High accuracy protein structures from minimal sparse paramagnetic solid-state NMR restraints
There is a pressing need for new computational tools to integrate data from diverse experimental approaches in structural biology. We present a strategy that combines sparse paramagnetic solid-state NMR restraints with physics-based atomistic simulations. Our approach explicitly accounts for uncertainty in the interpretation of experimental data through the use of a semi-quantitative mapping between the data and the restraint energy that is calibrated by extensive simulations. We apply our approach to solid-state NMR data for the model protein GB1 labeled with Cu2+-EDTA at six different sites. We are able to determine the structure to ca. 1 [A] accuracy within a single day of computation on a modest GPU cluster. We further show that in 4 of 6 cases, the data from only a single paramagnetic tag are sufficient to fold the protein to high accuracy.