ReviewInternational journal of molecular sciences2023
Recent Advances in NMR Protein Structure Prediction with ROSETTA.
Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Integrative frameworks for plastic biodegradation in insect-microbiome systems: mechanistic insights, emerging multi-omics and enzyme engineering perspectives.Biodegradation · 2026Review
- Bridging structure and function: artificial intelligence-based modelling of kidney proteins.Nature reviews. Nephrology · 2026Review
- Integrative Structural Modeling of Intrinsically Disordered Regions in a Human HDAC2 Chromatin Remodeling Complex.bioRxiv : the preprint server for biology · 2026Article
- Therapeutic Effect of a Recombinant Human Fibronectin Construct in Skeletal Muscle Repair and Oxidative Stress.International journal of molecular sciences · 2025Article
- Probing Biomolecular Interactions with Paramagnetic Nuclear Magnetic Resonance Spectroscopy.Chembiochem : a European journal of chemical biology · 2025Review
- Binding Free Energy Analysis of Colicin D, E3 and E8 to Their Respective Cognate Immunity Proteins Using Computational Simulations.Molecules (Basel, Switzerland) · 2025Article
- SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model.Computational and structural biotechnology journal · 2024Article
- AI-Driven Deep Learning Techniques in Protein Structure Prediction.International journal of molecular sciences · 2024Review
- The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins.Nature chemical biology · 2024Review
- Progress, Challenges and Opportunities of NMR and XL-MS for Cellular Structural Biology.JACS Au · 2024Review
- Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.Frontiers in cellular and infection microbiology · 2024Review
- Deciphering olfactory receptor binding mechanisms: a structural and dynamic perspective on olfactory receptors.Frontiers in molecular biosciences · 2024Review
- Dynamic Structures of Bioactive Proteins as Determined by Nuclear Magnetic Resonance.International journal of molecular sciences · 2023Article
- The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.Proteins · 2023Article
- The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15.bioRxiv : the preprint server for biology · 2023Article
- Analysis of the Structural Dynamics of Proteins in the Ligand-Unbound and -Bound States by Diffracted X-ray Tracking.International journal of molecular sciences · 2023Review
- Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol.International journal of molecular sciences · 2023Article
- NMR-Chemical-Shift-Driven Protocol Reveals the Cofactor-Bound, Complete Structure of Dynamic Intermediates of the Catalytic Cycle of Oncogenic KRAS G12C Protein and the Significance of the MgInternational journal of molecular sciences · 2023Article
Corrections and comments
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.
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Registered trials
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