More Power to the Many: Scalable Ensemble-based Simulations and Data Analysis

Shantenu Jha, Rutgers, the State University of New Jersey

Usage Details

Andre Merzky, Shantenu Jha, John Chodera, Philip Fowler, Shunzhou Wan, Stefan Zasada, David Wright, Vivekanandan Balasubramanian, Eugen Hruska, Srinivas Mushnoori, Alexander Patronis, Agastya Bhati, Jumana Dakka, Alessio Angius, Kristof Farkas-Pall, Maxime Vassaux, Christian Feld, Robert Sinclair, Hongyan Wang, Franklin Bettencourt, Abi Malik, Christian Suess, Fouad Husseini, Qingfen Yu, Adrian Devitt-Lee, Keverne Louison, Jayvee Abella, Alexander Gheorghiu

Glutamate receptors, and understanding their binding characteristics, are of fundamental biomedical importance as they mediate neuronal signaling. This project proposes to characterize and understand glutamate binding to the N-methyl-D-aspartate receptor (NMDAr), a member of the glutamate receptor family of proteins, with potential profound consequences for neuroscience and pharmacology. However, the characterization of the configurational landscape of NMDAr is a high performance computing problem. It requires simulations with timescales and system sizes well beyond any that have previously been undertaken. The project will use the petascale computing capabilities of Blue Waters to study such a system, using new sampling methods and original computing and data processing techniques.

The project will use molecular dynamics (MD) simulations to study this macromolecular system. However, it remains a challenge to obtain an adequate sampling of the configurational space of complex chemical systems to accurately describe the structural properties of important substates, their relative propensities, and accessible transitions between them. The project proposes to use a novel software framework that on the right computational resource makes a step-change in our ability to sample the conformational space of macromolecules by MD. The project will study a protein of great biomedical relevance that exemplifies these issues, namely the ligand binding domain (LBD) of NMDAr. The idea at the core of the software strategy is similar to many other multiscale methods—such as umbrella sampling, metadynamics, adaptive biasing methods, or transition path sampling: instead of one or a few long MD trajectories being run, many (hundreds or thousands) of short trajectories may be simulated concurrently. Information is extracted from these very large datasets using sophisticated data reduction and analysis methods, and the coarse-grained information—which embodies the chemical insight necessary to understand the system, e.g. an approximate free energy—is used to refine the way in which further trajectories are generated (i.e., how we sample). Results from the analysis of the space sampled are then used in an iterative process to further direct the search of the conformational space (i.e., where we sample). This Blue Waters allocation will allow the project to access a total of 2.7 milliseconds of simulation of the NMDAr LBD system. With the three orders of magnitude (at least) speed-up in sampling allowed by our methodology with respect to plain MD, the project will be able to map the configurational landscape of this protein relevant for conformational dynamics up to a timescale of seconds, that is, to completely characterize the role of the ligand binding domain in the biological function and mechanism of NMDAr.