Protein high polymer (HP) model calculation method based on variable angular distance quantum evolutionary algorithm (QEA) algorithm

Abstract

The invention discloses a protein high polymer (HP) calculation method based on a variable angular distance quantum evolutionary algorithm (QEA) algorithm. A quantum evolutionary algorithm based on the variable angular distance is applied to prediction of a secondary structure of proteins, and a variable angular distance evolutionary strategy is imported based on the framework foundation of the quantum evolutionary algorithm framework. A flexible high-efficient directional solution form is adopted for the presentation of the HP configuration. A direction traction mechanism is imported for increasing diversity of the directional solution, and therefore the protein HP model calculation method based on the variable angular distance QEA algorithm is enabled to find the lowest energy configuration of the protein high-efficiently. The method adopts the directional solution to replace the original coordinate solution, facilitates detection and repair of invalid closed loop HP configuration, and therefore execution speed of the method is improved. The protein HP calculation method based on the variable angular distance QEA algorithm adopts the pattern of the directional solution, a rollback method, the direction traction strategy, the variable angular distance technology or strategy and the like, and is better than other methods in efficiency.

Description

technical field [0001] The invention belongs to the technical field of biological information, and relates to a method for solving a protein HP model based on a variable angular distance QEA algorithm. Background technique [0002] The problem of protein folding, that is, the problem of protein structure prediction, is one of the core problems in the field of bioinformatics. The protein folding problem is to study how the natural structure of the protein is formed, that is, how the polypeptide chain with a certain amino acid sequence gradually forms the unique spatial structure of the protein. At present, many "conformational diseases" or "folding diseases" are caused by molecular aggregation or even precipitation caused by abnormal protein folding, such as: Alzheimer's disease (protein conformation change), cystic fibrosis (protein cannot fold), familial hypertrophy Cholesterolosis (protein misfolding), familial amyloidosis (protein deposits), and more. Therefore, a deep ...

AI Technical Summary

Problems solved by technology

Most of these algorithms use the coordinates of HP grid points of a certain scale as the problem solution, and evolve it iteratively to try to obtain the optimal coordinate solution (that is, the configuration with the lowest energy). However, these algorithms have certain defects.

Among them, with the expansion of the scale, the genetic algorithm cannot obtain the optimal solution (for example, for the calculation examples with 50, 60, 64, and 85 amino acids, the configuration with the lowest energy cannot be found), and the convergence speed is slow; although the difference algorithm The performance is better than the genetic algorithm, and the lowest energy configuration can be found for the published calculation examples, but the convergence speed needs to be improved; although the particle swarm algorithm has a good improvement in the convergence speed, it has the defect that it is easy to fall into local optimum

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