Collisional Cross-Section Prediction for Multiconformational Peptide Ions with IM2Deep
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| Název: | Collisional Cross-Section Prediction for Multiconformational Peptide Ions with IM2Deep |
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| Autoři: | Robbe Devreese, Alireza Nameni, Arthur Declercq, Emmy Terryn, Ralf Gabriels, Francis Impens, Kris Gevaert, Lennart Martens, Robbin Bouwmeester |
| Rok vydání: | 2025 |
| Témata: | Biochemistry, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, tailored architecture allows, single observed conformation, https :// github, exhibit multiple conformations, predict ccs values, multiconformational peptide ions, accurate ccs prediction, peptide ions, training data, source license, publicly available, proteomics data, permissive open, multiconformational peptides, improved version, gas phase, feature finding, downstream analysis, comprehensive set, collisional cross |
| Popis: | Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysis of proteomics data, for example for identification or quantification through feature finding. Here, we present an improved version of IM2Deep that is trained on a carefully curated data set to predict CCS values of multiconformational peptides. The training data is derived from a large and comprehensive set of publicly available data sets. This comprehensive training data set together with a tailored architecture allows for the accurate CCS prediction of multiple peptide conformational states. Furthermore, the enhanced IM2Deep model also retains high precision for peptides with a single observed conformation. IM2Deep is publicly available under a permissive open-source license at https://github.com/compomics/IM2Deep. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | unknown |
| DOI: | 10.1021/acs.analchem.5c01142.s001 |
| Dostupnost: | https://doi.org/10.1021/acs.analchem.5c01142.s001 https://figshare.com/articles/journal_contribution/Collisional_Cross-Section_Prediction_for_Multiconformational_Peptide_Ions_with_IM2Deep/29504991 |
| Rights: | CC BY-NC 4.0 |
| Přístupové číslo: | edsbas.20FFA028 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysis of proteomics data, for example for identification or quantification through feature finding. Here, we present an improved version of IM2Deep that is trained on a carefully curated data set to predict CCS values of multiconformational peptides. The training data is derived from a large and comprehensive set of publicly available data sets. This comprehensive training data set together with a tailored architecture allows for the accurate CCS prediction of multiple peptide conformational states. Furthermore, the enhanced IM2Deep model also retains high precision for peptides with a single observed conformation. IM2Deep is publicly available under a permissive open-source license at https://github.com/compomics/IM2Deep. |
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| DOI: | 10.1021/acs.analchem.5c01142.s001 |