Residue contact information self-learning-based protein structure prediction method

Abstract

The invention relates to a residue contact information self-learning-based protein structure prediction method. According to the method, a fragment library and a contact map are obtained through Robetta and RaptorX-Contact; in the first stage of population evolution, residue pair distance distribution learning is performed, an information entropy index is established so as to reflect the convergence degree of a population, the purpose of automatic learning can be achieved; in the second stage of the population evolution, a scoring function is established with learned residue pair distance distribution information so as to assist an energy function in conformation space search; and a final prediction result is obtained through clustering. With the protein structure prediction method provided by the invention adopted, the residue pair distance information can be learned automatically so as to assist the energy function in conformation space optimization; and the information entropy indexis constructed, and therefore, the dynamic switching of the two stages can be realized.

Description

technical field [0001] The invention relates to the fields of biological informatics, intelligent optimization and computer application, and in particular to a protein structure prediction method based on residue contact information self-learning. Background technique [0002] A protein is a biological macromolecule with a certain specific spatial structure formed by a polypeptide chain composed of amino acids in the form of "dehydration condensation" after twists and turns, and thus plays a specific function in an organism. The three-dimensional structure of proteins is of great importance in drug design, protein engineering, and biotechnology. At present, millions of protein sequences have been resolved, but most of the protein structures are unknown. Therefore, protein structure prediction is an important research problem. [0003] The gap between protein sequence and structure is mainly due to the rapid development of sequencing technology and relatively slow progress ...

AI Technical Summary

Problems solved by technology

However, the knowledge energy function of this method cannot effectively capture the long-range interaction between residues, and when predicting a target protein with a long sequence, switching between stages under a fixed cost is likely to cause "oversampling" or insufficient sampling to affect the prediction accuracy.

[0006] Therefore, existing multi-stage protein structure prediction methods are deficient in multi-stage sampling and prediction accuracy, and need to be improved

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