| NSF Org: |
CHE Division Of Chemistry |
| Recipient: |
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| Initial Amendment Date: | August 28, 2013 |
| Latest Amendment Date: | June 28, 2018 |
| Award Number: | 1265929 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Evelyn Goldfield
CHE Division Of Chemistry MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | September 1, 2013 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $585,000.00 |
| Total Awarded Amount to Date: | $585,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
6100 MAIN ST Houston TX US 77005-1827 (713)348-4820 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
6100 Main Street Houston TX US 77005-1827 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
OFFICE OF MULTIDISCIPLINARY AC, Software Institutes |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
Collaborative Research: SI2-CHE
ExTASY Extensible Tools for Advanced Sampling and analYsis
An international team consisting of Cecilia Clementi(Rice University), Mauro Maggioni (Duke University) Shantenu Jha (Rutgers University), Glenn Martyna (BM T. J. Watson Laboratory ), Charlie Laughton (University of Nottingham), Ben Leimkuhler ( University of Edinburgh), Iain Bethune (University of Edinburgh) and Panos Parpas(Imperial College) are supported through the SI2-CHE program for the development of ExTASY: Extensible Toolkit for Advanced Sampling and analYsis, a conceptual and software framework that provides a step-change in the sampling of the conformational space of macromolecular systems. Specifically, ExTASY is a lightweight toolkit to enable first-class support for ensemble-based simulations and their seamless integration with dynamic analysis capabilities and ultra-large time step integration methods, whilst being extensible to other community software components via well-designed and standard interfaces.
The primary impacts of this project are in the biological sciences. This software advances our understanding of biologically important systems, as it can be used to obtain fast and accurate sampling of the conformational dynamics of stable proteins; a prerequisite for the accurate prediction of thermodynamic parameters and biological functions. It also allows tackling systems like intrinsically disordered proteins, which can be beyond the reach of classical structural biology. Along with the research itself, the PIs are involved with outreach programs to attract high school students to science.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The main outcome of this award was the development of a software package for efficient sampling of the conformational space of a complex biomolecule, such as a protein.
The sampling of a macromolecule configurational space is very important to determine its thermodynamic, kinetic, and functional properties. However, it is a very computationally demanding task as usually biomolecules are characterized by multiple states separated by large free energy barriers, and the process of crossing a barrier in a molecular dynamic simulation is a rare event that may require to simulate longer than computationally feasible. For this reason, alternative approaches have been developed to speed the sampling and the barrier crossing events. In this context, we have developed an "adaptive sampling" method that run multiple molecular dynamic trajectories in parallel and periodically stops the trajectories and restarts them from different points among the sampled space. We have tested different variants of adaptive sampling strategies and we have shown that we can achieve speed-up of a few order of magnitudes in the sampling of protein folding/unfolding processes.
Besides the method development and the testing of different strategies for this adaptive sampling approach, in collaboration with Shantenu Jha (Rutgers University) we have implemented the method in a software package that is available to the community. The software can be run on high performance computing resources and allows to study large and complex biomolecular systems over biologically relavent timescales.
Last Modified: 01/22/2020
Modified by: Cecilia Clementi
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