Artificial Bee Colony Algorithm for the Protein Structure Prediction Based on the Toy Model

Abstract

The protein structure folding is one of the most challenging problems in the field of bioinformatics. The main problem of protein structure prediction in the 3D toy model is to find the lowest energy conformation. Although many heuristic algorithms have been proposed to solve the protein structure prediction (PSP) problem, the existing algorithms are far from perfect since PSP is an NP-problem. In this paper, we proposed an artificial bee colony (ABC) algorithm based on the toy model to solve PSP problem. In order to improve the global convergence ability and convergence speed of the ABC algorithm, we adopt a new search strategy by combining the global solution into the search equation. Experimental results illustrate that the suggested algorithm can get the lowest energy when the algorithm is applied to the Fibonacci sequences and to four real protein sequences which come from the Protein Data Bank (PDB). Compared with the results obtained by PSO, LPSO, PSO-TS, PGATS, our algorithm is more efficient.