Ionmob: a Python package for prediction of peptide collisional cross-section values
Rattachement africain : de, be. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
MOTIVATION: Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing. RESULTS: We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task. AVAILABILITY AND IMPLEMENTATION: The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Ionmob: a Python package for prediction of peptide collisional cross-section values
- Date Crossref
- 04/08/2023
- Éditeur
- Oxford University Press (OUP)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Johannes Gutenberg University Mainz Institute of Computer Science pays non établi dans la noticeUniversité ou école supérieure
-
University Medical Center of the Johannes Gutenberg University Mainz Institute for Immunology pays non établi dans la noticeÉtablissement de santé
-
Helmholtz Institute Mainz pays non établi dans la noticeOrganisme public
-
Ghent University Department of Biomolecular Medicine pays non établi dans la noticeUniversité ou école supérieure
-
VIB-UGent Center for Medical Biotechnology pays non établi dans la noticeStructure de recherche
-
Helmholtz-Institute for Translational Oncology (HI-TRON) Immunoproteomics Unit pays non établi dans la noticeStructure de recherche
Institute of Computer Science — Johannes Gutenberg University Mainz, Institute for Immunology — University Medical Center of the Johannes Gutenberg University Mainz et Helmholtz Institute Mainz, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.