CN109411013B - Group protein structure prediction method based on individual specific variation strategy - Google Patents

Abstract

Under the framework of a differential evolution algorithm, aiming at each conformation, calculating the distance between the conformation and all other conformations in the current population, and calculating a search state factor of the conformation according to the average distance, the maximum distance and the minimum distance to determine the state of the conformation individual; if the probability that the conformation is in a local search state is high, selecting partial individuals adjacent to the conformation, and selecting the optimal individual from the individuals to guide the variation process of the conformation; randomly selecting conformations from the whole population to mutate if the conformations are in a global probing state; therefore, a specific variation strategy is set for each conformation, and the effects of improving sampling efficiency and maintaining population diversity are achieved. The invention provides a group protein structure prediction method based on an individual specific variation strategy, which has high search efficiency and prediction precision.

Description

Group protein structure prediction method based on individual specific variation strategy

Technical Field

The invention relates to the fields of bioinformatics, intelligent optimization and computer application, in particular to a group protein structure prediction method based on an individual specific variation strategy.

Background

In 1990, the human genome project was initiated in the United states and formally announced to completion in 2003. Over a decade, the human genome project has increasingly deepened human awareness of self and disease, and has profound effects on medicine, mathematics, biology and computer science. However, to date, the blueprint depicted by president clinton, usa, has not been presented: "radically changes our means of diagnosis, prevention and treatment of most diseases". The reason for this is that genetic maps only map the amino acid sequence of a protein (i.e., the primary structure of a protein), and a protein can produce its specific biological function only by folding into a specific three-dimensional structure (i.e., the tertiary structure). The correspondence between the primary structure of a protein sequence and its tertiary structure (i.e., the second genetic code, or folding code) is still an unblended mystery relative to the first genetic code, i.e., the amino acid sequence of a protein that is translated from a set of three nucleotides into DNA. Compared with protein folding, protein structure prediction has stronger practicability, and gene diagnosis can be really realized only by obtaining the three-dimensional structure of the protein, and finally the purpose of gene therapy is achieved.

At present, experimental methods for determining the three-dimensional structure of proteins include X-ray crystallography, multidimensional Nuclear Magnetic Resonance (NMR), cryoelectron microscopy, and the like. X-ray crystal diffraction is the most effective method for determining the protein structure at present, the achieved precision is incomparable with other methods, and the main defects are that the protein crystal is difficult to culture and the period for determining the crystal structure is long; the NMR method can directly determine the conformation of the protein in the solution, but the required amount of the sample is large, the purity requirement is high, and only small molecular protein can be determined at present. Second, these experimental assays are expensive, requiring hundreds of thousands of dollars to determine the three-dimensional structure of a protein, however, only about $ 1000 to determine the primary amino acid sequence of a protein, resulting in an increasing gap between protein sequence and three-dimensional structure determination. Therefore, it is an important research topic in bioinformatics to directly predict the three-dimensional structure of a protein from an amino acid sequence by using a computer as a tool and using an appropriate algorithm.

Conformational space optimization (or sampling) is one of the most critical factors that currently restrict the accuracy of de novo protein structure prediction. The Differential Evolution (DE) is the most powerful algorithm in the Evolution algorithm, and is a stochastic algorithm proposed by Price and storm in 1995. The DE algorithm has the advantages of simple structure, high convergence rate, strong robustness and the like, and is widely applied to the field of protein conformation space optimization. Sudha et al propose a differential evolution protein structure prediction method based on a local strategy; custodio et al propose a local group protein structure prediction method based on a similarity agent model; based on a DE algorithm, a Shehu research group provides a series of effective protein conformation space optimization methods, such as a multi-scale hybrid evolution algorithm HEA and a multi-objective conformation space optimization method MOEA. However, in the DE algorithm, different variation strategies have different advantages, for example, some strategies have stronger global detection capability, and some strategies have stronger local search capability. In addition, during evolution, better and poorer conformational individuals play different roles. The better individuals are responsible for directing the search direction of the algorithm, while the worse individuals are responsible for maintaining the diversity of the population. Because only one conformational mutation strategy is used in the whole evolution process, the search efficiency of the algorithm is reduced, and the algorithm is easy to fall into local optimization, so that the final prediction precision is influenced.

Therefore, the existing population protein structure prediction methods have defects in prediction accuracy and search efficiency, and improvement is required.

Disclosure of Invention

In order to overcome the defects of low prediction precision and search efficiency of the conventional group protein structure prediction method, the invention provides the group protein structure prediction method based on the individual specific variation strategy, which has high prediction precision and search efficiency.

The technical scheme adopted by the invention for solving the technical problems is as follows:

a method for population protein structure prediction based on individual specific variation strategies, the method comprising the steps of:

1) inputting sequence information of a protein to be detected, and obtaining a fragment library from a ROBETTA server (http:// www.robetta.org /);

2) setting parameters: setting population size NP, cross probability CR, segment length l, temperature factor KT, adjacent individual number N and maximum iteration number G_maxInitializing the iteration number g to be 0;

3)randomly selecting fragments from a fragment library corresponding to each residue position to assemble to generate an initial conformation population P ═ C₁,C₂,...,C_NPIn which C is_iI ═ {1,2, …, NP } is the ith conformational individual in population P;

4) calculating the energy value of each conformation in the current population according to the energy function of Rosetta socre 3;

5) for each conformation C in the population_iI ∈ {1,2, …, NP } performs the following:

5.1) reduction of conformation C_iConsidering the target conformation, calculating Euclidean distances between the target conformation and other NP-1 conformations according to carbon alpha atoms, and marking the distance between the target conformation and the jth conformation as d_j；

5.2) calculation of NP-1 distances d_jJ-1, 2, NP-1 average d_aveAnd the largest distance among the NP-1 distances is recorded as d_maxAnd the minimum distance is denoted as d_min；

5.3) calculating the search State factor for the target conformation

5.4) randomly generating a fraction between 0 and 1

If it is not

The following operations are performed:

5.4.1) selecting the N conformations closest to the target conformation and selecting the conformation C with the lowest energy among the N conformations_pbestAnd randomly selecting two adjacent constellations from the N adjacent constellations which are different from each other and are C_pbestDifferent conformation C_aAnd C_b；

5.4.2) randomly generating a random integer R 'between 1 and 2, if R' is 1, from the constellation C, respectively_aAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_pbestFragment of the corresponding position in (1), generating the mutationConformation C_mutant；

5.4.3) if R' is 2, respectively from conformation C_pbestAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_aFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.5) if

The following operations are performed:

5.5.1) randomly selecting three conformations C from the current population, which are different from each other and from the target conformation_c、C_dAnd C_e；

5.5.2) from C, respectively_c、C_dAnd C_eWherein a fragment of length l different in residue position is randomly selected to replace target conformation C_iFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.6) randomly generating a decimal R between 0 and 1, if R<CR, from conformation C_iWherein a fragment of length l is randomly selected to replace the mutated conformation C_mutantAnd performing random fragment assembly once to generate test conformation C_trial(ii) a Otherwise, the variant conformation is directly subjected to one-time random fragment assembly to generate a test conformation C_trial；

5.7) calculation of the test conformation C according to the Rosetta score3 energy function_trialThe energy value of (a);

5.8) if C_trialEnergy value of less than C_iEnergy value of (1), then C_trialReplacement C_iOtherwise according to Boltzmann probability

With C_trialReplacement C_iWherein Δ E is C_trialEnergy value of and C_iAbsolute value of the energy value error of (a);

6) g is g +1, if g>G_maxThe conformation with the lowest energy is output as the final predicted structure, otherwise, the step 5) is returned to.

The technical conception of the invention is as follows: under the framework of a differential evolution algorithm, for each conformation, calculating the distance between the conformation and all other conformations in the current population, and calculating a search state factor of the conformation according to the average distance, the maximum distance and the minimum distance to determine the state of the conformation individual; if the probability that the conformation is in a local search state is high, selecting partial individuals adjacent to the conformation, and selecting the optimal individual from the individuals to guide the variation process of the conformation; randomly selecting conformations from the whole population to mutate if the conformations are in a global probing state; therefore, a specific variation strategy is set for each conformation, and the effects of improving sampling efficiency and maintaining population diversity are achieved. The invention provides a group protein structure prediction method based on an individual specific variation strategy, which has high search efficiency and prediction precision.

The beneficial effects of the invention are as follows: the search state of each conformation is measured according to the distance between the conformation and other conformations, and different variation strategies are designed according to the conformations in different states to guide conformation search, so that the effect of improving and maintaining population diversity is achieved, and the prediction precision is improved.

Drawings

Fig. 1 is a schematic diagram of the conformational update when protein 1BQ9 is structurally predicted by a population protein structure prediction method based on an individual specific variation strategy.

FIG. 2 is a conformational distribution diagram obtained when protein 1BQ9 is structurally predicted by a population protein structure prediction method based on an individual specific variation strategy.

FIG. 3 is a three-dimensional structure diagram obtained by predicting the structure of protein 1BQ9 by a population protein structure prediction method based on an individual-specific mutation strategy.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

Referring to fig. 1 to 3, a method for predicting a group protein structure based on an individual specific variation strategy includes the following steps:

1) inputting sequence information of a protein to be detected, and obtaining a fragment library from a ROBETTA server (http:// www.robetta.org /);

2) setting parameters: setting population size NP, cross probability CR, segment length l, temperature factor KT, adjacent individual number N and maximum iteration number G_maxInitializing the iteration number g to be 0;

3) randomly selecting fragments from a fragment library corresponding to each residue position to assemble to generate an initial conformation population P ═ C₁,C₂,...,C_NPIn which C is_iI ═ {1,2, …, NP } is the ith conformational individual in population P;

4) calculating the energy value of each conformation in the current population according to the energy function of Rosetta socre 3;

5) for each conformation C in the population_iI ∈ {1,2, …, NP } performs the following:

5.1) reduction of conformation C_iConsidering the target conformation, calculating Euclidean distances between the target conformation and other NP-1 conformations according to carbon alpha atoms, and marking the distance between the target conformation and the jth conformation as d_j；

5.2) calculation of NP-1 distances d_jJ-1, 2, NP-1 average d_aveAnd the largest distance among the NP-1 distances is recorded as d_maxAnd the minimum distance is denoted as d_min；

5.3) calculating the search State factor for the target conformation

5.4) randomly generating a fraction between 0 and 1

If it is not

The following operations are performed:

5.4.1) selecting the N conformations closest to the target conformation and selecting the conformation C with the lowest energy among the N conformations_pbestAnd randomly selecting two adjacent constellations from the N adjacent constellations which are different from each other and are C_pbestDifferent conformation C_aAnd C_b；

5.4.2) randomly generating a random integer R 'between 1 and 2, if R' is 1, from the constellation C, respectively_aAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_pbestFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.4.3) if R' is 2, respectively from conformation C_pbestAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_aFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.5) if

The following operations are performed:

5.5.1) randomly selecting three conformations C from the current population, which are different from each other and from the target conformation_c、C_dAnd C_e；

5.5.2) from C, respectively_c、C_dAnd C_eWherein a fragment of length l different in residue position is randomly selected to replace target conformation C_iFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.6) randomly generating a decimal R between 0 and 1, if R<CR, from conformation C_iWherein a fragment of length l is randomly selected to replace the mutated conformation C_mutantAnd performing random fragment assembly once to generate test conformation C_trial(ii) a Otherwise, the variant conformation is directly subjected to one-time random fragment assembly to generate a test conformation C_trial；

5.7) calculation of the test conformation C according to the Rosetta score3 energy function_trialThe energy value of (a);

5.8) if C_trialEnergy value of less than C_iEnergy value of (1), then C_trialReplacement C_iOtherwise according to Boltzmann probability

With C_trialReplacement C_iWherein Δ E is C_trialEnergy value of and C_iAbsolute value of the energy value error of (a);

6) g is g +1, if g>G_maxThe conformation with the lowest energy is output as the final predicted structure, otherwise, the step 5) is returned to.

In this embodiment, beta-sheet protein 1BQ9 with sequence length 53 is an example of a method for predicting a population protein structure based on an individual-specific variation strategy, comprising the steps of:

1) inputting sequence information of a protein to be detected, and obtaining a fragment library from a ROBETTA server (http:// www.robetta.org /);

2) setting parameters: setting the population size NP equal to 50, the crossover probability CR equal to 0.5, the fragment length l equal to 3, the temperature factor KT equal to 2, the number of neighboring individuals N equal to NP/5, and the maximum number of iterations G_max1000, and initializing the iteration number g to 0;

3) randomly selecting fragments from a fragment library corresponding to each residue position to assemble to generate an initial conformation population P ═ C₁,C₂,...,C_NPIn which C is_iI ═ {1,2, …, NP } is the ith conformational individual in population P;

4) calculating the energy value of each conformation in the current population according to the energy function of Rosetta socre 3;

5) for each conformation C in the population_iI ∈ {1,2, …, NP } performs the following:

5.1) reduction of conformation C_iConsidering the target conformation, calculating Euclidean distances between the target conformation and other NP-1 conformations according to carbon alpha atoms, and marking the distance between the target conformation and the jth conformation as d_j；

5.2) calculation of NP-1 distances d_jJ-1, 2, NP-1 average d_aveAnd the largest distance among the NP-1 distances is recorded as d_maxAnd the minimum distance is denoted as d_min；

5.3) calculating the search State factor for the target conformation

5.4) randomly generating a fraction between 0 and 1

If it is not

The following operations are performed:

5.4.1) selecting the N conformations closest to the target conformation and selecting the conformation C with the lowest energy among the N conformations_pbestAnd randomly selecting two adjacent constellations from the N adjacent constellations which are different from each other and are C_pbestDifferent conformation C_aAnd C_b；

5.4.2) randomly generating a random integer R 'between 1 and 2, if R' is 1, from the constellation C, respectively_aAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_pbestFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.4.3) if R' is 2, respectively from conformation C_pbestAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_aFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.5) if

The following operations are performed:

5.5.1) randomly selecting three conformations C from the current population, which are different from each other and from the target conformation_c、C_dAnd C_e；

5.5.2) from C, respectively_c、C_dAnd C_eWherein a fragment of length l different in residue position is randomly selected to replace target conformation C_iFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.6) randomly generating a decimal R between 0 and 1, if R<CR, from conformation C_iWherein a fragment of length l is randomly selected to replace the mutated conformation C_mutantAnd performing random fragment assembly once to generate test conformation C_trial(ii) a Otherwise, the variant conformation is directly subjected to one-time random fragment assembly to generate a test conformation C_trial；

5.7) calculation of the test conformation C according to the Rosetta score3 energy function_trialThe energy value of (a);

5.8) if C_trialEnergy value of less than C_iEnergy value of (1), then C_trialReplacement C_iOtherwise according to Boltzmann probability

With C_trialReplacement C_iWherein Δ E is C_trialEnergy value of and C_iAbsolute value of the energy value error of (a);

6) g is g +1, if g>G_maxThe conformation with the lowest energy is output as the final predicted structure, otherwise, the step 5) is returned to.

Using the example of beta-sheet protein 1BQ9 with sequence length of 53, the above method was used to obtain the near-native conformation of the protein with the minimum RMS deviation of

Mean root mean square deviation of

The prediction structure is shown in fig. 3.

The above description is the result of the protein 1BQ9 of the present invention, and is not intended to limit the scope of the present invention, and various modifications and improvements can be made without departing from the scope of the present invention.

Claims (1)

1. A group protein structure prediction method based on individual specific variation strategy is characterized in that: the method comprises the following steps:

1) inputting sequence information of the protein to be detected, and obtaining a fragment library from a ROBETTA server;

2) setting parameters: setting population size NP, cross probability CR, segment length l, temperature factor KT, adjacent individual number N and maximum iteration number G_maxInitializing the iteration number g to be 0;

3) randomly selecting fragments from a fragment library corresponding to each residue position to assemble to generate an initial conformation population P ═ C₁,C₂,...,C_NPIn which C is_iI ═ {1,2, …, NP } is the ith conformational individual in population P;

4) calculating the energy value of each conformation in the current population according to the energy function of Rosetta socre 3;

5) for each conformation C in the population_iI ∈ {1,2, …, NP } performs the following:

5.1) reduction of conformation C_iConsidering the target conformation, calculating Euclidean distances between the target conformation and other NP-1 conformations according to carbon alpha atoms, and marking the distance between the target conformation and the jth conformation as d_j；

5.2) calculation of NP-1 distances d_jJ-1, 2, NP-1 average d_aveAnd the largest distance among the NP-1 distances is recorded as d_maxAnd the minimum distance is denoted as d_min；

5.3) calculating the search State factor for the target conformation

5.4) randomly generating a fraction between 0 and 1

If it is not

The following operations are performed:

5.4.1) selecting the N conformations closest to the target conformation and selecting the conformation C with the lowest energy among the N conformations_pbestAnd randomly selecting two adjacent constellations from the N adjacent constellations which are different from each other and are C_pbestDifferent conformation C_aAnd C_b；

5.4.2) randomly generating a random integer R 'between 1 and 2, if R' is 1, from the constellation C, respectively_aAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_pbestFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.4.3) if R' is 2, respectively from conformation C_pbestAnd C_bWherein a fragment of length l different from the residue is randomly selected for substitution conformation C_aFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.5) if

The following operations are performed:

5.5.1) randomly selecting three conformations C from the current population, which are different from each other and from the target conformation_c、C_dAnd C_e；

5.5.2) from C, respectively_c、C_dAnd C_eWherein a fragment of length l different in residue position is randomly selected to replace target conformation C_iFragment of the corresponding position in (1), generating a mutated conformation C_mutant；

5.6) randomly generating a decimal R between 0 and 1, if R<CR, from conformation C_iWherein a fragment of length l is randomly selected to replace the mutated conformation C_mutantAnd performing random fragment assembly once to generate test conformation C_trial(ii) a Otherwise, the variant conformation is directly subjected to one-time random fragment assembly to generate a test conformation C_trial；

5.7) calculation of the test conformation C according to the Rosetta score3 energy function_trialThe energy value of (a);

5.8) if C_trialEnergy value of less than C_iEnergy value of (1), then C_trialReplacement C_iOtherwise according to Boltzmann probability

With C_trialReplacement C_iWherein Δ E is C_trialEnergy value of and C_iAbsolute value of the energy value error of (a);

6) g is g +1, if g>G_maxThe conformation with the lowest energy is output as the final predicted structure, otherwise, the step 5) is returned to.

Images