This proposal is to develop MELD, a computational Bayesian accelerator that ?melds? together molecular dynamics simulations with external knowledge. It is novel in harnessing information that has not been usable before ? because it is too sparse, noisy, ambiguous, combinatoric, or too corrupted for traditional approaches. In contrast to the high-certainty restraints traditionally used in MD simulations, MELD leverages a much broader range of real-world high-uncertainty restraints. The first specific aim is to incorporate such information in protein structure determination, in several collaboration projects with experimentalists who perform solution x-ray scattering, ESR, and high-throughput alanine scanning structures of peptide protein complexes.
The second aim i s to also harness information about processes, trajectories, and dynamic routes to speed the identification of protein states. MELD promises to extend physics-based simulations for determining larger protein structures, for folding larger proteins, for binding more flexible ligands, and for exploring larger mechanistic actions, than current MD simulation methods can handle.

Public Health Relevance

Biomedical research and pharmaceutics depend on detailed understanding of the structures and motions of proteins. Molecular dynamics simulations provide the most detailed descriptions possible, however they cannot yet describe average to large sized protein structures or motions within a reasonable time frame. We propose to develop a new physics-based computational accelerator for Molecular Dynamics, called MELD, which incorporates many types of relevant external information that was too vague and difficult to compute to have been practically useful before.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM125813-02
Application #
9618886
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Lyster, Peter
Project Start
2018-01-01
Project End
2021-12-31
Budget Start
2019-01-01
Budget End
2019-12-31
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
State University New York Stony Brook
Department
Miscellaneous
Type
Organized Research Units
DUNS #
804878247
City
Stony Brook
State
NY
Country
United States
Zip Code
11794