ArticleBriefings in bioinformatics2023
Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Four IgG Antibodies and Protein G Are Shapeshifters.International journal of molecular sciences · 2026Article
- Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.Mass spectrometry reviews · 2026Review
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 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
- The catalytic domain of free or ligand bound histone deacetylase 4 occurs in solution predominantly in closed conformation.Protein science : a publication of the Protein Society · 2024Article
- Collision cross section measurement and prediction methods in omics.Journal of mass spectrometry : JMS · 2023Review
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Authors and funding
4 authors.
Funding
Abstract
Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).
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Registered trials
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