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Protein structure prediction method based on double-layer biased search

A protein structure and prediction method technology, which is applied in the field of protein structure prediction based on double-layer bias search, can solve the problems of poor population diversity, insufficient prediction accuracy, and low sampling efficiency, so as to improve sampling efficiency, improve prediction accuracy, and increase The effect of population diversity

Active Publication Date: 2019-12-31
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] In order to overcome the problems of low sampling efficiency, poor population diversity, and insufficient prediction accuracy of existing protein structure prediction methods, the present invention provides a protein structure prediction method based on double-layer bias search. Under the framework of the basic genetic algorithm, energy and The two-layer search strategy composed of spatial structure differences performs biased search and exclusion of conformations, thereby improving sampling efficiency, increasing population diversity, and improving overall prediction accuracy

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  • Protein structure prediction method based on double-layer biased search
  • Protein structure prediction method based on double-layer biased search
  • Protein structure prediction method based on double-layer biased search

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] refer to figure 1 with figure 2 , a protein structure prediction method based on double-layer bias search, the method comprising the following steps:

[0055] 1) Input the sequence information of the target protein;

[0056] 2) Obtain the fragment library files of 3 fragments and 9 fragments from the ROBETTA server (http: / / www.robetta.org / ) according to the target protein sequence;

[0057] 3) Setting parameters: population size NP, maximum number of iterations G, number of divisions of energy intervals M, temperature factor β;

[0058] 4) Population initialization: use the first stage of the Rosetta protocol to generate a population with a population size of NP C={C 1 , C 2 ,...,C NP}, where C i , i=1, 2,..., NP is the i-th individual;

[0059] 5) Set g=1, g∈{1, 2, ..., G};

[0060] 6) Set n=1, n∈{1, 2, ..., NP};

[0061] 7) Record C i For the i-th ...

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Abstract

The invention relates to a protein structure prediction method based on double-layer biased search. The method is characterized by under a genetic algorithm framework, firstly, initializing a population, and distributing the population to different energy intervals according to conformation energy; and then, selecting a parent generation and eliminating the conformation in a biased manner according to an energy and space structure difference double-layer selection index. A problem of an inaccurate energy function can be relieved, the conformation with a more reasonable structure can be searched according to biased sampling, and prediction precision is improved while sampling efficiency is increased. The invention provides the protein structure prediction method based on the double-layer biased search and the method is high in prediction precision.

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 double-layer offset search. Background technique [0002] Protein molecules are at the heart of many biochemical processes in cells. Proteins can only perform their biological functions if they are folded into a specific three-dimensional structure. Therefore, to understand the function of a protein, it is necessary to obtain its three-dimensional structure. As a result, people began to continuously explore the three-dimensional structure of proteins. [0003] In recent years, the determination of the three-dimensional structure of proteins mainly adopts biological wet experiment methods, such as X-ray diffraction, nuclear magnetic resonance, and cryo-electron microscopy. However, these experimental methods need to spend a lot of manpower, material resources, and financial resources, and the speed of determ...

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Application Information

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IPC IPC(8): G16B15/20G16B50/30
CPCG16B15/20G16B50/30
Inventor 张贵军夏瑜豪赵凯龙刘俊彭春祥周晓根
Owner ZHEJIANG UNIV OF TECH
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