Method for predicting protein structure on basis of two-stage differential evolution algorithm

A differential evolution algorithm, protein structure technology, applied in the field of computer application and bioinformatics, can solve the problems of high complexity, low prediction accuracy, low sampling efficiency, etc., and achieve the effect of low complexity and high prediction accuracy

Inactive Publication Date: 2016-07-13
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of low sampling efficiency, high complexity and low prediction accuracy in existing protein structure prediction, the p

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  • Method for predicting protein structure on basis of two-stage differential evolution algorithm
  • Method for predicting protein structure on basis of two-stage differential evolution algorithm

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Embodiment Construction

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] refer to Figure 1 ~ Figure 2 , a protein structure prediction method based on a two-stage differential evolution algorithm, comprising the following steps:

[0033] 1) given query sequence information;

[0034] 2) Initialization: set the population size NP, variation factor F, crossover probability CR, iteration number of iterations, segment length L, energy function RosettaScore3, firstly generate an initial population size of NP by randomly folding and transforming the query sequence , the initial population is P={x i |i∈I}, calculate the energy value f(x i ), i∈I, where i is the population individual number, I is the set of population individual numbers, I={1,2,...,NP};

[0035] 3) Start iteration, set g=1, g is an iteration counter, and perform the following operations on each individual in the population in turn:

[0036] 3.1) If g

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Abstract

The invention discloses a method for predicting the protein structure on the basis of a two-stage differential evolution algorithm. The method comprises the following steps: under a framework of the differential evolution algorithm (DE), firstly carrying out random folding and disturbance on an inputted inquiry sequence, and generating initial conformation populations with diversified folding types; then dividing conformation searching into two stages according to iterative times; in the first stage, randomly selecting one conformation from the populations as a target individual; in the second stage, dividing the population into two parts according to energy, and randomly selecting an individual from the front 50% of populations with low energy as a target individual; then randomly selecting three conformation individuals different from the target individual, and generating a testing individual by variation, crossing and a segment assembling strategy; when the populations are updated, judging whether the testing individual is accepted according to the energy of the conformation; and under the guidance of the two staged population, obtaining a series of metastable-state conformations with higher predicting accuracy and lower complexity by continuously updating the populations. The method disclosed by the invention has the advantages of higher predicting accuracy and lower complexity.

Description

technical field [0001] The invention relates to the fields of bioinformatics and computer applications, in particular to a protein structure prediction method based on a two-stage differential evolution algorithm. Background technique [0002] Protein molecules play a vital role in the process of biological and cellular chemical reactions. Their structural models and bioactive states have important implications for our understanding and cure of many diseases. Only when proteins are folded into a specific three-dimensional structure can they produce their unique biological functions. Therefore, to understand the function of a protein, it is necessary to obtain its three-dimensional structure. [0003] 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, p...

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Application Information

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IPC IPC(8): G06F19/16
CPCG16B15/00
Inventor 张贵军俞旭锋周晓根郝小虎王柳静李章维
Owner ZHEJIANG UNIV OF TECH
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