Solution NMR spectroscopy provides atomistic measurements of proteins in a native-like biophysical state. Because these measurements are ensemble averages, it also has the potential to report on conformational diversity. However, conventional NMR structure determination typically converts experimental observables into restraints for molecular dynamics, which encode information on the mean structure but do not retain information on the underlying conformational distribution. Ensemble selection has long been proposed as an alternative, whereby experimental observables are compared directly with candidate conformers generated independently of the measurements. This allows population distributions to be inferred from the data. However, few such methods have incorporated NOESY - the richest source of structural information in protein NMR - data, due to challenges in the quantitative comparison of experimental and back-calculated spectra. To address this challenge, we previously introduced the CoMAND method, demonstrating that quantitative agreement is practical for NOESY spectra with bespoke heteronuclear editing schemes. Here we extend this approach into a framework for direct inference of protein ensembles within a flexible ensemble-selection architecture incorporating multiple classes of NMR observables. We introduce a quantitative scoring framework for comparing experimental and back-calculated observables and combine it with regularized ensemble selection and Monte Carlo simulated annealing. Integration with the OpenMM molecular dynamics engine allows conformational pools to be generated using established molecular simulation methods. Applied to human ubiquitin, the resulting ensemble provides simultaneous agreement with NOESY, residual dipolar coupling and scalar coupling data while retaining conformational diversity supported by experiment.
Coles, M.
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