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High Accuracy Protein Structures from Minimal Sparse Paramagnetic Solid‐State NMR Restraints
Author(s) -
Perez Alberto,
Gaalswyk Kari,
Jaroniec Christopher P.,
MacCallum Justin L.
Publication year - 2019
Publication title -
angewandte chemie
Language(s) - English
Resource type - Journals
eISSN - 1521-3757
pISSN - 0044-8249
DOI - 10.1002/ange.201811895
Subject(s) - paramagnetism , computation , solid state , solid state nuclear magnetic resonance , folding (dsp implementation) , chemistry , computer science , physics , materials science , statistical physics , biological system , computational chemistry , algorithm , nuclear magnetic resonance , condensed matter physics , engineering , electrical engineering , biology
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 Cu 2+ ‐EDTA at six different sites. We are able to determine the structure to 0.9 Å accuracy within a single day of computation on a GPU cluster. We further show that in some cases, the data from only a single paramagnetic tag are sufficient for accurate folding.