NERSC Initiative for Scientific Exploration (NISE) 2009 Awards

Minimum Free Energy Paths and Free-Energy Profiles of Protein Conformational Changes

Jhih-Wei Chu, University of California, Berkeley

Sponsoring NERSC Project: Activation and Stabilization of Enzymes in Non-aqueous Environments (m787), Principal Investigator: Jhih-Wei Chu, University of California, Berkeley

We propose to apply a reaction path optimization method that we recently developed (Brokaw, Haas, and Chu, JCTC, 5, 2009, 2050-2061) for finding minimum free energy paths and computing free-energy profiles of protein conformational changes.

This method duplicates many (10-100) replicas of a protein system to form a chain that connects two metastable conformations. Molecular dynamics (MD) simulations are performed to compute the mean forces on each replica to minimize the total free energy of a chain. As shown by Vanden-Eijden and coworkers (JPCB, 2005), the resulting minimum free energy path corresponds to a most probable path for the transition between two conformations. The free-energy profile along the optimized path can then be calculated to characterize the energetics of rare events.

Advancements in computational methodology that aim to enable this method to macromolecules have been developed (Brokaw, Haas, and Chu, JCTC, 2009) and our goal is to test and apply this approach to model large scale protein conformational change. In particular, we propose to employ the open-to-closed transition of a type II topoisomerase. Each protein system is composed of ~150,000 atoms and molecular dynamics simulations will be performed using NAMD, which scales very well for such systems to hundreds of cores. The results of MD will be interfaced with CHARMM, in which the reaction path optimization code is implemented. We plan to use 64-128 cores for the MD simulation of each replica and use 25-50 replicas along the chain. Therefore, the typical job size could be up to 1600-6400 cores. This method is implemented in CHARMM as parallel-distributed replicas using MPI. Based on the weak coupling nature of this algorithm, only a small amount of communication is required between replicas.