Replica-exchanged-based population conformation space optimization method

Abstract

A replica-exchange-based population conformation space optimization method comprises the following steps: at first, performing random folding and transforming on an inquiry sequence on each temperature layer to generate an initial population; during population regeneration, taking Rosetta Score3 as an optimized objective function, sequentially taking each individual in the population as a targeted individual in each temperature layer, then randomly selecting two individuals different from the targeted individual for mutation operation and crossover operation to generate a variation individual, after that, randomly selecting one segment of another individual to exchange with the variation individual so as to generate a test individual, performing energy value comparison on the test individual and the targeted individual, judging whether the test individual is received or not according to the energy value, after population regeneration, carrying out replica-exchange on the population individuals between every two adjacent temperature layers to increase the diversity of the population, and through continuous population regeneration and replica-exchange, obtaining a series of semi-stable-state conformations. The conformation space optimization method provided by the invention is relatively high in prediction accuracy and relatively low in complexity.

Description

technical field [0001] The present invention relates to the fields of bioinformatics and computer applications, in particular to a method for optimizing population conformation space based on copy exchange. Background technique [0002] Bioinformatics is a research hotspot in the intersection of life science and computer science. Bioinformatics research results have been widely used in gene discovery and prediction, gene data storage and management, data retrieval and mining, gene expression data analysis, protein structure prediction, gene and protein homology relationship prediction, sequence analysis and comparison, etc. . At present, according to the Anfinsen hypothesis, starting directly from the amino acid sequence, based on the potential energy model, using the global optimization method to search for the minimum energy state of the molecular system, so as to predict the natural conformation of the peptide chain with high throughput and low cost, has become the most ...

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Problems solved by technology

[0004] In order to overcome the disadvantages of low sampling efficiency, high complexity, and low prediction accuracy in existing conformation space optimization methods, the present invention proposes a population conformation space optimization method based on replica exchange: in the basic differential evolution algorithm (DE) Under the framework of , first, an initial population is generated by randomly folding and transforming the query sequence in each temperature layer; in the population update, Rosetta Score3 is used as the optimization objective function, and the free energy is the lowest based on the protein natural state structure proposed by Anfinsen In each temperature layer, each individual in the population is taken as the target individual in turn, and then two individuals different from the target individual are randomly selected for mutation and crossover to generate a mutant individual, and then another individual is randomly selected for a period of time with the mutation Individuals are exchanged to generate test individuals, and then the energy value of the test individual is compared with the target individual, and whether to accept the test individual is judged according to the energy value

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