An Efficient Ant Colony Optimization Algorithm for Protein Structure Prediction

Protein structure prediction is considered as one of the most long-standing and challenging problem in bioinformatics. In this paper, we present an efficient ant colony optimization algorithm to predict the protein structure on three-dimensional face-centered cubic lattice coordinates, using the hydrophobic-polar model and the Miyazawa-Jernigan model to calculate the free energy. The reinforcement learning information is expressed in the k-order Markov model, and the heuristic information is determined based on the increase of the total energy. On a set of benchmark proteins, the results show a remarkable efficiency of our algorithm in comparison with several state-of-the-art algorithms.