Method for designing proteins on basis of polarizable force fields and pso (particle swarm optimization)

Abstract

The invention relates to a method for designing proteins on the basis of polarizable force fields and pso (particle swarm optimization). The method includes steps of acquiring a plurality of proteins by the aid of a protein design template; optimizing each protein, in other words, searching certain rotamers in a rotamerlib, and creating a temporary particle by the aid of each found rotamer; computing the fitness of each temporary particle, and updating local optimal particles and neighbor optimal particles according to the fitness of each temporary particle; updating positions and speeds of all non-temporary particles; comparing the positions of all the neighbor optimal particles to acquire the global optimal positions; searching side-chain conformation combined spaces by the aid of particle swarms to acquire the optimized proteins. Each certain rotamer is the closest to sub-vectors of the corresponding particle which continuously moves in a 4n (four-dimensional) space. The method has the advantages that discrete problems are solved by the aid of pso, and the polarizable force fields are used as evaluation foundations by the aid of the high efficiency of pso; modification on the proteins can be predicted theoretically, and the proteins can be screened theoretically.

Description

technical field [0001] The invention relates to a method for predicting protein function by computer, which belongs to biological information technology, computing method and computer virtual reality technology. Background technique [0002] Computer-aided protein design is an important means in protein design. But full-sequence protein design presents two challenges. The first piece of the puzzle is the combinatorial explosion. To give a simple example, if a polypeptide chain with 100 amino acids is designed, then the combination space of the sequence is pow(20,100), which is too large, so choosing a well-developed optimization algorithm is a must-face problem. The second problem is that the speed and accuracy of energy calculation functions are contradictory, and it is often difficult to have both. Without an accurate energy function to identify the optimized sequence and conformation, the best optimization algorithm will not work. [0003] Optimization strategy: Durin...

AI Technical Summary

Problems solved by technology

The second problem is that the speed and accuracy of the energy calculation function are contradictory, and it is often difficult to have both

Without an accurate energy function to identify the optimized sequence and conformation, the best optimization algorithm will not work

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