Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints

Georg Kuenze, Richard Bonneau, Julia Koehler Leman, Jens Meiler

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1721-1734.e5
Issue number11
StatePublished - Nov 5 2019


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

ASJC Scopus subject areas

  • Structural Biology
  • Molecular Biology


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