A Method for Population Conformation Space Optimization Based on Distance Constrained Selection Strategy

Abstract

A method for colony conformation space optimization based on a distance constraint selection strategy comprises the following steps: under a differential evolution algorithm framework, adopting a Rosetta Score3 coarseness knowledge energy model to effectively reduce a conformation space search dimension number and improve the convergence rate of an algorithm; introducing a fragment packaging technique based on knowledge to effectively improve prediction accuracy; utilizing character constraints of a distance spectrum in protein structure to make more structurally similar protein conformations enter a colony, so as to reduce errors caused by energy function inaccuracy and obtain more excellent local conformations; and performing more effective sampling on conformation space in combination with a high global searching capability of the differential evolution algorithm, so as to obtain conformations highly similar to a natural structure. The Rosetta Score3 coarseness knowledge energy model is adopted to effectively reduce the conformation space search dimension number and improve the convergence rate of the algorithm and prediction accuracy.

Description

technical field [0001] The invention relates to the fields of bioinformatics and computer applications, in particular to a method for optimizing population conformation space based on a distance constraint selection strategy. Background technique [0002] Protein molecules play a vital role in the process of biological and cellular chemical reactions. Their structural models and bioactive states have important implications for our understanding and cure of many diseases. Only when proteins are folded into a specific three-dimensional structure can they produce their unique biological functions. Therefore, to understand the function of a protein, it is necessary to obtain its three-dimensional structure. [0003] Protein tertiary structure prediction is an important task in bioinformatics. The biggest challenge facing the protein conformation optimization problem is to search the extremely complex protein energy function surface. The protein energy model takes into accoun...

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Problems solved by technology

[0006] In order to overcome the deficiencies of the existing population conformation space optimization methods, such as high conformation space search dimension, slow convergence speed, and low prediction accuracy, the present invention adopts the Rosetta Score3 coarse-grained knowledge energy model under the framework of differential evolution algorithm to effectively reduce Conformation space search dimension, improve the convergence speed of the algorithm, improve prediction accuracy, and provide a group conformation space optimization method based on distance constraint selection strategy

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