Protein two-phase conformational space optimization method based on dihedral angle entropy

Abstract

The invention discloses a protein two-phase conformational space optimization method based on dihedral angle entropy. According to the method, first, a first phase and a second phase of a Rosetta protocol are utilized to initialize a population; in an exploration phase, a third phase of the Rosetta protocol is iterated to perform large-range conformational search, and the change of the dihedral angle entropy in each generation of the population is used as a standard for phase switching; and in an enhancement phase, random local disturbance is performed on a dihedral angle of a residue in a loop region on the basis of a fourth phase of the Rosetta protocol, conformational update is guided by introducing dihedral angle energy in combination with a Rosetta score3 energy function, and the adverse influence brought by non-precision of the energy function is reduced while loop region exploration is enhanced. The protein two-phase conformational space optimization method based on the dihedralangle entropy is high in prediction precision.

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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