High-performance weighted ensemble software for simulation of complex bio-events (Renewal) 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. The WESTPA software package is a powerful ?meta tool? which can make possible computations which otherwise would be impossible within a given computing budget - while letting researchers continue to use simulation engines and models of their choice. WESTPA controls existing dynamics engines by orchestrating up to thousands of trajectories run natively by those packages at any scale (e.g., Gromacs, Amber, BioNetGen, MCell) using a ?weighted ensemble? strategy. Not only does WESTPA automatically 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 and equilibrium observables in significantly less computing time than would be required in ordinary parallelization.
The aims of the proposal are (i) to optimize WESTPA for cloud-computing, improving its ease of use, and developing highly scalable data storage analysis; (ii) to expand WESTPA?s base-computing power via algorithmic improvements; and (iii) to demonstrate the effectiveness of WESTPA through a series of ?showcase? examples from molecular to cellular scale using a variety of dynamics engines. Completion of the aims will enable the investigators, their experimental and computational collaborators, and users throughout the world to make a wide range of contributions to the burgeoning field of biological simulation at multiple scales.

Public Health Relevance

The project will provide open-source software to enhance the power of biological 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, making possible computations that otherwise would be out of reach for typical research groups. Additionally, research to be performed directly by the investigators is designed to yield insights into cancer and viral 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 #
5R01GM115805-06
Application #
9985850
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Lyster, Peter
Project Start
2015-08-01
Project End
2023-06-30
Budget Start
2020-07-01
Budget End
2021-06-30
Support Year
6
Fiscal Year
2020
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
15260
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
Harris, Leonard A; Hogg, Justin S; Tapia, José-Juan et al. (2016) BioNetGen 2.2: advances in rule-based modeling. Bioinformatics 32:3366-3368

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