Fiche publication
Date publication
février 2026
Journal
Nature communications
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CARAPITO Christine
Tous les auteurs :
Bouwmeester R, Nameni A, Declercq A, Devreese R, Velghe K, Gorshkov V, Penanes PA, Kjeldsen F, Rompais M, Carapito C, Gabriels R, Martens L
Lien Pubmed
Résumé
While LC retention time prediction of peptides and their modifications has proven useful, widespread adoption and optimal performance are hindered by variations in experimental parameters. These variations can render retention time prediction models inaccurate and dramatically reduce the value of predictions for identification, validation, and DIA spectral library generation. To date, mitigation of these issues has been attempted through calibration or by training bespoke models for specific experimental setups, with only partial success. We here demonstrate that transfer learning can successfully overcome these limitations by leveraging pre-trained model parameters. Remarkably, this approach can even fit highly performant models to substantially different peptide modifications and LC conditions than those on which the model was originally trained. This impressive adaptability of transfer learning makes it a highly robust solution for accurate peptide retention time prediction across a very wide variety of imaginable proteomics workflows.
Référence
Nat Commun. 2026 02 10;:
