ArticleAnalytical chemistry2018
Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.
Article in Analytical chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.Mass spectrometry reviews · 2026Review
- Incorporating Surfaced-Induced Dissociation Mass Spectrometry Data into an AlphaFold-derived deep learning network improves protein structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 2026Article
- Multidose Inline Size-Exclusion Chromatography-Flash Oxidation for Structural Analysis of Dynamic Protein-Ligand Interactions of Antithrombin III and Unfractionated Heparin.Analytical chemistry · 2026Article
- Ion Mobility Mass Spectrometry Guided Modeling with AlphaFold and Rosetta Improves Protein Complex Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.bioRxiv : the preprint server for biology · 2026Article
- Article
- Deep-learning structure elucidation from single-mutant deep mutational scanning.Nature communications · 2025Article
- RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data.Journal of structural biology · 2025Article
- Energy Resolved Mass Spectrometry Data from Surfaced Induced Dissociation Improves Prediction of Protein Complex Structure.Analytical chemistry · 2025Article
- Systematic Fe(II)-EDTA Method of Dose-Dependent Hydroxyl Radical Generation for Protein Oxidative Footprinting.Analytical chemistry · 2023Article
- Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver.Briefings in bioinformatics · 2023Article
- Determining the gas-phase structures of α-helical peptides from shape, microsolvation, and intramolecular distance data.Nature communications · 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
- Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction.Nature communications · 2022Article
- Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation.Analytical chemistry · 2022Article
- Validated determination of NRG1 Ig-like domain structure by mass spectrometry coupled with computational modeling.Communications biology · 2022Article
- Protein Structure Prediction with Mass Spectrometry Data.Annual review of physical chemistry · 2022Review
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4 authors.
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Abstract
In recent years mass spectrometry-based covalent labeling techniques such as hydroxyl radical footprinting (HRF) have emerged as valuable structural biology techniques, yielding information on protein tertiary structure. These data, however, are not sufficient to predict protein structure unambiguously, as they provide information only on the relative solvent exposure of certain residues. Despite some recent advances, no software currently exists that can utilize covalent labeling mass spectrometry data to predict protein tertiary structure. We have developed the first such tool, which incorporates mass spectrometry derived protection factors from HRF labeling as a new centroid score term for the Rosetta scoring function to improve the prediction of protein tertiary structures. We tested our method on a set of four soluble benchmark proteins with known crystal structures and either published HRF experimental results or internally acquired data. Using the HRF labeling data, we rescored large decoy sets of structures predicted with Rosetta for each of the four benchmark proteins. As a result, the model quality improved for all benchmark proteins as compared to when scored with Rosetta alone. For two of the four proteins we were even able to identify atomic resolution models with the addition of HRF data.
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