TY - JOUR
T1 - Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints
AU - Kuenze, Georg
AU - Bonneau, Richard
AU - Leman, Julia Koehler
AU - Meiler, Jens
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/11/5
Y1 - 2019/11/5
N2 - Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.
AB - Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.
KW - NMR spectroscopy
KW - Rosetta
KW - integrative modeling
KW - paramagnetic NMR
KW - protein structure prediction
KW - sparse experimental restraints
UR - http://www.scopus.com/inward/record.url?scp=85074159123&partnerID=8YFLogxK
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U2 - 10.1016/j.str.2019.08.012
DO - 10.1016/j.str.2019.08.012
M3 - Article
C2 - 31522945
AN - SCOPUS:85074159123
SN - 0969-2126
VL - 27
SP - 1721-1734.e5
JO - Structure
JF - Structure
IS - 11
ER -