3D Protein structure prediction with genetic tabu search algorithm

Jack Y Yang,Xiaolong Zhang,Youping Deng,Jinshan Tang,Ting Wang,Mary Qu Yang,Huiping Luo

doi:10.1186/1752-0509-4-s1-s6

Abstract

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Highlights

Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on
Results for Fibonacci sequences we describe our experiments by using Fibonacci sequences to test the efficiency of the proposed GATS
GATS was implemented by C++ in Windows XP

Summary

Introduction

Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. It has become an important research topics in bioinformatics and it has important applications in medicine and other fields, such as drug design, prediction of diseases, and so on. Because of the complexity of the realistic protein structure, it is hard to determine the exact tri-dimensional structure from its sequence of amino acids [2]. Different from the complex structure models, HP model only assumes two types of amino acids—hydrophobic (H) and hydrophilic (P) and the sequence of amino acids is assumed to be embedded in a lattice, which is used to discretize the space of conformations. The only interaction considered in HP model is the interaction between the nonadjacent but next-neighboured hydrophobic monomers [3], which is used to force the formation of a compact hydrophobic core as observed in real proteins [4]

Methods

Results

Conclusion