Method for constructing distance model between protein residues based on Bolzmann probability density function

Abstract

The invention provides a method for constructing a distance model between protein residues based on a Bolzmann probability density function. The method comprises the following steps: firstly, downloading a protein file with a known structure in a protein database, constructing a non-redundant template library by comparing sequence similarities and removing redundant polypeptide chains with similarities over 30%; secondly, performing similarity comparison on a protein structure in the template library and a query sequence through a sliding window, selecting top 200 fragments with the highest score in each position of the query sequence to form a fragment library file; thirdly, selecting distances between structures from the same template fragment in the fragment libraries on the two positions of the query sequence to form a distance spectrum; and finally, calculating the probability density statistics of a residue pair in the distance spectrum according to the probability density function, using the probability density statistic between the residues to effectively reinforce the sampling of a protein conformational space, and obtain a near native conformation with higher accuracy.

Description

technical field [0001] The invention relates to the fields of bioinformatics and computer applications, in particular to a method for constructing a distance model between protein residues based on a Bolzmann probability density function. 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] Protein tertiary structure prediction is an important task in bioinformatics. The biggest challenge facing the protein conformation optimization problem is to search the extremely complex protein energy function surface. The protein energy model take...

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Problems solved by technology

[0006] In order to overcome the shortcomings of the existing conformational space search methods, such as poor conformational space sampling ability and low precision, the present invention proposes a Bolzmann probability density function-based protein residue distance model construction that enhances the conformational space sampling ability and improves accuracy method,

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