Ron Levy of Temple University is supported by an award from the Chemical Theory, Models and Computational Methods Program in the Chemistry Division for the development of powerful computational and statistical tools to provide a fundamental understanding of how proteins function in controlling cellular processes. This project is co-funded by the Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences Program in the Division of Mathematical Sciences, and contributes to the National Strategic Computing Initiative. Understanding how proteins function requires extensive surveys of the molecular landscapes that control which structural states are functional, and knowing the free energy costs associated with transitions between states, including "active" and "inactive" protein states. The adaptive computational techniques based on stochastic reweighting that are being developed in this project make it possible to perform more reliable and accurate simulations of protein-ligand binding and conformational free energy changes of proteins, and to do so at extreme scale, on large computing grids consisting of hundreds of thousands of processors. This capability has important applications in the pharmaceutical industry and for biotechnology. The research is being carried out in an academic environment, where students are trained in state-of-the-art simulation techniques. A key aspect of the research is the creation and dissemination of the new theoretical and computational approaches, implemented as computer scripts and programs made freely available to the community.
The focus of this research is on developing new sampling protocols for performing biomolecular simulations on massive but minimally communicating computational grids. The algorithms are designed to scale from campus grids with ~5K processors to the IBM World Community grid with ~500K processors. New free energy reweighting techniques based on stochastic solutions to the unbinned weighted histogram analysis equations are being formulated which can be used to estimate free energies from multi-state biomolecular simulations, when the simulations are locally equilibrated but not close to globally equilibrated at some of the states. Professor Levy is developing an adaptive version of asynchronous replica exchange that incorporates stochastic solutions to the free energy reweighting equations directly into the algorithms used to propagate the trajectories. This project addresses the significant computational and statistical challenges associated with performing and analyzing biomolecular simulations using multi-canonical sampling schemes designed specifically for use on computational grids, and which span sizes from thousands of processors (campus grids) to hundreds of thousands of processors (the IBM World Community Grid (WCG)). Two applications are used to illustrate the new approach and its implementation on the WCG: designing inhibitors of HIV-1 integrase, and exploring the effect of sequence on functional transitions in kinases. Professor Levy provides lectures and hands-on experience with the new codes and techniques through summer schools and workshops in the U.S. and Europe. Codes and scripts are freely disseminated for use by the simulation community, and can be interfaced to widely-used open source molecular dynamics codes such as GROMACS and OpenMM.
