Wang-Landau sampling in face-centered-cubic hydrophobic-hydrophilic lattice model proteins.
Finding the global minimum-energy structure is one of the main problems of protein structure prediction. The face-centered-cubic (fcc) hydrophobic-hydrophilic (HP) lattice model can reach high approximation ratios of real protein structures, so the fcc lattice model is a good choice to predict the protein structures. The lacking of an effective global optimization method is the key obstacle in solving this problem. The Wang-Landau sampling method is especially useful for complex systems with a rough energy landscape and has been successfully applied to solving many optimization problems. We apply the improved Wang-Landau (IWL) sampling method, which incorporates the generation of an initial conformation based on the greedy strategy and the neighborhood strategy based on pull moves into the Wang-Landau sampling method to predict the protein structures on the fcc HP lattice model. Unlike conventional Monte Carlo simulations that generate a probability distribution at a given temperature, the Wang-Landau sampling method can estimate the density of states accurately via a random walk, which produces a flat histogram in energy space. We test 12 general benchmark instances on both two-dimensional and three-dimensional (3D) fcc HP lattice models. The lowest energies by the IWL sampling method are as good as or better than those of other methods in the literature for all instances. We then test five sets of larger-scale instances, denoted by the S, R, F90, F180, and CASP target instances on the 3D fcc HP lattice model. The numerical results show that our algorithm performs better than the other five methods in the literature on both the lowest energies and the average lowest energies in all runs. The IWL sampling method turns out to be a powerful tool to study the structure prediction of the fcc HP lattice model proteins.
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China and School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China and Network Information Center, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Jingfa
J
Liu
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China and School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
Beibei
B
Song
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China and School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
Yonglei
Y
Yao
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China and School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
Yu
Y
Xue
Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China and School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
Wenjie
W
Liu
Network Information Center, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Zhaoxia
Z
Liu
D000465:Algorithms; D003198:Computer Simulation; D057927:Hydrophobic and Hydrophilic Interactions; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D011506:Proteins