Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2013 (2013), Article ID 924137, 15 pages
Research Article

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

1Institute for Integrated & Intelligent Systems, Science 2 (N34) 1.45, 170 Kessels Road, Nathan, QLD 4111, Australia
2Queensland Research Lab, National ICT Australia, Level 8, Y Block, 2 George Street, Brisbane, QLD 4000, Australia
3Computer Science, 2000 Lakeshore drive, Math 308, New Orleans, LA 70148, USA

Received 30 April 2013; Revised 16 August 2013; Accepted 19 August 2013

Academic Editor: Tatsuya Akutsu

Copyright © 2013 Mahmood A. Rashid et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results.