Protein conformation optimization method based on simple space abstract convexity lower bound estimation

Abstract

A protein conformation optimization method based on simple space abstract convexity lower bound estimation comprises the following steps that according to a coarsness energy model, a Rosetta Score 3 is adopted as an optimized objective function, and an energy calculation model is converted into a dihedral angle optimized space energy model; through feature vector extraction, a high-dimensional dihedral angle optimization problem is converted into an actually operable Descartes space optimization problem; based on Karmarker projective transformation, a Descartes space energy model is converted into a nonlinear optimization problem constrained by unit simplex, and an abstract convexity lower bound supporting face is constructed in this way, and is updated; fragment assembly and a Monte Carlo algorithm are combined to obtain a series of metastable state conformation; finally, high-resolution protein conformation is obtained through a Refinement service provided by a Rosetta sever. The method is high in sampling efficiency, low in complexity and high in prediction precision.

Description

technical field [0001] The invention relates to the fields of bioinformatics and computer applications, in particular to a method for optimizing protein conformation based on the estimation of a reduced convex lower bound of space abstraction. Background technique [0002] Bioinformatics is a research hotspot in the intersection of life science and computer science. At present, according to the Anfinsen hypothesis, starting directly from the amino acid sequence, based on the potential energy model, using the global optimization method to search for the minimum energy state of the molecular system, so as to predict the natural conformation of the peptide chain with high throughput and low cost, has become the most important bioinformatics. one of the research topics. For low sequence similarity or peptides (small proteins <10 residues), de novo prediction methods are the only option. Ab initio prediction methods must consider the following two factors: (1) protein struct...

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Problems solved by technology

[0004] In order to overcome the disadvantages of low sampling efficiency, high complexity, and low prediction accuracy of existing protein conformation optimization methods, the present invention proposes a simple space-based method with high sampling efficiency, low complexity, and high prediction accuracy. Protein conformation optimization method based on abstract convex lower bound estimation

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