Elucidating Biomolecular Dynamics using Magnetic Resonance Augmented by Machine Learning and Quantum Technologies

About This Grant

PROJECT SUMMARY / ABSTRACT This proposal, “Elucidating biomolecular dynamics using magnetic resonance augmented by machine learning and quantum technologies” addresses the need for experimental methods to probe the dynamics of large biomolecules in near-physiological conditions in a straightforward and rapid manner. Indeed, many post- translational modifications employed by cells to regulate fundamental biological processes remain poorly understood. Magnetic resonance techniques are well-suited to elucidate these mechanisms, but their application to large biomolecules is limited by sensitivity and resolution constraints. Dr. Seetharam proposes a two-pronged approach to overcome these limitations: (i) develop a novel analysis workflow for magnetic resonance experiments that can extract information from data uninterpretable by humans, and (ii) develop protocols for a novel nanoscale magnetic resonance platform that can operate at high molecular weights and low concentrations. This approach will be conducted through three specific aims that leverage machine learning, quantum computing, and quantum sensing methods. Successful completion of these aims will greatly expand the application of magnetic resonance techniques by enabling an interpretability to information density trade-off in protocol design, bypassing the molecular weight bottleneck, and circumventing the sensitivity limitation, thereby opening a high-resolution window into biomolecular dynamics in near-physiological conditions. Dr. Seetharam is uniquely positioned to perform the proposed work given his interdisciplinary background bridging research and policy, physics and engineering, theory and experiment. He is currently a postdoctoral researcher under the mentorship of Prof. Mikhail Lukin, a pioneer in quantum computing and nanoscale quantum sensing, at Harvard University and Prof. Haribabu Arthanari, a leading expert in biomolecular magnetic resonance, at the Dana-Farber Cancer Institute (DFCI). The proposed research will be conducted at Harvard University and DFCI with support from an exceptional scientific advisory committee experienced in machine learning for magnetic resonance, nanoscale magnetic resonance, single-molecule biophysics, biochemistry, cell biology, and systems biology. This K25 award will provide Dr. Seetharam with the requisite training in machine learning and biological techniques needed to successfully transition to an independent career in magnetic resonance while simultaneously addressing an unmet need in the study of biological processes.