Prosit - Deep learning model for proteomics

Prosit ( is a free to use deep learning framework for generating custom spectral libraries and iRT prediction and inferrence of high quality MS2 spectra for any organism and protease. Prosit is part of the ProteomeTools project and was trained on the project's high quality synthetic datasets.

Prosit has a wide range of functions, such as predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition (DIA) and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.

Available theses and research projects

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Relevant source code

  • DLOmix  - python framework to train deep learning models in proteomics research
  • prosit_grpc - a python client to retrieve predictions in real-time
  • spectrum_fundamentals - a python library for basic processing and annotation tasks specifically for generating training/testing/holdout data
  • PROSPECT - a large annotated dataset leveraging the raw data from ProteomeTools

Primary literature

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  • Neely, Benjamin A.; Dorfer, Viktoria; Martens, Lennart; Bludau, Isabell; Bouwmeester, Robbin; Degroeve, Sven; Deutsch, Eric W.; Gessulat, Siegfried; Käll, Lukas; Palczynski, Pawel; Payne, Samuel H.; Rehfeldt, Tobias Greisager; Schmidt, Tobias; Schwämmle, Veit; Uszkoreit, Julian; Vizcaíno, Juan Antonio; Wilhelm, Mathias; Palmblad, Magnus: Toward an Integrated Machine Learning Model of a Proteomics Experiment. Journal of Proteome Research 22 (3), 2023, 681-696 more…


  • Ekvall, Markus; Truong, Patrick; Gabriel, Wassim; Wilhelm, Mathias; Käll, Lukas: Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities. Journal of Proteome Research 21 (5), 2022, 1359-1364 more…
  • Gabriel, Wassim; The, Matthew; Zolg, Daniel P.; Bayer, Florian P.; Shouman, Omar; Lautenbacher, Ludwig; Schnatbaum, Karsten; Zerweck, Johannes; Knaute, Tobias; Delanghe, Bernard; Huhmer, Andreas; Wenschuh, Holger; Reimer, Ulf; Médard, Guillaume; Kuster, Bernhard; Wilhelm, Mathias: Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides. Analytical Chemistry, 2022 more…
  • Giansanti, Piero; Samaras, Patroklos; Bian, Yangyang; Meng, Chen; Coluccio, Andrea; Frejno, Martin; Jakubowsky, Hannah; Dobiasch, Sophie; Hazarika, Rashmi R.; Rechenberger, Julia; Calzada-Wack, Julia; Krumm, Johannes; Mueller, Sebastian; Lee, Chien-Yun; Wimberger, Nicole; Lautenbacher, Ludwig; Hassan, Zonera; Chang, Yun-Chien; Falcomatà, Chiara; Bayer, Florian P.; Bärthel, Stefanie; Schmidt, Tobias; Rad, Roland; Combs, Stephanie E.; The, Matthew; Johannes, Frank; Saur, Dieter; de Angelis, Martin Hrabe; Wilhelm, Mathias; Schneider, Günter; Kuster, Bernhard: Mass spectrometry-based draft of the mouse proteome. Nature Methods, 2022 more…
  • Lautenbacher, Ludwig; Samaras, Patroklos; Muller, Julian; Grafberger, Andreas; Shraideh, Marwin; Rank, Johannes; Fuchs, Simon T; Schmidt, Tobias K; The, Matthew; Dallago, Christian; Wittges, Holger; Rost, Burkhard; Krcmar, Helmut; Kuster, Bernhard; Wilhelm, Mathias: ProteomicsDB: toward a FAIR open-source resource for life-science research. Nucleic Acids Research, 2022 more…


  • Schmidt, Tobias; Samaras, Patroklos; Dorfer, Viktoria; Panse, Christian; Kockmann, Tobias; Bichmann, Leon; van Puyvelde, Bart; Perez-Riverol, Yasset; Deutsch, Eric W.; Kuster, Bernhard; Wilhelm, Mathias: Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison. Journal of Proteome Research 20 (6), 2021, 3388-3394 more…
  • Verbruggen, Steven; Gessulat, Siegfried; Gabriels, Ralf; Matsaroki, Anna; Van de Voorde, Hendrik; Kuster, Bernhard; Degroeve, Sven; Martens, Lennart; Van Criekinge, Wim; Wilhelm, Mathias; Menschaert, Gerben: Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics. Molecular & Cellular Proteomics 20, 2021, 100076 more…
  • Wilhelm, Mathias; Zolg, Daniel P.; Graber, Michael; Gessulat, Siegfried; Schmidt, Tobias; Schnatbaum, Karsten; Schwencke-Westphal, Celina; Seifert, Philipp; de Andrade Krätzig, Niklas; Zerweck, Johannes; Knaute, Tobias; Bräunlein, Eva; Samaras, Patroklos; Lautenbacher, Ludwig; Klaeger, Susan; Wenschuh, Holger; Rad, Roland; Delanghe, Bernard; Huhmer, Andreas; Carr, Steven A.; Clauser, Karl R.; Krackhardt, Angela M.; Reimer, Ulf; Kuster, Bernhard: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nature Communications 12 (1), 2021 more…


  • Searle, Brian C.; Swearingen, Kristian E.; Barnes, Christopher A.; Schmidt, Tobias; Gessulat, Siegfried; Küster, Bernhard; Wilhelm, Mathias: Generating high quality libraries for DIA MS with empirically corrected peptide predictions. Nature Communications 11 (1), 2020 more…


  • Gessulat, Siegfried; Schmidt, Tobias; Zolg, Daniel Paul; Samaras, Patroklos; Schnatbaum, Karsten; Zerweck, Johannes; Knaute, Tobias; Rechenberger, Julia; Delanghe, Bernard; Huhmer, Andreas; Reimer, Ulf; Ehrlich, Hans-Christian; Aiche, Stephan; Kuster, Bernhard; Wilhelm, Mathias: Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature Methods 16 (6), 2019, 509-518 more…
  • Verbruggen, Steven; Ndah, Elvis; Van Criekinge, Wim; Gessulat, Siegfried; Kuster, Bernhard; Wilhelm, Mathias; Van Damme, Petra; Menschaert, Gerben: PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms. Molecular & Cellular Proteomics 18 (8), 2019, S126-S140 more…

Tools utilizing Prosit

Universal Spectrum Explorer

A web app based on IPSA for cross-resource (peptide) spectrum visualization and comparison.


A freely available MS Windows application for building and analyzing various proteomics data.


A freely available library search engine comprised of several algorithms for DIA data analysis.