Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM

Gabriel, W.; Zolg, D. P.; Giurcoiu, V.; Shouman, O.; Prokofeva, P.; Seefried, F.; Bayer, F. P.; Lautenbacher, L.; Soleymaniniya, A.; Schnatbaum, K.; Zerweck, J.; Knaute, T.; Delanghe, B.; Huhmer, A.; Wenschuh, H.; Reimer, U.; Medard, G.; Kuster, B.; Wilhelm, M.

doi:10.1101/2025.11.07.687302

bioRxiv · 10.1101/2025.11.07.687302

Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM

Gabriel, W.·Zolg, D. P.·Giurcoiu, V.·Shouman, O.·Prokofeva, P.·Seefried, F.·Bayer, F. P.·Lautenbacher, L.·Soleymaniniya, A.·Schnatbaum, K.·Zerweck, J.·Knaute, T.·Delanghe, B.·Huhmer, A.·Wenschuh, H.·Reimer, U.·Medard, G.·Kuster, B.·Wilhelm, M.

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Abstract

Post-translational modifications (PTMs) are critical regulators of protein function, yet confidently identifying and localizing PTM sites across proteomes remains a challenging task. Integrating peptide property predictions into spectrum interpretation improves identification performance, but training data enabling zero-shot prediction across diverse PTMs are scarce. Here, we present a major expansion of the ProteomeTools dataset, comprising over 977,000 synthetic peptides, covering 22 PTM-residue combinations. Furthermore we developed Prosit-PTM, a model with chemically-informed encoding and amino acid substitution-based augmentation trained with our novel ground-truth dataset, that achieves accurate zero-shot predictions. Applied to modified peptides, Prosit-PTM enhances PTM-site localization in phosphoproteomics, increases identification of multiply modified peptides in histones, and enables data-driven rescoring for unseen modifications such as HLA peptides. Furthermore, the learned embeddings of amino acids and modifications capture physicochemical relationships underlying PTM-driven HLA presentation. Prosit-PTM is integrated into multiple open-source tools enabling PTM-aware rescoring, site localization, spectral library generation, and beyond.

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Gabriel, W., Zolg, D. P., Giurcoiu, V., Shouman, O., Prokofeva, P., Seefried, F., Bayer, F. P., Lautenbacher, L., Soleymaniniya, A., Schnatbaum, K., Zerweck, J., Knaute, T., Delanghe, B., Huhmer, A., Wenschuh, H., Reimer, U., Medard, G., Kuster, B., Wilhelm, M.. 2025-11-10. Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM. https://doi.org/10.1101/2025.11.07.687302

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