| NSF Org: |
MCB Division of Molecular and Cellular Biosciences |
| Recipient: |
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| Initial Amendment Date: | April 12, 2016 |
| Latest Amendment Date: | April 30, 2020 |
| Award Number: | 1552471 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Jaroslaw Majewski
MCB Division of Molecular and Cellular Biosciences BIO Directorate for Biological Sciences |
| Start Date: | June 1, 2016 |
| End Date: | May 31, 2022 (Estimated) |
| Total Intended Award Amount: | $642,400.00 |
| Total Awarded Amount to Date: | $680,279.00 |
| Funds Obligated to Date: |
FY 2017 = $123,742.00 FY 2018 = $127,455.00 FY 2019 = $131,277.00 FY 2020 = $173,095.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1 BROOKINGS DR SAINT LOUIS MO US 63130-4862 (314)747-4134 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
660 South Euclid Avenue St. Louis MO US 63110-1093 |
| 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): |
Molecular Biophysics, PHYSICS OF LIVING SYSTEMS, Cross-BIO Activities |
| Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT 01001819DB NSF RESEARCH & RELATED ACTIVIT 01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT |
| 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): | 47.074 |
ABSTRACT
Title: CAREER: FAST methods for protein folding and design
Proteins are molecular machines that are largely responsible for processes as varied as digestion of food to building new components of cells. Many proteins are capable of spontaneously folding from an extended chain into compact, functional machines. Once folded, proteins continue to undergo motions that are related to their stability and function. Understanding the functional relevance of these motions remains extremely challenging because it is difficult to observe movement on the atomic scale and provide the necessary structural detail to connect these motions with a protein's function. The objectives of this project are 1) to develop powerful algorithms for simulating these protein motions, 2) apply these algorithms to understand how proteins fold, and 3) to combine these algorithms with biochemical experiments to design proteins that are more stable than their natural counterparts. Completion of this research will lay the foundation for future efforts to understand the role of protein motions in processes like cellular communications and to design proteins for applications such as the synthesis of biofuels. In concert with these research objectives, the PI will develop a python programming boot camp to teach students in biology the basic programming skills required to analyze their own data, providing a starting point for more sophisticated integration of computation and experiments and opening new job opportunities in the STEM fields.
This project will identify general properties of free energy landscapes of proteins from simulation datasets created with specialized hardware and leverage them to empower similar studies with commodity hardware. This work will be guided by the hypothesis that leveraging ideas from optimization theory regarding exploration/exploitation tradeoffs will allow efficient conformational searches. Based on preliminary analyses, the PI's lab has already begun to prototype a new algorithm, referred to as fluctuation amplification of specific traits, or FAST. Further developing this algorithm, demonstrating its power, and disseminating it to the broader scientific community will lay a foundation for understanding and designing protein's conformational ensembles. Specific goals include: 1) develop the fluctuation amplification of specific traits (FAST) algorithm to efficiently explore a protein's conformational space, 2) test whether FAST can fold proteins, and 3) test whether FAST can reveal opportunities for designing stabilized proteins without perturbing their functions.
This project is jointly funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences and the Physics of Living Systems Program in the Division of Physics in the Directorate of Mathematical and Physical Sciences.
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.
Proteins are the molecular machines responsible for many of the active processes that we associate with life, from muscle contraction to sensing light in the eye and sound in the ear. Like more familiar machines, such as cars, protein function depends on moving parts. However, proteins are so small that there is no microscope that enables scientists to simply watch them in action. Instead, researchers only have isolated snapshots. Learning about how proteins function is then like trying to learn how a car works from a photo of a parked car.
Funding from this grant enabled the development of new computer algorithms capable of simulating proteins’ moving parts with at least 500-fold less computer time, thereby making it tractable to study many more biologically-relevant problems than was previously possible.
Using this computational microscope, the research team succeeded in explaining how changing parts of a protein that are far from where chemical reactions take place results in significant changes to how quickly those reactions proceed and which reactions are possible. The team used this insight to design new protein variants to control these chemical reactions, and experimentally confirmed that the designs worked as intended. The team also became adept at identifying cryptic pockets that are absent in snapshots of proteins but become apparent when watching the moving parts. Experiments confirmed these predictions, and also showed that cryptic pockets provide new opportunities for drug design because engaging them with drug-like molecules can change a proteins function.
In response to the COVID-19 pandemic, the team quickly pivoted to applying these tools to proteins from the SARS-CoV-2 virus to understand why it has outperformed other coronaviruses and to inform the design of new therapeutics. Much progress was made with the help of over a million citizen scientists who contributed their personal computing effort to this work through the Folding@home distributed computing project. In collaboration with the COVID Moonshot, this research identified new inhibitors of crucial viral proteins that could serve as potent coronavirus antivirals. These compounds are now moving towards clinical trials.
In tandem with research, the team engaged in significant community building and outreach efforts. All computer code is shared online, and the team developed new means to share large simulation datasets. Through the Folding@home project, the team also engaged with over a million citizen scientists through a combination of blog posts, Tweets, and discussion forums. Finally, the PI continued his outreach efforts to other visually impaired people, including giving talks and Tweets on his own experiences, mentoring, and creating an online community that meets on a monthly basis.
Last Modified: 10/20/2022
Modified by: Gregory R Bowman
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