A two-stage protein conformational space optimization method based on dihedral angle entropy

Abstract

A two-stage protein conformational space optimization method based on dihedral angle entropy. First, use the first and second stages of the Rosetta protocol to initialize the population; in the exploration stage, iterate the third stage of the Rosetta protocol to search for a wide range of conformations , and the change of dihedral angle entropy value in each generation of population is used as the standard of stage switching; the enhancement stage performs random local perturbation on the dihedral angle of the residues in the loop area on the basis of the fourth stage of the Rosetta protocol, and introduces the dihedral The angular energy combined with the Rosetta score3 energy function guides the conformational update, enhances the exploration of the loop region and reduces the adverse effects of inaccurate energy functions. The invention provides a protein two-stage conformational space optimization method based on dihedral angle entropy with high prediction accuracy.

Description

technical field [0001] The invention relates to the fields of bioinformatics and computer applications, in particular to a two-stage protein conformation space optimization method based on dihedral angle entropy. Background technique [0002] Protein molecules are an important component of all cells and tissues in the human body. All important components of the body require protein. Proteins are rich in functions and play a vital role in the normal functioning of the body. The three-dimensional structure of the protein determines the function of the protein. Only when the protein is correctly folded into a specific three-dimensional structure can it produce a unique biological function. Diseases due to protein misfolding are not uncommon. Therefore, in order to understand the function of proteins and cure various diseases related to proteins, it is necessary to obtain the three-dimensional structure of proteins. [0003] Since the end of the 20th century, the field of li...

AI Technical Summary

Problems solved by technology

On the one hand, the conformational sampling ability of the existing protein structure prediction methods is not strong enough, especially the exploration efficiency of the loop region is extremely low; on the other hand, the energy function is not accurate enough, and the low-energy conformation does not necessarily correspond to the near-native conformation, which makes only Using the energy function as the standard for conformational update cannot produce high-precision prediction results

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