
4.2.5) according to formulaCalculate target Body xiDistance vectorWith the characteristic vector D={ d in priori1,…,dm, d1,2,…,dk,k+1Manhattan distances；
5) according to target individual xiWith the individual x " that makes a variationi、x″jEnergy and Distance conformability degree selected, select advantage Body and Population Regeneration, process are as follows：
5.1) according to Rosetta Score3 function E (xi) target individual x is calculated respectivelyiWith the individual x " that makes a variationi、x″jEnergy Measure E (xi)、E(x″i) and E (x "j)；
5.2) in target individual xiWith the individual x " that makes a variationi、x″jIn, if a certain individual X, X ∈ { xi,x″i,x″jEnergy value Less than other two individual energy values, while corresponding Manhattan distance values are also than corresponding to other two individuals Manhattan distance values are small, then the individual is advantage individual；If a certain individual X ', X ' ∈ { xi,x″i,x″jThere was only energy value ratio Other two individual energy values are small, then by select probability PsThe individual is set to advantage individual；Similarly, if a certain individual X ", X″∈{xi,x″i,x″jThere was only corresponding Manhattan distance values than Manhattan distance values corresponding to other two individuals It is small, then by select probability PsThe individual is set to advantage individual；Finally, advantage individual substitutes target individual, Population Regeneration；
6) judge whether to reach maximum genetic algebra GmaxIf meeting end condition, output result, step is otherwise gone to 4)。
The α unfolded proteins 1AIL that the present embodiment sequence length is 73 is embodiment, one kind based on secondary structure space away from From the protein conformation searching method of constraint, wherein comprising the steps of：
1) list entries information is given；
2) parameter initialization：Population scale NP=200, maximum genetic algebra G are setmax=2000, determine crossover probability Pc =0.1, initial population iterations iteration=2000, intersect fragment length frag_length=9, assemble counter Reject_number=0, maximum assembling number reject_max=100, in priori the space length of secondary structure and Characteristic vector D=that space length between two neighboring secondary structure center residue is formed 3.81085,33.8066, 8.38603,30.3193,6.69076,22.1852,19.6409,17.2739,15.4455,14.6372,15.5907, 12.43 }, ultimate range restriction range δ=15, select probability Ps=0.3；
3) population is initialized：Start NP bar Monte Carlo tracks, every track search iteration times, that is, generate NP Individual initial individuals；
4) to each target individual xiWith the individual x randomly selectedjProceed as follows, i, j ∈ (1 ..., NP) and j ≠ i：
4.1) probability P is pressedcTo individual xiAnd xjCrossover operation is carried out, process is as follows：
4.1.1) random selection intersects starting point in allowed band [1, total_residue-frag_length] Begin_position, while cross termination point end_position=begin_position+frag_length is calculated, Wherein total_residue is total number of residues；
4.1.2) reversed at each intersection site position ∈ [begin_position, end_position] place Angle exchanges, generation new individual x 'i,x′j, that is, intersect individual x 'i,x′j；
4.2) to intersecting individual x 'i, x 'jFollowing mutation operation is carried out, process is as follows：
4.2.1) using fragment package technique to intersecting individual x 'iSpace conformation search is carried out, calculates and intersects individual x 'i Space length between the length of secondary structure after fragment assembling and two neighboring secondary structure center residue, and form distance VectorWhereinIt is to intersect individual x 'iIn m-th of secondary structure block length,It is the space length of+1 secondary structure block center residue of k-th of secondary structure block center residue and kth；
4.2.2) according to formulaCalculating there emerged a Body x 'iCharacteristic vectorWith the characteristic vector D={ d in priori1,…,dm, d1,2,…,dk,k+1Manhattan distances, the individual x " for the generation that made a variation if similarity_mutation_1≤δiMeet Secondary structure space length constrains, and goes to step 4.2.4), otherwise go to 4.2.3)；
4.2.3) counter reject_number is started counting up, if reject_number≤reject_max successively Perform step 4.2.1) and 4.2.2) generate new individual x "i, until meeting that similarity_mutation_1≤δ stops；Otherwise Perform step 4.2.1) generation new individual x "i；
4.2.4) with step 4.2.1) and 4.2.2) similarly to individual x 'jCarry out fragment assembling and calculate corresponding Manhattan distance value similarity_mutation_2, finally obtain new individual x "j；
4.2.5) according to formulaCalculate target Body xiDistance vectorWith the characteristic vector D={ d in priori1,…,dm, d1,2,…,dk,k+1Manhattan distances；
5) according to target individual xiWith the individual x " that makes a variationi、x″jEnergy and Distance conformability degree selected, select advantage Body and Population Regeneration, process are as follows：
5.1) according to Rosetta Score3 function E (xi) target individual x is calculated respectivelyiWith the individual x " that makes a variationi、x″jEnergy Measure E (xi)、E(x″i) and E (x "j)；
5.2) in target individual xiWith the individual x " that makes a variationi、x″jIn, if a certain individual X, X ∈ { xi,x″i,x″jEnergy value Less than other two individual energy values, while corresponding Manhattan distance values are also than corresponding to other two individuals Manhattan distance values are small, then the individual is advantage individual；If a certain individual X ', X ' ∈ { xi,x″i,x″jThere was only energy value ratio Other two individual energy values are small, then by select probability PsThe individual is set to advantage individual；Similarly, if a certain individual X ", X″∈{xi,x″i,x″jThere was only corresponding Manhattan distance values than Manhattan distance values corresponding to other two individuals It is small, then by select probability PsThe individual is set to advantage individual；Finally, advantage individual substitutes target individual, Population Regeneration；
6) judge whether to reach maximum genetic algebra GmaxIf meeting end condition, output result, step is otherwise gone to 4)。
The α unfolded proteins 1AIL for being 73 using sequence length has obtained the protein as embodiment with above method Nearly native state conformation, lowest mean square root deviation areAverage root-mean-square deviation isPre- geodesic structure is as shown in Figure 3.
Described above is the effect of optimization that is drawn using 1AIL protein by example of the present invention, and non-limiting reality of the invention Scope is applied, various modifications and improvement are done to it on the premise of without departing from scope involved by substance of the present invention, should not be excluded Outside protection scope of the present invention.

Claims (1)

  1. A kind of 1. protein conformation searching method based on the constraint of secondary structure space length, it is characterised in that：The conformation is empty Between searching method comprise the following steps：
    1) list entries information is given；
    2) parameter initialization：Population scale NP, maximum genetic algebra G are setmax, determine crossover probability Pc, initial population iteration time Number iteration, intersects fragment length frag_length, assembles counter reject_number, maximum assembling number Reject_max, space in priori between the space length of secondary structure and two neighboring secondary structure center residue away from From the characteristic vector D={ d of composition1,…,dm,d1,2,…,dk,k+1, wherein dmIt is m-th of secondary structure block of target protein Length, dk,k+1It is the space length of+1 secondary structure center residue of k-th of secondary structure block and kth, ultimate range constrains model Enclose δ, select probability Ps；
    3) population is initialized：Start NP bar Monte Carlo tracks, every track search iteration time, that is, generate NP it is individual at the beginning of Begin individual；
    4) to each target individual xiWith the individual x randomly selectedjProceed as follows, i, j ∈ (1 ..., NP) and j ≠ i：
    4.1) probability P is pressedcTo individual xiAnd xjCrossover operation is carried out, process is as follows：
    4.1.1) random selection intersects starting point begin_ in allowed band [1, total_residue-frag_length] Position, while cross termination point end_position=begin_position+frag_length is calculated, wherein Total_residue is total number of residues；
    4.1.2) windup-degree is carried out at each intersection site position ∈ [begin_position, end_position] place Exchange, generation new individual x 'i,x′j, that is, intersect individual x 'i,x′j；
    4.2) to intersecting individual x 'i,x′jFollowing mutation operation is carried out, process is as follows：
    4.2.1) using fragment package technique to intersecting individual x 'iSpace conformation search is carried out, calculates and intersects individual x 'iFragment Space length between the length of secondary structure after assembling and two neighboring secondary structure center residue, and form distance vectorWhereinIt is to intersect individual x 'iIn m-th of secondary structure block length,It is The space length of+1 secondary structure block center residue of k-th of secondary structure block center residue and kth；
    4.2.2) according to formulaCalculate individual x 'i Characteristic vectorWith the characteristic vector D={ d in priori1,…,dm,d1,2,…, dk,k+1Manhattan distances, the individual x " for the generation that made a variation if similarity_mutation_1≤δiMeet two level knot Conformational space distance restraint, goes to step 4.2.4), otherwise go to 4.2.3)；
    4.2.3) counter reject_number is started counting up, and is performed successively if reject_number≤reject_max Step 4.2.1) and 4.2.2) generate new individual x "i, until meeting that similarity_mutation_1≤δ stops；Otherwise perform Step 4.2.1) generation new individual x "i；
    4.2.4) with step 4.2.1) and 4.2.2) similarly to individual x 'jCarry out fragment assembling and calculate corresponding Manhattan away from From value similarity_mutation_2, new individual x " is finally obtainedj；
    4.2.5) according to formulaCalculate target individual xi Distance vectorWith the characteristic vector D={ d in priori1,…,dm,d1,2,…, dk,k+1Manhattan distances；
    5) according to target individual xiWith the individual x " that makes a variationi、x″jEnergy and Distance conformability degree selected, the advantage individual of selecting is simultaneously Population Regeneration, process are as follows：
    5.1) according to Rosetta Score3 function E (xi) target individual x is calculated respectivelyiWith the individual x " that makes a variationi、x″jENERGY E (xi)、E(x″i) and E (x "j)；
    5.2) in target individual xiWith the individual x " that makes a variationi、x″jIn, if a certain individual X, X ∈ { xi,x″i,x″jEnergy value be less than Other two individual energy values, while corresponding Manhattan distance values are also than Manhattan corresponding to other two individuals Distance value is small, then the individual is advantage individual；If a certain individual X ', X ' ∈ { xi,x″i,x″jThere was only energy value than other two The energy value of individual is small, then by select probability PsThe individual is set to advantage individual；Similarly, if a certain individual X ", X " ∈ { xi, x″i,x″jOnly have corresponding Manhattan distance values smaller than Manhattan distance values corresponding to other two individuals, then by choosing Select probability PsThe individual is set to advantage individual；Finally, advantage individual substitutes target individual, Population Regeneration；
    6) judge whether to reach maximum genetic algebra GmaxIf meeting end condition, output result, step 4) is otherwise gone to.
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