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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:INSTITUTE FOR PROTEIN INNOVATION, INC.
Doing Business As Name:INSTITUTE FOR PROTEIN INNOVATION, INC.
PD/PI:
  • Rob Meijers
  • (617) 651-8328
  • rob.meijers@proteininnovation.org
Co-PD(s)/co-PI(s):
  • Christopher D Bahl
Award Date:09/21/2021
Estimated Total Award Amount: $ 409,152
Funds Obligated to Date: $ 409,152
  • FY 2021=$409,152
Start Date:01/01/2022
End Date:12/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CRCNS US-German Research Proposal: Combining computational modeling and artificial intelligence to understand receptor function in physiology and disease
Federal Award ID Number:2113030
DUNS ID:080819863
Program:CRCNS-Computation Neuroscience
Program Officer:
  • Kenneth Whang
  • (703) 292-5149
  • kwhang@nsf.gov

Awardee Location

Street:4 Blackfan Circle
City:BOSTON
State:MA
ZIP:02115-5701
County:Boston
Country:US
Awardee Cong. District:07

Primary Place of Performance

Organization Name:Institute for Protein Innovation
Street:4 Blackfan Circle
City:Boston
State:MA
ZIP:02115-5701
County:Boston
Country:US
Cong. District:07

Abstract at Time of Award

G-protein coupled receptors (GPCRs) are cell surface receptors that translate external signals (e.g. binding of drugs) into the activation of processes inside the cell. The three-dimensional shape of the receptor changes depending on the molecule that is binding to it and the external environment (e.g. injured versus normal tissue). To sample all possible conformations of a GPCR, a powerful computational simulation approach is used that combines traditional molecular dynamics simulations with artificial intelligence. This novel approach is applied to opioid receptors, a prominent subfamily of GPCRs. These receptors mediate pain relief in injured and inflamed tissue, but also have adverse side effects in healthy tissue, such as depression of breathing or sedation in the brain. The opioid receptors change their conformation as a result of the inflamed environment. If there were drugs that selectively targeted this "pathological" form of opioid receptors, they would treat acute pain without affecting receptors in a healthy environment, and thereby avoid the adverse side effects observed for conventional opioids. This approach can be used in the future to discover safer pain killers that only affect opioid receptors in injured tissues. The innovative combination of molecular dynamics simulations with artificial intelligence enables the sampling in silico of large numbers of opioid receptor conformations. Efficient simulation will take advantage of a recently developed artificial neural network approach that efficiently represents the dynamics of high-dimensional molecular interactions. The corresponding mathematical theory is not restricted to molecular simulation; in principle, it could apply to any generated Markov process. The molecular simulations will provide insight into the effects of environmental factors such as pH and the presence of free radicals and will be used to suggest experiments in the laboratory. Computational models will be tested in the lab using opioid receptor mutants and antibodies that can lock the receptors into an active state. Antibodies and miniproteins mimicking G-protein subunits will be generated using a combination of protein design and yeast display technologies. Once these methods have been established for opioid receptors, they may be extended to GPCRs involved in other nervous system disorders. Ultimately, the combination of novel computational methods with in vitro experiments will enable a systematic study of GPCR signaling in healthy versus injured environments. A companion project is being funded by the Federal Ministry of Education and Research, Germany (BMBF). 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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