ArticleStructure (London, England : 1993)2019
Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints.
Article in Structure (London, England : 1993), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Bridging structure and function: artificial intelligence-based modelling of kidney proteins.Nature reviews. Nephrology · 2026Review
- Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Assisting and accelerating NMR assignment with restrained structure prediction.Communications biology · 2025Article
- Probing Biomolecular Interactions with Paramagnetic Nuclear Magnetic Resonance Spectroscopy.Chembiochem : a European journal of chemical biology · 2025Review
- Modeling membrane geometries implicitly in Rosetta.Protein science : a publication of the Protein Society · 2024Article
- DNAJB6 mutants display toxic gain of function through unregulated interaction with Hsp70 chaperones.Nature communications · 2023Article
- Sparse pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction.Journal of biomolecular NMR · 2023Article
- Recent Advances in NMR Protein Structure Prediction with ROSETTA.International journal of molecular sciences · 2023Review
- Modeling of protein conformational changes with Rosetta guided by limited experimental data.Structure (London, England : 1993) · 2022Article
- Paramagnetic Chemical Probes for Studying Biological Macromolecules.Chemical reviews · 2022Review
- Characterizing conformational ensembles of multi-domain proteins using anisotropic paramagnetic NMR restraints.Biophysical reviews · 2022Review
- Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook.Frontiers in immunology · 2022Review
- Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.Nature communications · 2021Article
- An automated iterative approach for protein structure refinement using pseudocontact shifts.Journal of biomolecular NMR · 2021Article
- An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling.Frontiers in molecular biosciences · 2021Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
- Article
- Macromolecular modeling and design in Rosetta: recent methods and frameworks.Nature methods · 2020Review
- Paramagnetic NMR in drug discovery.Journal of biomolecular NMR · 2020Review
- Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions.Medical microbiology and immunology · 2020Review
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4 authors.
Funding
Abstract
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.
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