Computational and Data Science Approaches to Enhance Biomolecular NMR

NMR is one of the most versatile analytic tools for investigating biomolecules. It can probe structure and dynamics in solution at atomic resolution, even for disordered molecules, and it can quantify the affinity and kinetics of biomolecular interactions and the sites of interaction. It is capable of quantifying individual components of complex mixtures of small molecules, with applications to metabolomics, drug discovery, and clinical diagnostics. The broad goal of this project is to develop computational methods and tools that enhance the application of NMR in these biomedical applications, by improving sensitivity, resolution, lowering the barriers to implementing complex experimental and analytic workflows, and making them more reproducible and the resulting data easier to survey and share. The approach will leverage and enhance the open, international repository for biomolecular NMR data, the Biological Magnetic Resonance Data Bank, and the NMRbox computational platform, comprised of >250 NMR-related software packages and high performance computational and storage resources. Special emphasis is given to the application of NMR for investigating dynamics and disorder in biological macromolecules, which are inaccessible to other methods, and to the quantitative analysis of mixtures of small molecules, for NMR-based screening, metabolomics, and biomarker discovery. Project Number: 1R35GM158031-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: JEFFREY HOCH | Institution: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT, FARMINGTON, CT | Award Amount: $457,875 | Activity Code: R35 | Study Section: Maximizing Investigators' Research Award B Study Section[MRAB] View on NIH RePORTER: https://reporter.nih.gov/project-details/11089170