CN109063413B - Method for optimizing space of protein conformation by population hill climbing iteration - Google Patents

Method for optimizing space of protein conformation by population hill climbing iteration Download PDF

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CN109063413B
CN109063413B CN201810579338.5A CN201810579338A CN109063413B CN 109063413 B CN109063413 B CN 109063413B CN 201810579338 A CN201810579338 A CN 201810579338A CN 109063413 B CN109063413 B CN 109063413B
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CN109063413A (en
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张贵军
刘俊
彭春祥
周晓根
胡俊
余宝昆
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Zhejiang University of Technology ZJUT
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Abstract

A population hill climbing iterative protein conformation space optimization method includes the steps of firstly, utilizing a Rosetta protocol to conduct large-scale conformation search, then utilizing an iterative hill climbing search method to conduct further exploration on a conformation space, effectively avoiding trapping local optima while improving conformation space search efficiency, and forming a three-dimensional structure closer to a natural protein, so that accuracy of protein structure prediction is improved. The invention provides a method for optimizing a group hill-climbing iterative protein conformation space with high prediction precision.

Description

Method for optimizing space of protein conformation by population hill climbing iteration
Technical Field
The invention relates to the fields of bioinformatics and computer application, in particular to a method for optimizing a group mountain climbing iterative protein conformation space.
Background
Protein molecules play a crucial role in the course of biochemical reactions in biological cells. It is estimated that the highest content of organic substances is the highest protein content in cells of living bodies, which is 15% to 20%. The protein has abundant functions and plays an important role in the normal operation of the organism. The three-dimensional structure of a protein determines the function of the protein, and the protein can only be correctly folded into a specific three-dimensional structure to generate a specific biological function. The diseases such as mad cow disease, senile dementia and the like are caused by protein misfolding. Therefore, it is necessary to obtain a three-dimensional structure of a protein in order to understand the function of the protein and cure various diseases related to the protein.
Different proteins possess different amino acid sequences, and understanding the three-dimensional structure of proteins is the basis for studying their biological functions. The mainstream experimental methods for determining the tertiary structure of protein include X-ray crystal diffraction, nuclear magnetic resonance and the like. X-ray crystal diffraction enables the acquisition of highly accurate protein structures, but many proteins have difficulty in preparing crystals for structure analysis; whereas nmr methods are generally only capable of measuring small proteins no longer than 300 amino acids in length. The cryoelectron microscopy technology has recently developed rapidly, with the major advantage of being able to determine the structure of large proteins. Because the experimental determination speed of protein structure is far from the speed of sequence determination, it is important to predict the three-dimensional structure of protein by simulating the process of protein folding from amino acid sequence into specific space structure by combining computer technology and bioinformatics method. Anfinsen et al demonstrated: in general, proteins are capable of spontaneously folding into a particular structural conformation. That is, structural information of a protein is contained in its amino acid sequence. Therefore, it is feasible to predict the three-dimensional structure of a protein based on its amino acid sequence.
Protein structure prediction methods are mainly classified into homology modeling, canonical methods, and de novo prediction methods. Where the de novo prediction method does not rely on a database of known structures, with the possibility of finding new structure types. The existing successful methods for predicting the protein structure from the head include a Rosetta method designed by David Baker and a team thereof, a QUARK method developed by Zhangyang and the team thereof, and the like. However, a very complete method for predicting the three-dimensional structure of a protein is not available so far. The existing conformation space optimization method has the problems of low search efficiency, low convergence speed and the like, even falls into local optimum, and the phenomenon of premature convergence occurs, thereby influencing the prediction precision.
Therefore, the current conformational space optimization methods are deficient in search efficiency and prediction accuracy, and need to be improved.
Disclosure of Invention
In order to overcome the defects of the conventional conformational space optimization method in search efficiency and prediction precision, the invention provides a population hill-climbing iterative protein conformational space optimization method with higher prediction precision. Firstly, initializing a population by utilizing a first phase, a second phase, a third phase and a fourth phase of a Rosetta protocol; and then, further exploring the conformational space by using an iterative hill-climbing search method, so that the search efficiency of the conformational space is improved, and the situation that the conformational space is trapped in local optimum is effectively avoided.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a population hill-climbing iterative protein conformation space optimization method, comprising the steps of:
1) inputting sequence information of a target protein;
2) setting parameters: population size NP, number of iterations GmaxThe number of cross iterations HC, the number of variant iterations HM;
3) population initialization: iterating the first, second, third and fourth stages of the Rosetta protocol to generate a population P of NP individuals ═ P1,P2,...,PNP}；
4) Iterative hill climbing search, the process is as follows:
4.1) set G ═ 1, where G ∈ {1,2max}；
4.2) random selection of two individuals P from the population P1,P2And select P1,P2The individual with the lowest energy in Rosetta score3 is taken as the optimal individual in the cross stage
Figure BDA0001687963060000021
4.3) iterative cross-over phase, the process is as follows:
4.3.1) setting HC ═ 1, where HC ∈ {1, 2.
4.3.2) generating uniform random integers rand1, rand1 ∈ [1, L ], wherein L represents the length of the sequence of the target protein;
4.3.3) exchange P with residue rand1 as the crossover point1,P2The structures before and after the intersection point, to generate the crossed body
Figure BDA0001687963060000022
And select
Figure BDA0001687963060000023
The lowest energy individual of Rosetta score3 was used as the test individual
Figure BDA0001687963060000024
4.3.4) determining individuals based on Metropolis criteria
Figure BDA0001687963060000025
Whether or not to replace
Figure BDA0001687963060000026
The process is as follows:
4.3.4.1) calculated using the Rosetta score3 energy function
Figure BDA0001687963060000027
And
Figure BDA0001687963060000028
energy of
Figure BDA0001687963060000029
And
Figure BDA00016879630600000210
order to
Figure BDA00016879630600000211
4.3.4.2) the replacement probability p is calculated as follows,
Figure BDA0001687963060000031
KT is a temperature parameter and is set to be 2 by default;
4.3.4.3) generates random uniform fraction rand2, rand2 belongs to [0,1 ];
4.3.4.4) if rand2 is not more than p, use
Figure BDA0001687963060000032
Replacement of
Figure BDA0001687963060000033
Otherwise, keep
Figure BDA0001687963060000034
The change is not changed;
4.3.5)hc＝hc+1；
4.3.6) if HC is less than or equal to HC, go to step 4.3.2); otherwise, ending the iterative crossover stage and entering the iterative variation stage;
4.4) iterative variation phase, the process is as follows:
4.4.1) order
Figure BDA0001687963060000035
Wherein
Figure BDA0001687963060000036
And
Figure BDA0001687963060000037
respectively representing the optimal individual and the target individual in the variation stage;
4.4.2) set HM ═ 1, where HM ∈ {1, 2.
4.4.3) pairs
Figure BDA0001687963060000038
Performing mutation operation on each segment window to select the optimal variant individual, wherein the process is as follows:
4.4.3.1) setting the fragment window number hw equal to 1, where hw is equal to {1, 2.., L-2}, and L represents the length of the sequence of the predicted protein;
4.4.3.2) randomly selecting a fragment from the fragment library corresponding to the hw window, and replacing the fragment with the fragment to generate variant individuals
Figure BDA0001687963060000039
4.4.3.3) determining whether to use the individual according to Metropolis criteria
Figure BDA00016879630600000310
Replacement of
Figure BDA00016879630600000311
4.4.3.4)hw＝hw+1；
4.4.3.5) if hw is less than or equal to L-2, go to step 4.4.3.2); otherwise, go to step 4.4.4);
4.4.4) if in step 4.4.3)
Figure BDA00016879630600000312
If it is successfully replaced, the command
Figure BDA00016879630600000313
4.4.5)hm＝hm+1；
4.4.6) if HM is less than or equal to HM, turning to the step 4.4.3); otherwise, ending the iterative variation stage and entering the selection stage;
4.5) selection phase, the process is as follows:
4.5.1) selecting the two individuals with the highest energy from the population P according to the Rosetta score3 energy function
Figure BDA0001687963060000041
4.5.2) use respectively
Figure BDA0001687963060000042
And
Figure BDA0001687963060000043
replacement of
Figure BDA0001687963060000044
And
Figure BDA0001687963060000045
4.6)g＝g+1；
4.7) if G is less than or equal to GmaxGo to step 4.2); otherwise, ending the iterative hill climbing search;
5) and clustering the individuals in the population P according to a Rosetta clustering algorithm, and selecting the heart-like conformation individual of the maximum class as a final prediction result.
The invention has the beneficial effects that: firstly, a Rosetta protocol is utilized to search the conformation in a large range, then an iterative hill climbing search method is utilized to further explore the conformation space, the search efficiency of the conformation space is improved, the conformation space is effectively prevented from being trapped into local optimum, a three-dimensional structure closer to natural protein is formed, and the prediction precision of the protein structure is improved.
Drawings
Fig. 1 is a schematic diagram of conformation update when a population hill-climbing iterative protein conformation space optimization method is used for performing structure prediction on protein 1HZ 6.
FIG. 2 is a three-dimensional structure diagram obtained by performing structure prediction on protein 1HZ6 by a population hill-climbing iterative protein conformation space optimization method.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 and2, a method for optimizing a population hill-climbing iterative protein conformation space comprises the following steps:
1) inputting sequence information of a target protein;
2) setting parameters: population size NP, number of iterations GmaxThe number of cross iterations HC, the number of variant iterations HM;
3) population initialization: iterating the first, second, third and fourth stages of the Rosetta protocol to generate a population P of NP individuals ═ P1,P2,...,PNP}；
4) Iterative hill climbing search, the process is as follows:
4.1) set G ═ 1, where G ∈ {1,2max}；
4.2) random selection of two individuals P from the population P1,P2And select P1,P2The individual with the lowest energy in Rosetta score3 is taken as the optimal individual in the cross stage
Figure BDA0001687963060000046
4.3) iterative cross-over phase, the process is as follows:
4.3.1) setting HC ═ 1, where HC ∈ {1, 2.
4.3.2) generating uniform random integers rand1, rand1 ∈ [1, L ], wherein L represents the length of the sequence of the target protein;
4.3.3) exchange P with residue rand1 as the crossover point1,P2The structures before and after the intersection point, to generate the crossed body
Figure BDA0001687963060000051
And select
Figure BDA0001687963060000052
The lowest energy individual of Rosetta score3 was used as the test individual
Figure BDA0001687963060000053
4.3.4) determining individuals based on Metropolis criteria
Figure BDA0001687963060000054
Whether or not to replace
Figure BDA0001687963060000055
The process is as follows:
4.3.4.1) calculated using the Rosetta score3 energy function
Figure BDA0001687963060000056
And
Figure BDA0001687963060000057
energy of
Figure BDA0001687963060000058
And
Figure BDA0001687963060000059
order to
Figure BDA00016879630600000510
4.3.4.2) the replacement probability p is calculated as follows,
Figure BDA00016879630600000511
KT is a temperature parameter and is set to be 2 by default;
4.3.4.3) generates random uniform fraction rand2, rand2 belongs to [0,1 ];
4.3.4.4) if rand2 is not more than p, use
Figure BDA00016879630600000512
Replacement of
Figure BDA00016879630600000513
Otherwise, keep
Figure BDA00016879630600000514
The change is not changed;
4.3.5)hc＝hc+1；
4.3.6) if HC is less than or equal to HC, go to step 4.3.2); otherwise, ending the iterative crossover stage and entering the iterative variation stage;
4.4) iterative variation phase, the process is as follows:
4.4.1) order
Figure BDA00016879630600000515
Wherein
Figure BDA00016879630600000516
And
Figure BDA00016879630600000517
respectively representing the optimal individual and the target individual in the variation stage;
4.4.2) set HM ═ 1, where HM ∈ {1, 2.
4.4.3) pairs
Figure BDA00016879630600000518
Performing mutation operation on each segment window to select the optimal segment windowThe process of (1) is as follows:
4.4.3.1) setting the fragment window number hw equal to 1, where hw is equal to {1, 2.., L-2}, and L represents the length of the sequence of the predicted protein;
4.4.3.2) randomly selecting a fragment from the fragment library corresponding to the hw window, and replacing the fragment with the fragment to generate variant individuals
Figure BDA00016879630600000519
4.4.3.3) determining whether to use the individual according to Metropolis criteria
Figure BDA00016879630600000520
Replacement of
Figure BDA00016879630600000521
4.4.3.4)hw＝hw+1；
4.4.3.5) if hw is less than or equal to L-2, go to step 4.4.3.2); otherwise, go to step 4.4.4);
4.4.4) if in step 4.4.3)
Figure BDA0001687963060000061
If it is successfully replaced, the command
Figure BDA0001687963060000062
4.4.5)hm＝hm+1；
4.4.6) if HM is less than or equal to HM, turning to the step 4.4.3); otherwise, ending the iterative variation stage and entering the selection stage;
4.5) selection phase, the process is as follows:
4.5.1) selecting the two individuals with the highest energy from the population P according to the Rosetta score3 energy function
Figure BDA0001687963060000063
4.5.2) use respectively
Figure BDA0001687963060000064
And
Figure BDA0001687963060000065
replacement of
Figure BDA0001687963060000066
And
Figure BDA0001687963060000067
4.6)g＝g+1；
4.7) if G is less than or equal to GmaxGo to step 4.2); otherwise, ending the iterative hill climbing search;
5) and clustering the individuals in the population P according to a Rosetta clustering algorithm, and selecting the heart-like conformation individual of the maximum class as a final prediction result.
In this embodiment, the protein 1HZ6 with a sequence length of 72 is taken as an example, and a method for optimizing the conformational space of a population hill-climbing iterative protein comprises the following steps:
1) inputting sequence information of the target protein 1HZ 6;
2) setting parameters: population size NP 200, iteration number G max1000, 20 times of cross iteration HC, 20 times of variant iteration HM;
3) population initialization: iterating the first, second, third and fourth stages of the Rosetta protocol to generate a population P of NP individuals ═ P1,P2,...,PNP}；
4) Iterative hill climbing search, the process is as follows:
4.1) set G ═ 1, where G ∈ {1,2max}；
4.2) random selection of two individuals P from the population P1,P2And select P1,P2The individual with the lowest energy in Rosetta score3 is taken as the optimal individual in the cross stage
Figure BDA0001687963060000068
4.3) iterative cross-over phase, the process is as follows:
4.3.1) setting HC ═ 1, where HC ∈ {1, 2.
4.3.2) generating uniform random integers rand1, rand1 ∈ [1, L ], wherein L represents the length of the sequence of the target protein;
4.3.3) exchange P with residue rand1 as the crossover point1,P2The structures before and after the intersection point, to generate the crossed body
Figure BDA0001687963060000071
And select
Figure BDA0001687963060000072
The lowest energy individual of Rosetta score3 was used as the test individual
Figure BDA0001687963060000073
4.3.4) determining individuals based on Metropolis criteria
Figure BDA0001687963060000074
Whether or not to replace
Figure BDA0001687963060000075
The process is as follows:
4.3.4.1) calculated using the Rosetta score3 energy function
Figure BDA0001687963060000076
And
Figure BDA0001687963060000077
energy of
Figure BDA0001687963060000078
And
Figure BDA0001687963060000079
order to
Figure BDA00016879630600000710
4.3.4.2) the replacement probability p is calculated as follows,
Figure BDA00016879630600000711
KT is a temperature parameter and is set to be 2 by default;
4.3.4.3) generates random uniform fraction rand2, rand2 belongs to [0,1 ];
4.3.4.4) if rand2 is not more than p, use
Figure BDA00016879630600000712
Replacement of
Figure BDA00016879630600000713
Otherwise, keep
Figure BDA00016879630600000714
The change is not changed;
4.3.5)hc＝hc+1；
4.3.6) if HC is less than or equal to HC, go to step 4.3.2); otherwise, ending the iterative crossover stage and entering the iterative variation stage;
4.4) iterative variation phase, the process is as follows:
4.4.1) order
Figure BDA00016879630600000715
Wherein
Figure BDA00016879630600000716
And
Figure BDA00016879630600000717
respectively representing the optimal individual and the target individual in the variation stage;
4.4.2) set HM ═ 1, where HM ∈ {1, 2.
4.4.3) pairs
Figure BDA00016879630600000718
Performing mutation operation on each segment window to select the optimal variant individual, wherein the process is as follows:
4.4.3.1) setting the fragment window number hw equal to 1, where hw is equal to {1, 2.., L-2}, and L represents the length of the sequence of the predicted protein;
4.4.3.2) randomly selecting a fragment from the fragment library corresponding to the hw window, and replacing the fragment with the fragment to generate variant individuals
Figure BDA00016879630600000719
4.4.3.3) determining whether to use the individual according to Metropolis criteria
Figure BDA00016879630600000720
Replacement of
Figure BDA00016879630600000721
4.4.3.4)hw＝hw+1；
4.4.3.5) if hw is less than or equal to L-2, go to step 4.4.3.2); otherwise, go to step 4.4.4);
4.4.4) if in step 4.4.3)
Figure BDA0001687963060000081
If it is successfully replaced, the command
Figure BDA0001687963060000082
4.4.5)hm＝hm+1；
4.4.6) if HM is less than or equal to HM, turning to the step 4.4.3); otherwise, ending the iterative variation stage and entering the selection stage;
4.5) selection phase, the process is as follows:
4.5.1) selecting the two individuals with the highest energy from the population P according to the Rosetta score3 energy function
Figure BDA0001687963060000083
4.5.2) use respectively
Figure BDA0001687963060000084
And
Figure BDA0001687963060000085
replacement of
Figure BDA0001687963060000086
And
Figure BDA0001687963060000087
4.6)g＝g+1；
4.7) if G is less than or equal to GmaxGo to step 4.2); otherwise, ending the iterative hill climbing search;
5) and clustering the individuals in the population P according to a Rosetta clustering algorithm, and selecting the heart-like conformation individual of the maximum class as a final prediction result.
Using protein 1HZ6 with amino acid sequence length of 72 as an example, the above method can obtain the near-native conformation of the protein, the conformation renewal scheme is shown in FIG. 1, and the minimum root mean square deviation is
Figure BDA0001687963060000088
The prediction structure is shown in fig. 2.
The foregoing is a predictive description of the invention as embodied in one embodiment, and it will be apparent that the invention is not limited to the embodiment described above, but may be embodied with various modifications without departing from the basic inventive concept and without departing from the spirit thereof.

Claims (1)

1. A method for optimizing a population hill-climbing iterative protein conformation space is characterized by comprising the following steps: the conformation space optimization method comprises the following steps:
1) inputting sequence information of a target protein;
2) setting parameters: population size NP, number of iterations GmaxCross iteration number HC and variation iteration number HM;
3) population initialization: iterating the first, second, third and fourth stages of the Rosetta protocol to generate a population P of NP individuals ═ P1,P2,...,PNP}；
4) Iterative hill climbing search, the process is as follows:
4.1) set G ═ 1, where G ∈ {1,2max}；
4.2) random selection of two individuals P from the population P1,P2And select P1,P2The individual with the lowest energy in Rosetta score3 is taken as the optimal cross-stage individual Pc best；
4.3) iterative cross-over phase, the process is as follows:
4.3.1) setting HC ═ 1, where HC ∈ {1, 2.
4.3.2) generating uniform random integers rand1, rand1 ∈ [1, L ], wherein L represents the length of the sequence of the target protein;
4.3.3) exchange P with residue rand1 as the crossover point1,P2The structures before and after the intersection point generate the crossed individual Pc 1,Pc 2And select Pc 1,Pc 2The lowest energy individual of Rosetta score3 was designated as test individual Pc trial；
4.3.4) determining individual P according to Metropolis criteriac trialWhether or not to replace Pc bestThe process is as follows:
4.3.4.1) calculating P using the Rosetta score3 energy functionc bestAnd Pc trialEnergy of
Figure FDA0002893369050000012
And
Figure FDA0002893369050000013
order to
Figure FDA0002893369050000014
4.3.4.2) the replacement probability p is calculated as follows,
Figure FDA0002893369050000011
KT is a temperature parameter and is set to be 2 by default;
4.3.4.3) generates random uniform fraction rand2, rand2 belongs to [0,1 ];
4.3.4.4) if rand2 is not more than P, use Pc trialReplacement of Pc best(ii) a Otherwise, P is maintainedc bestThe change is not changed;
4.3.5)hc＝hc+1；
4.3.6) if HC is less than or equal to HC, go to step 4.3.2); otherwise, ending the iterative crossover stage and entering the iterative variation stage;
4.4) iterative variation phase, the process is as follows:
4.4.1) order
Figure FDA0002893369050000021
Wherein
Figure FDA0002893369050000022
And
Figure FDA0002893369050000023
respectively representing the optimal individual and the target individual in the variation stage;
4.4.2) set HM ═ 1, where HM ∈ {1, 2.
4.4.3) pairs
Figure FDA0002893369050000024
Performing mutation operation on each segment window, selecting the optimal variant individuals,
the process is as follows:
4.4.3.1) setting the fragment window number hw equal to 1, wherein hw is equal to {1, 2.., L-2}, and L represents the length of the sequence of the target protein;
4.4.3.2) randomly selecting a fragment from the fragment library corresponding to the hw window, and replacing the fragment with the fragment to generate variant individuals
Figure FDA0002893369050000025
4.4.3.3) determining whether to use the individual according to Metropolis criteria
Figure FDA0002893369050000026
Replacement of
Figure FDA0002893369050000027
4.4.3.4)hw＝hw+1；
4.4.3.5) if hw is less than or equal to L-2, go to step 4.4.3.2); otherwise, go to step 4.4.4);
4.4.4) if in step 4.4.3)
Figure FDA0002893369050000028
If it is successfully replaced, the command
Figure FDA0002893369050000029
4.4.5)hm＝hm+1；
4.4.6) if HM is less than or equal to HM, turning to the step 4.4.3); otherwise, ending the iterative variation stage and entering the selection stage;
4.5) selection phase, the process is as follows:
4.5.1) selecting the two individuals with the highest energy from the population P according to the Rosetta score3 energy function
Figure FDA00028933690500000210
4.5.2) with P, respectivelyc bestAnd
Figure FDA00028933690500000211
replacement of
Figure FDA00028933690500000212
And
Figure FDA00028933690500000213
4.6)g＝g+1；
4.7) if G is less than or equal to GmaxGo to step 4.2); otherwise, ending the iterative hill climbing search;
5) and clustering the individuals in the population P according to a Rosetta clustering algorithm, and selecting the heart-like conformation individual of the maximum class as a final prediction result.
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