Structure
Volume 27, Issue 11, 5 November 2019, Pages 1721-1734.e5
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Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints

https://doi.org/10.1016/j.str.2019.08.012Get rights and content
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Highlights

  • A new computational framework for paramagnetic NMR data is introduced in Rosetta

  • Using PCS, RDC, and PRE data with CSs and NOEs allows integrative structure modeling

  • RosettaNMR leverages de novo prediction and modeling of protein and ligand complexes

  • Paramagnetic data generate high-accuracy (≤2 Å) models for 50% of benchmark proteins

Summary

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.

Keywords

Rosetta
NMR spectroscopy
paramagnetic NMR
integrative modeling
protein structure prediction
sparse experimental restraints

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