Prediction of Protein Structures Using GPU Based Simulated Annealing

Simulated annealing (SA) is one of the popular approaches to predict protein structures. SA is prohibitive because it usually consumes much computing time and is likely to fall into local minimum points. We proposed a parallel SA algorithm based on a Graph Process Unit (GPU) technique to improve the efficiency and accuracy of the protein structure prediction. First, we analyze the SA algorithm based on CPU, second, we introduce the architecture of Compute Unified Device Architecture (CUDA). Finally, we applied statistical method to optimize the performance of the CUDA based parallel algorithm. The experimental result shows that our algorithm provides a feasible solution for the protein structure prediction.

[1]  Alexander D. MacKerell,et al.  An Improved Empirical Potential Energy Function for Molecular Simulations of Phospholipids , 2000 .

[2]  U Bastolla,et al.  How to guarantee optimal stability for most representative structures in the protein data bank , 2001, Proteins.

[3]  Arunachalam Jothi Principles, challenges and advances in ab initio protein structure prediction. , 2012, Protein and peptide letters.

[4]  Nir Friedman,et al.  Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge , 2005, PLoS Comput. Biol..

[5]  P. Pevzner,et al.  Spectral Dictionaries , 2009, Molecular & Cellular Proteomics.

[6]  Michael Schroeder,et al.  Protein interactions in 3D: from interface evolution to drug discovery. , 2012, Journal of structural biology.

[7]  B. Honig,et al.  Classical electrostatics in biology and chemistry. , 1995, Science.

[8]  D. Beveridge,et al.  Exploratory studies of ab initio protein structure prediction: Multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model , 2002, Proteins.

[9]  Nitin Gupta,et al.  Parallel implementation of DNA sequences matching algorithms using PWM on GPU architecture , 2011, Int. J. Bioinform. Res. Appl..

[10]  B. Honig,et al.  Free energy determinants of secondary structure formation: I. alpha-Helices. , 1995, Journal of molecular biology.

[11]  Reha Uzsoy,et al.  Integrating Interval Estimates of Global Optima and Local Search Methods for Combinatorial Optimization Problems , 2000, J. Heuristics.

[12]  Liam J. McGuffin,et al.  The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction , 2011, Nucleic Acids Res..

[13]  P. Kollman,et al.  Pathways to a protein folding intermediate observed in a 1-microsecond simulation in aqueous solution. , 1998, Science.

[14]  N. Linial,et al.  On the design and analysis of protein folding potentials , 2000, Proteins.

[15]  Marco Punta,et al.  Structural genomics plucks high-hanging membrane proteins. , 2012, Current opinion in structural biology.

[16]  Manuel C. Peitsch,et al.  SWISS-MODEL: an automated protein homology-modeling server , 2003, Nucleic Acids Res..

[17]  Takako Takeda,et al.  High performance transcription factor-DNA docking with GPU computing , 2012, Proteome Science.

[18]  Lorenzo Dematté,et al.  GPU computing for systems biology , 2010, Briefings Bioinform..

[19]  Juan Fernández-Recio,et al.  Protein-protein docking and hot-spot prediction for drug discovery. , 2012, Current pharmaceutical design.