Investigating Protein-Protein and Protein-Ligand Interactions by Molecular Dynamics Simulations
In recent years, the earlier view of proteins as relatively rigid structures has been replaced by a dynamic model in which the internal motions and resulting conformational changes play an essential role in their function. In this context, molecular dynamics (MD) simulations have become an important computational tool for understanding the physical basis of the structure and function of biological macromolecules. Also in the process of finding new drugs MD simulations play an important role. Our workgroup uses molecular dynamics simulations to study proteins of biological and medical relevance, e.g. signal transduction proteins or human integrin complexes. The general aim of these investigations is to find possible new lead structures or drugs and also to understand the basic and essential mechanisms behind the mode of action of our target systems. In MD simulation, the problem size is fixed and a large number of iterations must be executed, so the MD simulation suites have to scale to hundreds or thousands CPUs to get detailed view inside biomolecular systems. The used programs AMBER and GROMACS scale well up to 64 or 32 CPUs, respectively. A typical run for about 100 ns simulation time consumes 5500 up to 21000 CPU hours.
Unable to display preview. Download preview PDF.
- 6.W. Wang, O. Donini, C.M. Reyes, P.A. Kollman, Biomolecular simulations: Recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu. Rev. Biophys. Biomol. Struct. 30(1), 211–243 (2001) CrossRefGoogle Scholar
- 7.R.W. Hockney, J.W. Eastwood, Computer simulation using particles (1988) Google Scholar
- 9.S.M. Larson, C.D. Snow, M. Shirts, V.S. Pande, Folding@home and genome@home: Using distributed computing to tackle previously intractable problems in computational biology. Comput. Genomics (2002) Google Scholar
- 10.K.J. Bowers, E. Chow, H. Xu, R.O. Dror, M.P. Eastwood, B.A. Gregersen, J.L. Klepeis, I. Kolossvary, M.A. Moraes, F.D. Sacerdoti et al., Molecular dynamics—scalable algorithms for molecular dynamics simulations on commodity clusters, in Proceedings of the 2006 ACM/IEEE Conference on Supercomputing (2006) Google Scholar
- 11.D.E. Shaw, M.M. Deneroff, R.O. Dror, J.S. Kuskin, R.H. Larson, J.K. Salmon, C. Young, B. Batson, K.J. Bowers, J.C. Chao et al., Anton, a special-purpose machine for molecular dynamics simulation, in Proceedings of the 34th Annual International Conference on Computer Architecture (2007), pp. 1–12 Google Scholar
- 15.Y. Chong, K. Borroto-Esoda, P.A. Furman, R.F. Schinazi, C.K. Chu, Molecular mechanism of DAPD/DXG against zidovudine- and lamivudine-drug resistant mutants: A molecular modeling approach. Antivir. Chem. Chemother. 13(2), 115–128 (2002) Google Scholar
- 22.U. Burkert, N.L. Allinger et al., Molecular mechanics (1982) Google Scholar
- 23.D.A. Case, T. Darden, T.E. Cheatham III, C. Simmerling, J. Wang, R.E. Duke, R. Luo, K.M. Merz, D.A. Pearlman, M. Crowley et al., in AMBER 9 (University of California, San Francisco, 2006) Google Scholar
- 28.S.B. Levy, Resistance of minicells to penicillin lysis: A method of obtaining large quantities of purified minicells. J. Bacteriol. 103(3), 836–839 (1970) Google Scholar