High Accuracy Computational Methods for Biomolecular Nuclear Magnetic Resonance Spectroscopy

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High accuracy computational methods for biomolecular nuclear magnetic resonance spectroscopy Nuclear magnetic resonance (NMR) spectroscopy is one of the most important condensed phase probes of composition, structure and dynamics of biomolecules and bio-organic species. NMR observables such as chemical shifts and spin-spin splittings can be measured to very high accuracy, and are sensitive both to the functional groups that are present and to their detailed geometries and chemical environment. As such these NMR measurements could be used to develop protein structures with a quality equivalent to high resolution X-ray crystallography but in their native aqueous environments. The connection to structure, while true in principle, is nevertheless sometimes difficult to reveal in practice through direct assignment of the spectrum. Simulation methods that accurately predict spectral observables from structure are a key goal for spectral assignment. Such methods are even more crucial for the inverse problem of realizing high quality NMR structures of folded proteins from spectra, and as powerful restraints for determining the structural ensembles of intrinsically disordered proteins (IDPs). Existing approaches to this problem typically rely on semi-empirical heuristics, and while they have achieved considerable success, they also reveal limitations that significantly degrade the quality of structural prediction. In this proposal, we will develop a new, first principles quantum mechanical (QM) based approach to simulation of NMR spectral observables for condensed phase biomolecules and bio-organics. Rapid prototyping of new QM methods will be enabled by the development of a distinctive in-silico NMR laboratory that applies finite magnetic fields and nuclear spins. From this capability, new methods for chemical shifts and spin-spin splittings will emerge that offer improved accuracy versus cost tradeoffs, and will be employed to populate databases that reflect protein relevant and energetically accessible environments. With such data, both artificial neural networks and Bayesian supervised learning approaches will determine a quantitative relationship between structure and computed NMR observable, and the resulting eQMCalculator will be tested on the refinement of folded proteins and creation of structural ensembles for IDPs.