CN109086565B - Protein structure prediction method based on contact constraint between residues - Google Patents

Abstract

A protein structure prediction method based on residue contact constraint comprises the following steps: firstly, predicting inter-residue distance Contact information of a query sequence by using RaptorX-Contact to construct a fragment library; secondly, initializing the population conformation by adopting a tabu search method, establishing an evaluation function based on contact constraint between residues, and designing a cross mutation strategy; and finally, population updating is realized according to the distance constraint value between residues, and the algorithm sampling capacity and the search efficiency can be effectively improved by utilizing the distance constraint between the residues, so that the conformation with more compact structure and lower energy is obtained. The invention provides a protein structure prediction method with high prediction precision.

Description

Protein structure prediction method based on contact constraint between residues

Technical Field

The invention relates to the fields of bioinformatics, intelligent information processing, computer application and protein tertiary structure prediction, in particular to a protein structure prediction method based on contact constraint among residues.

Background

Proteins are important components of living bodies and are players of vital activities. The basic constituent unit of protein is amino acid, and there are more than 20 kinds of amino acid in nature, and protein is composed of carbon, hydrogen,Oxygen gasNitrogen, and general proteins may also containPhosphorus (P)Sulfur, iron, zinc, copper, boron,Manganese oxide、Iodine、Molybdenum (Mo)The amino acid consists of central carbon atom, amino group, carboxyl group, hydrogen atom and side chain of amino acid, and the amino acid is dewatered and condensed to form peptide bond, and the amino acid connected by the peptide bond forms a long chain, i.e. protein.

Protein molecules play a crucial role in the course of biochemical reactions in biological cells. Their structural models and biological activity states are of great importance to our understanding and cure of various diseases. Proteins can only produce their specific biological functions by folding into a specific three-dimensional structure. To understand the function of a protein, its three-dimensional structure must be obtained. Therefore, it is crucial for human beings to obtain the three-dimensional structure of protein, and Anfinsen suggested an innovative theory that the amino acid sequence determines the three-dimensional structure of protein in 1961. The three-dimensional structure directly determines the biological function of the protein, so people have generated great interest and developed research on the three-dimensional structure of the protein. The foreign scholars Kendelu and Pebrutz carry out structural analysis on myoglobin and hemoglobin to obtain the three-dimensional structure of the protein, and the three-dimensional structure of the protein is firstly measured by human beings, so that the two people have taken the annual Nobel prize of chemistry. In addition, the british crystallographers Bernal and 1958 proposed the concept of quaternary structure of proteins, which was defined as the extended development of primary, secondary and tertiary structure of proteins. Multidimensional nuclear magnetic resonance method and radio-crystal method are two of the most important experimental methods for determining protein structure developed in recent years. The multidimensional nuclear magnetic resonance method is a method of directly measuring the three-dimensional structure of a protein by placing the protein in water and using nuclear magnetic resonance. The ray crystal method is the most effective means for measuring the three-dimensional structure of protein so far. The proteins determined using these two methods have, to date, accounted for a vast proportion of the proteins determined. Because the experimental method has limited conditions and time, needs a large amount of manpower and material resources, and has a measuring speed far beyond that of the sequence, a prediction method which does not depend on chemical experiments and has certain accuracy is urgently needed. How to predict the three-dimensional structure of an unknown protein simply, quickly and efficiently becomes a troublesome problem for researchers. Under the double promotion of theoretical exploration and application requirements, according to the theory of determining the three-dimensional structure of the protein based on the proposed primary structure of the protein, a computer is utilized to design a proper algorithm, and the protein structure prediction taking the sequence as a starting point and the three-dimensional structure as a target is developed vigorously from the end of the 20 th century.

Predicting the three-dimensional structure of a protein using a computer and optimization algorithms starting from a sequence is called de novo prediction. The de novo prediction method is directly based on a protein physical or knowledge energy model, and utilizes an optimization algorithm to search a global minimum energy conformational solution in a conformational space. Conformational space optimization (or sampling) is one of the most critical factors that currently restrict the accuracy of de novo protein structure prediction. The application of the optimization algorithm to the de novo prediction sampling process must first solve the following three problems: (1) complexity of the energy model. The protein energy model considers the bonding action of a molecular system and the non-bonding actions such as Van der Waals force, static electricity, hydrogen bond, hydrophobicity and the like, so that the formed energy curved surface is extremely rough, and the number of local minimum solutions grows exponentially along with the increase of the sequence length; the funnel characteristic of the energy model also necessarily generates local high-energy obstacles, so that the algorithm is easy to fall into a local solution. (2) And (4) high-dimensional characteristics of the energy model. For the present time, de novo prediction methods can only deal with target proteins of smaller size, typically not more than 100. For target proteins with the size of more than 150 residues, the existing optimization methods are not sufficient. This further illustrates that as the size scale increases, it necessarily causes dimensionality problems, and the computational efforts involved in performing such a vastly organized conformational search process are prohibitive for the most advanced computers currently in use. (3) Inaccuracy of the energy model. For complex biological macromolecules such as proteins, besides various physical bonding and knowledge-based effects, the interaction between the complex biological macromolecules and surrounding solvent molecules is considered, and an accurate physical description cannot be given at present. In consideration of the problem of computational cost, researchers have proposed several physical-based force field simplification models (AMBER, CHARMM, etc.), knowledge-based force field simplification models (Rosetta, QUARK, etc.) in succession in the last decade. However, we are still far from constructing a sufficiently accurate force field that can direct the target sequence to fold in the correct direction, resulting in a mathematically optimal solution that does not necessarily correspond to the native state structure of the target protein; furthermore, the inaccuracy of the model inevitably results in the failure to objectively analyze the performance of the algorithm, thereby preventing the application of high-performance algorithms in the field of de novo protein structure prediction.

Therefore, the current protein structure prediction methods have defects in prediction accuracy and energy function, and improvement is required.

Disclosure of Invention

In order to overcome the defects of inaccurate energy function and low prediction precision of the conventional protein structure prediction method, the invention provides the protein structure prediction method which has high prediction precision and is based on the contact constraint between residues.

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

a method for predicting protein structure based on inter-residue contact constraints, the method comprising the steps of:

1) inputting a query sequence and using Raptorx-Contact (http://raptorx.uchicago.edu/ ContactMap) Predicting inter-residue distance contact information for the query sequence;

2) setting initial population size NP, maximum iteration times Gen, cross probability CR and fragment assembly times M, inputting a query sequence, a fragment library and inter-residue contact information, wherein the iteration times g is 0;

3) initializing the population by using a tabu search method, and performing search on each conformation C in the population_iDoing the following operation, wherein i ∈ [1, NP]Is the conformational index value in the population, and the process is as follows:

3.1) Pair conformation C_iPerforming M times of fragment assembly, and recording fragments used in the fragment assembly;

3.2) setting of conformation C_iSegment pair C for segment assembly_jSampling segments for taboo, where j ∈ [1, NP]And j ≠ i;

4) conformational crossing, operating as follows:

4.1) selection of the ith conformation C_iGenerating a random number r, r ∈ [0,1 ] for the target conformation]If r is smaller than CR, jump to 4.2), otherwise jump to step 5);

4.2) random selection of a conformation C_jJ ≠ i, and the conformation C is acquired by utilizing a computing secondary structure algorithm DSSP_iThe secondary structure information of (1);

4.3) according to C_iRandomly selecting a cross point p at the residue position, and judging the secondary structure type S of the residue corresponding to the cross point p, wherein the S belongs to { H, E, L }, H, E and L respectively represent a helix, a sheet layer and a random folding;

4.4) for C_iAnd C_jInterchanging two in sequence starting from the point of intersection pThe secondary structure of the corner pairs up to the residue at the other intersection S ' ≠ S, S ' ∈ { H, E, L }, yielding two new conformations C '_iAnd C'_j；

5) Conformational variant, to conformational C'_iAnd C'_jThe mutation process is as follows:

5.1) to conformation C'_iAnd C'_jAssembly of the 9 residue fragment was performed to generate two conformations C ″_iAnd C ″)_j；

5.2) alignment of conformations C ″, respectively_iAnd C ″)_jEvaluation of distance constraint score E between residues_co：

Wherein N is the total number of residue contacts,

is the confidence that the query sequence query kth residue pair p and q are predicted to have a contact,

is the true distance of k residues in the test conformation to p and q, d_conIs the threshold value at which contact is predicted,

5.3) from conformation C ″)_iAnd C ″)_jHighest inter-residue distance constraint score E 'was selected'_coThe corresponding conformation is taken as the mutated successful conformation;

6) the selection is based on the inter-residue contact constraint and is as follows:

6.1) inter-residue distance constraint score E for each conformation in the population_coAnd finding the minimum inter-residue distance constraint score E ″)_co；

6.2) if E'_coGreater than E ″)_coThen, use E'_coCorresponding conformational substitution E ″)_coImplementing population updates corresponding to the acquired conformation, otherwiseThe population is kept unchanged;

7) and g +1, judging whether the maximum iteration number Gen is reached, if the condition termination condition is not met, traversing the population to execute the step 4), and otherwise, outputting a final prediction result.

The technical conception of the invention is as follows: a protein structure prediction method based on residue contact constraint comprises the following steps: firstly, predicting inter-residue distance Contact information of a query sequence by using RaptorX-Contact to construct a fragment library; secondly, initializing the population conformation by adopting a tabu search method, establishing an evaluation function based on contact constraint between residues, and designing a cross mutation strategy; and finally, population updating is realized according to the distance constraint value between residues, and the algorithm sampling capacity and the search efficiency can be effectively improved by utilizing the distance constraint between the residues, so that the conformation with more compact structure and lower energy is obtained.

The invention has the beneficial effects that: the conformation space sampling capability is strong, and the potential conformation can be effectively stored, so that the prediction precision is improved.

Drawings

FIG. 1 is a graph of the contact profile between residues of protein 1 AOY.

FIG. 2 is a schematic diagram of the three-dimensional structure of protein 1AOY predicted by the protein structure prediction method based on the contact constraint between residues.

Detailed Description

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

Referring to fig. 1 and 2, a method for predicting protein structure based on contact constraint between residues includes the following steps:

1) inputting a query sequence and using Raptorx-Contact (http://raptorx.uchicago.edu/ ContactMap) Predicting inter-residue distance contact information for the query sequence;

2) setting initial population size NP, maximum iteration times Gen, cross probability CR and fragment assembly times M, inputting a query sequence, a fragment library and inter-residue contact information, wherein the iteration times g is 0;

3) initializing the population by using a tabu search method, and performing search on each conformation C in the population_iDoing the following operation, wherein i ∈ [1, NP]Is the conformational index value in the population, and the process is as follows:

3.1) Pair conformation C_iPerforming M times of fragment assembly, and recording fragments used in the fragment assembly;

3.2) setting of conformation C_iSegment pair C for segment assembly_jSampling segments for taboo, where j ∈ [1, NP]And j ≠ i;

4) conformational crossing, operating as follows:

4.1) selection of the ith conformation C_iGenerating a random number r, r ∈ [0,1 ] for the target conformation]If r is smaller than CR, jump to 4.2), otherwise jump to step 5);

4.2) random selection of a conformation C_jJ ≠ i, and the conformation C is acquired by utilizing a computing secondary structure algorithm DSSP_iThe secondary structure information of (1);

4.3) according to C_iRandomly selecting a cross point p at the residue position, and judging the secondary structure type S of the residue corresponding to the cross point p, wherein the S belongs to { H, E, L }, H, E and L respectively represent a helix, a sheet layer and a random folding;

4.4) for C_iAnd C_jThe secondary structure S ' ≠ S, S ' ∈ { H, E, L } of residues at the successive interchange dihedral pairs starting from the intersection p until the other, yielding two new conformations C '_iAnd C'_j；

5) Conformational variant, to conformational C'_iAnd C'_jThe mutation process is as follows:

5.1) to conformation C'_iAnd C'_jAssembly of the 9 residue fragment was performed to generate two conformations C ″_iAnd C ″)_j；

5.2) alignment of conformations C ″, respectively_iAnd C ″)_jEvaluation of distance constraint score E between residues_co：

Wherein N is the total number of residue contacts,

is the confidence that the query sequence query kth residue pair p and q are predicted to have a contact,

is the true distance of k residues in the test conformation to p and q, d_conIs the threshold value at which contact is predicted,

5.3) from conformation C ″)_iAnd C ″)_jHighest inter-residue distance constraint score E 'was selected'_coThe corresponding conformation is taken as the mutated successful conformation;

6) the selection is based on the inter-residue contact constraint and is as follows:

6.1) inter-residue distance constraint score E for each conformation in the population_coAnd finding the minimum inter-residue distance constraint score E ″)_co；

6.2) if E'_coGreater than E ″)_coThen, use E'_coCorresponding conformational substitution E ″)_coRealizing population updating corresponding to the obtained conformation, otherwise, keeping the population unchanged;

7) and g +1, judging whether the maximum iteration number Gen is reached, if the condition termination condition is not met, traversing the population to execute the step 4), and otherwise, outputting a final prediction result.

This example illustrates an α/β sheet protein 1AOY with a sequence length of 78, and is a method for predicting the tertiary structure of a protein based on inter-residue contact constraints, the method comprising the steps of:

1) inputting a query sequence and using Raptorx-Contact (http://raptorx.uchicago.edu/ ContactMap) Predicting inter-residue distance contact information for the query sequence;

2) setting an initial population scale of 100, a maximum iteration number of 1000, a cross probability of 0.5 and a fragment assembly number of 2000, and inputting a query sequence, a fragment library and inter-residue contact information, wherein the iteration number g is 0;

3) initializing population by tabu search method, andeach conformation C in the population_iDoing the following operation, where i ∈ [1,100 ]]Is the conformational index value in the population, and the process is as follows:

3.1) Pair conformation C_iPerforming 2000 times of fragment assembly, and recording fragments used in fragment assembly;

3.2) setting of conformation C_iSegment pair C for segment assembly_jIs a tabu sampling segment, wherein j belongs to [1,100 and j is not equal to i;

4) conformational crossing, operating as follows:

4.1) selection of the ith conformation C_iGenerating a random number r, r ∈ [0,1 ] for the target conformation]If r is less than 0.5, jump to 4.2), otherwise jump to step 5);

4.2) random selection of a conformation C_jJ ≠ i, and the conformation C is acquired by utilizing a computing secondary structure algorithm DSSP_iThe secondary structure information of (1);

4.3) according to C_iRandomly selecting a cross point p at the residue position, and judging the secondary structure type S of the residue corresponding to the cross point p, wherein the S belongs to { H, E, L }, H, E and L respectively represent a helix, a sheet layer and a random folding;

4.4) for C_iAnd C_jThe secondary structure S ' ≠ S, S ' ∈ { H, E, L } of residues at the successive interchange dihedral pairs starting from the intersection p until the other, yielding two new conformations C '_iAnd C'_j；

5) Conformational variant, to conformational C'_iAnd C'_jThe mutation process is as follows:

5.1) to conformation C'_iAnd C'_jAssembly of the 9 residue fragment was performed to generate two conformations C ″_iAnd C ″)_j；

5.2) alignment of conformations C ″, respectively_iAnd C ″)_jEvaluation of distance constraint score E between residues_co：

Wherein N is the total number of residue contacts,

is the confidence that the query sequence query kth residue pair p and q are predicted to have a contact,

is the true distance of k residues in the test conformation to p and q, d_conIs the threshold value at which contact is predicted,

5.3) from conformation C ″)_iAnd C ″)_jHighest inter-residue distance constraint score E 'was selected'_coThe corresponding conformation is taken as the mutated successful conformation;

6) the selection is based on the inter-residue contact constraint and is as follows:

6.1) inter-residue distance constraint score E for each conformation in the population_coAnd finding the minimum inter-residue distance constraint score E ″)_co；

6.2) if E'_coGreater than E ″)_coThen, use E'_coCorresponding conformational substitution E ″)_coRealizing population updating corresponding to the obtained conformation, otherwise, keeping the population unchanged;

7) and g +1, judging whether the maximum iteration number is 1000, if the condition termination condition is not met, traversing the population to execute the step 4), and otherwise, outputting the final prediction result.

Using the method described above, the protein was obtained in a near-native conformation with the minimum RMS deviation of 78 as exemplified by the α/β sheet protein 1AOY having a sequence length of 78

Mean root mean square deviation of

The prediction structure is shown in fig. 2.

The above description is of the excellent effects of the present invention using the 1AOY protein as an example, and it is obvious that the present invention is not only suitable for the above examples, but various modifications and improvements can be made thereto without departing from the scope of the invention as defined in the basic contents thereof, and therefore, the present invention should not be excluded from the scope of the invention.

Claims (1)

1. A protein structure prediction method based on residue-to-residue contact constraint is characterized in that: the method comprises the following steps:

1) inputting a query sequence, and predicting inter-residue distance Contact information of the query sequence by using RaptorX-Contact;

2) setting initial population size NP, maximum iteration times Gen, cross probability CR and fragment assembly times M, inputting a query sequence, a fragment library and inter-residue contact information, wherein the iteration times g is 0;

3) initializing the population by using a tabu search method, and performing search on each conformation C in the population_iDoing the following operation, wherein i ∈ [1, NP]Is the conformational index value in the population, and the process is as follows:

3.1) Pair conformation C_iPerforming M times of fragment assembly, and recording fragments used in the fragment assembly;

3.2) setting of conformation C_iSegment pair C for segment assembly_jSampling segments for taboo, where j ∈ [1, NP]And j ≠ i;

4) conformational crossing, operating as follows:

4.1) selection of the ith conformation C_iGenerating a random number r, r ∈ [0,1 ] for the target conformation]If r is smaller than CR, jump to 4.2), otherwise jump to step 5);

4.2) random selection of a conformation C_jJ ≠ i, and the conformation C is acquired by utilizing a computing secondary structure algorithm DSSP_iThe secondary structure information of (1);

4.3) according to C_iRandomly selecting a cross point p at the residue position, and judging the secondary structure type S of the residue corresponding to the cross point p, wherein the S belongs to { H, E, L }, H, E and L respectively represent a helix, a sheet layer and a random folding;

4.4) for C_iAnd C_jThe secondary structure S ' ≠ S, S ' ∈ { H, E, L } of residues at the successive interchange dihedral pairs starting from the intersection p until the other, yielding two new conformations C '_iAnd C'_j；

5) Conformational variant, to conformational C'_iAnd C'_jThe mutation process is as follows:

5.1) to conformation C'_iAnd C'_jAssembly of the 9 residue fragment was performed to generate two conformations C ″_iAnd C ″)_j；

5.2) alignment of conformations C ″, respectively_iAnd C ″)_jEvaluation of distance constraint score E between residues_co：

Wherein N is the total number of residue contacts,

is the confidence that the query sequence query kth residue pair p and q are predicted to have a contact,

is the true distance of k residues in the test conformation to p and q, d_conIs the threshold value at which contact is predicted,

5.3) from conformation C ″)_iAnd C ″)_jHighest inter-residue distance constraint score E 'was selected'_coThe corresponding conformation is taken as the mutated successful conformation;

6) the selection is based on the inter-residue distance constraint as follows:

6.1) inter-residue distance constraint score E for each conformation in the population_coAnd finding the minimum inter-residue distance constraint score E ″)_co；

6.2) if E'_coGreater than E ″)_coThen, use E'_coCorresponding conformational substitution E ″)_coRealizing population updating corresponding to the obtained conformation, otherwise, keeping the population unchanged;

7) and g +1, judging whether the maximum iteration number Gen is reached, if the condition termination condition is not met, traversing the population to execute the step 4), and otherwise, outputting a final prediction result.

Images