A population protein conformation space optimization method based on diversity index

Abstract

A population protein conformation space optimization method based on diversity index, firstly, use the first stage of Rosetta to assemble the fragments to generate the initial population, and use the second stage of Rosetta to assemble the fragments to generate a new population; then, for the randomly selected Loop region, Loop exchange is performed on the conformation to achieve population reorganization; secondly, based on the loop region, mutation operation is performed on each conformation, and all new individuals generated by mutation are merged with the current population, and the USR characteristic information of the conformation is extracted to establish a diversity index , to update the entire population based on the index and energy; finally, the third and fourth stages of Rosetta are used to further optimize the entire population to obtain the final prediction result. The invention has higher sampling efficiency, better diversity, lower complexity and higher prediction accuracy.

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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