Distance spectrum intelligence based normal distribution distance receiving probability model construction method

Abstract

The present invention provides a distance spectrum intelligence based normal distribution distance receiving probability model construction method. The method comprises: firstly, downloading a high resolution protein file with a known structure in a protein database, and removing a sequence whose homology is greater than a preset threshold by comparing a sequence similarity degree to form a non-redundancy template library; next, performing similarity degree comparison on a protein structure and a query sequence in the template library by means of a slide window, and selecting M segments with the highest score at each location of the query sequence to form a segment library file; then, selecting distances from the same segment structure in a segment library at two locations of the query sequence; and finally, according to distance distribution of an inter-residue distance spectrum, extracting a predicted distance and a variance to construct a probability density function of normal distribution, comparing structure similarity of an induced conformation, and receiving a conformation according to a distance receiving probability of normal probability. The method provided by the present invention has a stronger spatial sampling ability and higher update precision.

Description

technical field [0001] The invention relates to the fields of bioinformatics and computer applications, in particular to a method for constructing a normal distribution distance reception probability model based on distance spectrum knowledge. 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...

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Problems solved by technology

[0006] In order to overcome the disadvantages of weak spatial sampling ability and low update accuracy of existing conformational space search methods, the present invention provides a normal distribution distance reception probability based on distance spectrum knowledge with strong spatial sampling ability and high update accuracy Model building method

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