A Method for Protein Conformation Space Optimization Based on Parsimonious Abstract Convex Estimation

Abstract

A protein conformation space optimization method based on minimalist abstract convex estimation. First, the mutation process is performed according to the local optimal conformation information to generate a better test conformation, thereby improving sampling efficiency; second, the slope control factor is dynamically updated through the learning process. Avoid the influence of the slope control factor on the accuracy of the estimated value of the lower bound of the energy; then, calculate the estimated value of the lower bound of the energy of the test conformation according to the estimated support vector of the abstract convex lower bound of the individuals adjacent to the test conformation, and reduce the computational complexity; finally, according to the estimated value of the lower bound of the energy Guide conformation selection, thereby avoiding unnecessary energy function evaluation times and reducing calculation costs. The invention provides a protein conformation space optimization method based on simple abstract convex estimation with low calculation cost and high search efficiency.

Description

technical field [0001] The invention relates to the fields of biological informatics, intelligent optimization and computer application, and in particular to a protein conformation space optimization method based on minimalist abstract convex estimation. Background technique [0002] Biological cells contain many proteins that are folded by long chains formed by more than 20 amino acids. The problem of protein structure prediction is a research hotspot in the field of computational biology today. , Interaction modeling between proteins, and the design of drug target proteins have very important guiding significance. The experimental methods for determining the three-dimensional structure of proteins mainly include X-ray crystallography and multidimensional nuclear magnetic resonance (NMR). X-ray crystallography is currently the most effective method for determining protein structure. The accuracy achieved is unmatched by other methods. The main disadvantage is that protein ...

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Problems solved by technology

However, in the above optimization methods, a large number of evaluations of the energy function are required, resulting in high computational cost; secondly, the late convergence speed is slow, resulting in low conformational search efficiency

[0004] Therefore, the existing conformational space optimization methods have shortcomings in terms of computational cost and search efficiency, and need to be improved

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