Spatial optimization method for population protein conformation based on diversity indicators

Abstract

A spatial optimization method for population protein conformation based on diversity indicators, comprising the steps of: firstly using the first stage of Rosetta to assemble in segments to generate an initial population, and using the second stage of Rosetta to assemble in segments to generate a new population; then performing Loop exchange on the conformation for the randomly selected Loop region to realize population reorganization; secondly, mutating each conformation based on the Loop region, merging all new individuals generated by the mutation with the current population, extracting theUSR characteristic information of the conformation to establish a diversity index, and updating the entire population based on the index and energy; finally, further optimizing the entire populationrespectively using the third and fourth stages of Rosetta to obtain the final prediction result. The invention has high sampling efficiency, good diversity, low complexity and high prediction precision.

Description

technical field [0001] The invention relates to the fields of bioinformatics and computer applications, in particular to a method for optimizing population protein conformation space based on diversity indexes. 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. . The genome specifies all the proteins that make up the organism, and the genes specify the sequence of amino acids that make up the proteins. Proteins determine the function of cells, linking cell activities to life. The three-dimensional structure of a protein is one of the main determinants of its unique functional properties. Determination of...

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Problems solved by technology

[0005] In order to overcome the shortcomings of the existing protein structure prediction methods, such as low sampling efficiency, poor conformational diversity, high complexity, and low prediction accuracy, the present invention measures conformational diversity by extracting protein feature information, and proposes a method with high sampling efficiency A population protein conformation space optimization method based on diversity index, better diversity, lower complexity and higher prediction accuracy

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