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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF ARKANSAS SYSTEM
Doing Business As Name:University of Arkansas
PD/PI:
  • Mahmoud Moradi
  • (479) 575-6459
  • moradi@uark.edu
Award Date:01/07/2020
Estimated Total Award Amount: $ 650,000
Funds Obligated to Date: $ 650,000
  • FY 2020=$650,000
Start Date:03/01/2020
End Date:02/28/2025
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.049
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CAREER: Riemannian Reformulation of Collective Variable Based Free Energy Calculation Methods
Federal Award ID Number:1945465
DUNS ID:191429745
Parent DUNS ID:055600001
Program:Chem Thry, Mdls & Cmptnl Mthds
Program Officer:
  • Walter Ermler
  • (703) 292-2919
  • waermler@nsf.gov

Awardee Location

Street:1125 W. Maple Street
City:Fayetteville
State:AR
ZIP:72701-3124
County:Fayetteville
Country:US
Awardee Cong. District:03

Primary Place of Performance

Organization Name:University of Arkansas
Street:119 Chemistry Building
City:FAYETTEVILLE
State:AR
ZIP:72701-3073
County:Fayetteville
Country:US
Cong. District:03

Abstract at Time of Award

Mahmoud Moradi of University of Arkansas is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry. Professor Moradi develops chemical theories that improve the accuracy of computational methods used for investigating the functions of biomolecules such as proteins at the molecular level. These theories specifically improve geometric models that describe how proteins change their shape and how such changes change the protein's behavior. Professor Moradi bridges the gap between the state-of-the-art computational methods and biomolecular applications by developing rigorous theories. Moradi and his research group pursue advanced geometric tools necessary to provide a robust molecular picture of protein changes. This research enables researchers to understanding biomolecular processes involved in protein function and leads to a better understanding of disease and a more efficient computational framework for drug design and discovery. By taking advantage of state-of-the-art supercomputers incorporating advanced computational methods, statistical physics techniques, and statistical analysis tools this research will quantify conformational changes of membrane proteins that will impact biological and biomedical sciences. This research lies at the intersection of Biology, Physics, Chemistry, Mathematics, Statistics, and Computer Science, and offers a firsthand experience in interdisciplinary science to students and trainees, in particular those underrepresented in science. The research outcomes also provide new materials for teaching at both the graduate and undergraduate level. The project equips the high school science teachers with a user-friendly molecular dynamics visualization platform to illustrate the dynamic nature of biomolecular processes to their students. In addition, interdisciplinary training opportunities are provided for undergraduate students from various departments who are interested in receiving short-term and long-term training in biomolecular simulations. Professor Moradi and his research group develop a Riemannian framework for free energy calculation methods for biomolecular simulations. Riemannian geometric tools are employed to both modify previously established non-Riemannian algorithms and design novel Riemannian algorithms aimed at describing protein dynamics. A detailed picture of such phenomena can currently be addressed using all-atom molecular dynamics simulations, that are often computationally intensive. These simulations cannot describe many biomolecular processes such as large-scale protein conformational changes due to their timescale differences. Although various enhanced sampling techniques have been developed over the past few decades to address this “timescale gap”, the application of these methods to biologically relevant systems remain challenging due to both computational costs and methodological flaws. Moradi and his research group address a molecular level characterization of conformational changes of membrane proteins using free energy calculation methods and path-finding algorithms within a Riemannian framework. Methodologies that are both robust and result in transition pathways and free energies that are invariant under coordinate transformations may result. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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