There is a silicon ceiling that ultimately limits many, if not most, types of dynamical biological simulations. That is, even the world's most powerful computers cannot generate sufficiently long simulations, whether for atomistic models of proteins or for realistic models of cell behavior. In many cases, the key events may occur beyond simulation timescales - such as protein folding, conformational transitions of proteins, assembly of protein complexes, or transitions of cell behavior from healthy to pathological states. We therefore propose a response to PA-14-156, Extended Development, Hardening and Dissemination of Technologies in Biomedical Computing, Informatics and Big Data Science (RO1), in which we will continue to enhance the WESTPA software package. WESTPA is a tool for controlling other software tools: it orchestrates up to thousands of trajectories run natively by other software at any scale (e.g., Gromacs, Amber, BioNetGen, MCell) using a weighted ensemble strategy. Not only does WESTPA parallelize the use of dynamics engines - but because of the statistical process by which trajectories are added and removed, WESTPA can obtain estimates of key kinetic as well as equilibrium observables in significantly less computing time than would be required in ordinary parallelization.
The aims of the proposal are to improve the ease of use and interoperability of WESTPA; to improve its performance and reliability; to demonstrate the effectiveness of WESTPA through a series of showcase examples from molecular to cellular scale using a variety of dynamics engines; and to improve instructional materials based on the showcase examples.

Public Health Relevance

In response to a call from NIH, the aims to provide open-source software to enhance the power of simulations at any scale (e.g. molecular, cellular) for a potentially large user base. Thus, the primary impact will be to facilitate key segments of the burgeoning field of computational biomedical research. Additionally, research to be performed directly by the investigators is designed to yield insights into cancer and neurological processes with potential to enhance drug design efforts.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
3R01GM115805-02S1
Application #
9268876
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Ravichandran, Veerasamy
Project Start
2015-08-01
Project End
2019-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Pittsburgh
Department
Biology
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Grossfield, Alan; Patrone, Paul N; Roe, Daniel R et al. (2018) Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]. Living J Comput Mol Sci 1:
Debiec, Karl T; Whitley, Matthew J; Koharudin, Leonardus M I et al. (2018) Integrating NMR, SAXS, and Atomistic Simulations: Structure and Dynamics of a Two-Domain Protein. Biophys J 114:839-855
Nunes-Alves, Ariane; Zuckerman, Daniel M; Arantes, Guilherme Menegon (2018) Escape of a Small Molecule from Inside T4 Lysozyme by Multiple Pathways. Biophys J 114:1058-1066
DeGrave, Alex J; Ha, Jeung-Hoi; Loh, Stewart N et al. (2018) Large enhancement of response times of a protein conformational switch by computational design. Nat Commun 9:1013
Zuckerman, Daniel M; Chong, Lillian T (2017) Weighted Ensemble Simulation: Review of Methodology, Applications, and Software. Annu Rev Biophys 46:43-57
Chong, Lillian T; Saglam, Ali S; Zuckerman, Daniel M (2017) Path-sampling strategies for simulating rare events in biomolecular systems. Curr Opin Struct Biol 43:88-94
Cerutti, David S; Debiec, Karl T; Case, David A et al. (2017) Links between the charge model and bonded parameter force constants in biomolecular force fields. J Chem Phys 147:161730
Saglam, A S; Wang, D W; Zwier, M C et al. (2017) Flexibility vs Preorganization: Direct Comparison of Binding Kinetics for a Disordered Peptide and Its Exact Preorganized Analogues. J Phys Chem B 121:10046-10054
Morel, Penelope A; Lee, Robin E C; Faeder, James R (2017) Demystifying the cytokine network: Mathematical models point the way. Cytokine 98:115-123
Debiec, Karl T; Cerutti, David S; Baker, Lewis R et al. (2016) Further along the Road Less Traveled: AMBER ff15ipq, an Original Protein Force Field Built on a Self-Consistent Physical Model. J Chem Theory Comput 12:3926-47

Showing the most recent 10 out of 17 publications