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Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides
Open accessPreprint — published as 10.1016/j.mcpro.2025.101501
Abstract
Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides from tandem mass spectra, but is hindered by the combinatorial expansion of modification types and sites. De novo peptide sequencing offers an attractive alternative, yet existing methods remain limited to unmodified peptides or a narrow set of PTMs. Here, we curated a large dataset of spectra from endogenous and synthetic peptides from ProteomeTools spanning 19 biologically relevant amino acid-PTM combinations, covering phosphorylation, acetylation, and ubiquitination. We used this dataset to develop Modanovo, an extension of the Casanovo transformer architecture for de novo peptide sequencing. Modanovo achieved robust performance across these amino acid-PTM combinations (median area under the precision-coverage curve 0.92), while maintaining performance on unmodified peptides (0.93), nearly identical to Casanovo (0.94). The model outperformed π-PrimeNovo-PTM and showed increased precision and complementarity to the database search tool MSFragger. Robustness was confirmed across independent datasets, particularly at peptide lengths frequently represented in the curated dataset. Applied to a phosphoproteomics dataset from monkeypox virus-infected cells, Modanovo recovered numerous confident peptides not reported by database search, including new viral phosphosites supported by spectral evidence, thereby demonstrating its complementarity to database-driven identification approaches. These results establish Modanovo as a broadly applicable model for comprehensive de novo sequencing of both modified and unmodified peptides.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Research works printed but not linked: 51
- 10.1586/14789450.2015.1042867 · DOI
- Klostermeier D , Rudolph MG . Biophysical Chemistry. 1st ed. CRC Press; 2017. · DOI
- Ramazi S , Zahiri J. Post-translational modifications in proteins: resources, tools and prediction methods. Database [Internet]. 2021 Apr 7 [cited 2025 July 17];2021. Available from: https://academic.oup.com/database/article/doi/10.1093/database/baab012/6214407 · DOI
- 10.1038/nature19949 · DOI · Paper page
- 10.1039/d2an01246d · DOI
- 10.1038/nrm1468 · DOI
- 10.3390/ijms24065350 · DOI
- 2020, A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of This Field, Proteomes, 8, 14, 10.3390/proteomes8030014 · DOI
- 10.1021/cr3003533 · DOI · Paper page
- 10.1021/acs.jproteome.2c00838 · DOI
- 10.1089/106652799318300 · DOI
- 10.1002/(SICI)1097-0231(19970615)11:9<1067::AID-RCM953>3.0.CO;2-L · DOI
- 10.1093/bib/bbx033 · DOI
- 10.1093/bib/bbs004 · DOI
- 10.1016/1044-0305(94)80016-2 · DOI · Paper page
- 10.1074/mcp.R111.009522 · DOI
- 2013, ISPTM: An Iterative Search Algorithm for Systematic Identification of Post-translational Modifications from Complex Proteome Mixtures, J Proteome Res, 12, 3831, 10.1021/pr4003883 · DOI
- 10.1002/mas.21406 · DOI
- 10.1038/s41467-024-49731-x · DOI
- Straub G , Ananth V , Fondrie WE , Hsu C , Klaproth-Andrade D , Riffle M , et al. Improvements to Casanovo, a deep learning de novo peptide sequencer [Internet]. Molecular Biology; 2025 [cited 2025 Aug 28]. Available from: http://biorxiv.org/lookup/doi/10.1101/2025.07.25.666826 · DOI
- Yilmaz M , Fondrie W , Bittremieux W , Oh S , Noble WS . De novo mass spectrometry peptide sequencing with a transformer model. In: International Conference on Machine Learning. PMLR; 2022. p. 25514–22. · DOI
- Bittremieux W , Ananth V , Fondrie WE , Melendez C , Pominova M , Sanders J , et al. Deep Learning Methods for De Novo Peptide Sequencing. Mass Spectrom Rev [Internet]. 2024 Nov 29 [cited 2025 July 17]; Available from: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/mas.21919 · DOI
- 2025, InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments, Nat Mach Intell, 7, 565, 10.1038/s42256-025-01019-5 · DOI
- Xia J , Chen S , Zhou J , Shan X , Du W , Gao Z , et al. AdaNovo: Towards Robust\textbackslash emphłbrace De Novo\rbrace Peptide Sequencing in Proteomics against Data Biases. In: Globerson A , Mackey L , Belgrave D , Fan A , Paquet U , Tomczak J , et al., editors. Advances in Neural Information Processing Systems [Internet]. Curran Associates, Inc.; 2024. p. 1811–28. Available from: https://proceedings.neurips.cc/paper_files/paper/2024/file/034b4a2860d4d170ce663584bc78cb32-Paper-Conference.pdf
- Zhang X , Ling T , Jin Z , Xu S , Gao Z , Sun B , et al. π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing. Nat Commun [Internet]. 2025 Jan 2 [cited 2025 July 17];16(1). Available from: https://www.nature.com/articles/s41467-024-55021-3 · DOI
- 10.1038/s41598-024-65861-0 · DOI
- Correction to: Introducing π-HelixNovo for practical large-scale de novo peptide sequencing. Brief Bioinform. 2024 Mar 27;25(3):bbae134. · DOI
- Lee S , Kim H . Bidirectional de novo peptide sequencing using a transformer model. Bittremieux W , editor. PLOS Comput Biol. 2024 Feb 28;20(2):e1011892. · DOI
- 2024, ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing, Proc AAAI Conf Artif Intell, 38, 144
- 2018, Assembling the community-scale discoverable human proteome, Cell Syst, 7, 412, 10.1016/j.cels.2018.08.004 · DOI · Paper page
- 10.1074/mcp.TIR118.000783 · DOI
- 10.1016/j.cell.2020.05.043 · DOI
- Gabriel W , Shouman O , Schroeder A , Boessl F , Wilhelm M . PROSPECT PTMs: Rich Labeled Tandem Mass Spectrometry Dataset of Modified Peptides for Machine Learning in Proteomics. Adv Neural Inf Process Syst. 2024;37.
- 10.1038/NMETH.4153 · DOI
- Huang Y , Bergant V , Grass V , Emslander Q , Hamad MS , Hubel P , et al. Multi-omics characterization of the monkeypox virus infection. Nat Commun [Internet]. 2024 Aug 8 [cited 2025 July 17];15(1). Available from: https://www.nature.com/articles/s41467-024-51074-6 · DOI
- Zhang X , Ling T , Jin Z , Xu S , Gao Z , Sun B , et al. π-PrimeNovo: An Accurate and Efficient Non-Autoregressive Deep Learning Model for De Novo Peptide Sequencing. bioRxiv. 2024;2024–05. · DOI
- 10.1038/nmeth.4256 · DOI
- 10.1038/nbt.1511 · DOI · Paper page
- 10.1016/j.cpc.2010.04.018 · DOI
- 10.1038/S41592-019-0426-7 · DOI
- Lautenbacher L , Yang KL , Kockmann T , Panse C , Chambers M , Kahl E , et al. Koina: Democratizing machine learning for proteomics research [Internet]. Cold Spring Harbor Laboratory; 2024 [cited 2025 Aug 6]. Available from: http://biorxiv.org/lookup/doi/10.1101/2024.06.01.596953 · DOI
- Sun W , Zhang Q , Zhang X , Tran NH , Ziaur Rahman M , Chen Z , et al. Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics. Nat Commun [Internet]. 2023 July 8 [cited 2025 July 17];14(1). Available from: https://www.nature.com/articles/s41467-023-39699-5 · DOI
- 10.1002/pmic.200300744 · DOI
- 10.1016/j.celrep.2019.04.042 · DOI
- 2014, Static and dynamic protein phosphorylation in the Vaccinia virion, Virology, 452–453, 310
- 10.1186/1471-2091-1-2 · DOI
- 2018, Proteotype profiling unmasks a viral signalling network essential for poxvirus assembly and transcriptional competence, Nat Microbiol, 3, 588, 10.1038/s41564-018-0142-6 · DOI
- 10.1016/j.virol.2010.01.020 · DOI
- 10.1128/JVI.01042-16 · DOI
- Lauridsen J , Ramasamy P , Catzel R , Canbay V , Mabona A , Eloff K , et al. InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics [Internet]. Cold Spring Harbor Laboratory; 2025 [cited 2025 July 17]. Available from: http://biorxiv.org/lookup/doi/10.1101/2025.05.14.654049 · DOI
- Picciani M , Gabriel W , Giurcoiu V , Shouman O , Hamood F , Lautenbacher L , et al. Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit. PROTEOMICS [Internet]. 2024 Apr [cited 2025 Aug 6];24(8). Available from: https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202300112 · DOI
Metadata
- Authors
- Klaproth-Andrade, D., Bruns, Y., Gabriel, W., Nix, C., Bergant, V., Pichlmair, A., Wilhelm, M., & Gagneur, J.
- Affiliations
- Technical University of Munich (DE)
- National Institute of Chemistry (SI)
- German Center for Infection Research (DE)
- Helmholtz Munich (DE)
- Corresponding author
- Wilhelm, Mathias
- Gagneur, Julien
- ORCID
- Venue
- Molecular & Cellular Proteomics
- Publisher
- openRxiv
- Published
- 2025-09-16
- Volume
- 25
- Issue
- 2
- Pages
- 101501-101501
- ISSN
- 1535-9476 (ISSN-L)
- PMC
- PMC12860953
- OpenAlex
- W7117237808
- Cited by
- 4
- References
- 1
- Citations by year
- 2026: 3 · 2025: 1
- FWCI
- 1.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- European Commission (101077037)
- Deutsche Forschungsgemeinschaft (TRR 353, TRR 237, TRR237 (A07), TRR179, 461264291, TRR179-TP24)
- Bundesministerium für Bildung und Forschung (16LW0243K)
- Javna Agencija za Raziskovalno Dejavnost RS (J4-60082, P1-0242)
- European Research Council (101077037)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- post-translational modification, de novo peptide sequencing, tandem mass spectrometry, synthetic peptides, endogenous peptides, phosphorylation, acetylation, ubiquitination, phosphoproteomics, monkeypox virus, spectral library
- MeSH terms
- Acetylation, Humans, Peptides, Phosphorylation, Protein Processing, Post-Translational, Software, Sequence Analysis, Protein, Databases, Protein, Proteomics, Tandem Mass Spectrometry
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