{"entity": "researcher", "timestamp": "2026-08-20T20:49:08.232Z", "family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "affiliations": ["Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe"}}, "publications": [{"entity": "publication", "iuid": "b580045770084598aff068459c95b44e", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/b580045770084598aff068459c95b44e.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/b580045770084598aff068459c95b44e"}}, "title": "Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra.", "authors": [{"family": "Lapin", "given": "Joel", "initials": "J"}, {"family": "Nilsson", "given": "Alfred", "initials": "A"}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}], "type": "journal article", "published": "2025-07-04", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "24", "issue": "7", "pages": "3722-3730", "issn-l": "1535-3893"}, "abstract": "A fundamental challenge in mass spectrometry-based proteomics is determining which peptide generated a given MS2 spectrum. Peptide sequencing typically relies on matching spectra against a known sequence database, which in some applications is not available. Deep learning-based de novo sequencing can address this limitation by directly predicting peptide sequences from MS2 data. We have seen the application of the transformer architecture to de novo sequencing produce state-of-the-art results on the so-called nine-species benchmark. In this study, we propose an improved transformer encoder inspired by the heuristics used in the manual interpretation of spectra. We modify the attention mechanism with a learned bias based on pairwise mass differences, termed Pairwise Attention (PA). Adding PA improves average peptide precision at 100% coverage by 12.7% (5.9 percentage points) over our base transformer on the original nine-species benchmark. We have also achieved a 7.4% increase over the previously published model Casanovo. Our MS2 encoding strategy is largely orthogonal to other transformer-based models encoding MS2 spectra, enabling straightforward integration into existing deep-learning approaches. Our results show that integrating domain-specific knowledge into transformers boosts de novo sequencing performance.", "doi": "10.1021/acs.jproteome.5c00063", "pmid": "40454436", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12235698"}], "notes": [], "created": "2026-08-20T08:12:03.344Z", "modified": "2026-08-20T08:12:03.429Z"}, {"entity": "publication", "iuid": "23554e8d4bc44a80bc391882eabc44af", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/23554e8d4bc44a80bc391882eabc44af.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/23554e8d4bc44a80bc391882eabc44af"}}, "title": "Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra", "authors": [{"family": "Lapin", "given": "Joel", "initials": "J", "orcid": "0009-0009-5013-238X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/76acb2bd1deb456c9a740e9e5eaaa010.json"}}, {"family": "Nilsson", "given": "Alfred", "initials": "A", "orcid": "0000-0002-3181-3800", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/33c3a0dde79c446b8b42300074ee9a45.json"}}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}], "type": "posted-content", "published": "2025-04-03", "journal": {"issn-l": null}, "abstract": null, "doi": "10.1101/2025.03.28.645943", "pmid": null, "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T11:01:57.125Z", "modified": "2026-08-20T11:01:57.284Z"}, {"entity": "publication", "iuid": "75d22281ae0c460ab81945d4b6549abe", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/75d22281ae0c460ab81945d4b6549abe.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/75d22281ae0c460ab81945d4b6549abe"}}, "title": "Toward an Integrated Machine Learning Model of a Proteomics Experiment.", "authors": [{"family": "Neely", "given": "Benjamin A", "initials": "BA", "orcid": "0000-0001-6120-7695", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6942cadc06104793927194f19aa57a8d.json"}}, {"family": "Dorfer", "given": "Viktoria", "initials": "V", "orcid": "0000-0002-5332-5701", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b2a0a15b0b2945238e7a39343b5c2a29.json"}}, {"family": "Martens", "given": "Lennart", "initials": "L", "orcid": "0000-0003-4277-658X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/df9a736ba6d04cbdb881a040480bf6f0.json"}}, {"family": "Bludau", "given": "Isabell", "initials": "I"}, {"family": "Bouwmeester", "given": "Robbin", "initials": "R", "orcid": "0000-0001-6807-7029", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cd945733eeaf46d895f9d8799b430d66.json"}}, {"family": "Degroeve", "given": "Sven", "initials": "S", "orcid": "0000-0001-8349-3370", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/99e10b1c63144945aa258d810ddbb090.json"}}, {"family": "Deutsch", "given": "Eric W", "initials": "EW", "orcid": "0000-0001-8732-0928", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2463c206245a4dbb979cad9d201d73c4.json"}}, {"family": "Gessulat", "given": "Siegfried", "initials": "S"}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Palczynski", "given": "Pawel", "initials": "P"}, {"family": "Payne", "given": "Samuel H", "initials": "SH", "orcid": "0000-0002-8351-1994", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fe701ec4cf24456980c1902ec61a0ab1.json"}}, {"family": "Rehfeldt", "given": "Tobias Greisager", "initials": "TG", "orcid": "0000-0002-1190-9485", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9671e5e2b13f40e293e935c355e68d2a.json"}}, {"family": "Schmidt", "given": "Tobias", "initials": "T", "orcid": "0000-0002-1883-6514", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d612bc6bfdce4e968b76abbd88a7603d.json"}}, {"family": "Schw\u00e4mmle", "given": "Veit", "initials": "V", "orcid": "0000-0002-9708-6722", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/158e08b0cfd34f90882f66142d8e62b7.json"}}, {"family": "Uszkoreit", "given": "Julian", "initials": "J", "orcid": "0000-0001-7522-4007", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/97c6efaaa4f045a7a718ca2ba376f641.json"}}, {"family": "Vizca\u00edno", "given": "Juan Antonio", "initials": "JA", "orcid": "0000-0002-3905-4335", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/81245410f3234e658147bc94f6b3dad2.json"}}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}, {"family": "Palmblad", "given": "Magnus", "initials": "M", "orcid": "0000-0002-5865-8994", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/80053e2256cd4e55afc7ba916821602c.json"}}], "type": "journal article", "published": "2023-03-03", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "22", "issue": "3", "pages": "681-696", "issn-l": "1535-3893"}, "abstract": "In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.", "doi": "10.1021/acs.jproteome.2c00711", "pmid": "36744821", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9990124"}], "notes": [], "created": "2026-08-20T08:11:44.566Z", "modified": "2026-08-20T08:11:45.110Z"}, {"entity": "publication", "iuid": "08ee30650bc34e33bb0b209135396f9e", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/08ee30650bc34e33bb0b209135396f9e.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/08ee30650bc34e33bb0b209135396f9e"}}, "title": "Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities.", "authors": [{"family": "Ekvall", "given": "Markus", "initials": "M"}, {"family": "Truong", "given": "Patrick", "initials": "P"}, {"family": "Gabriel", "given": "Wassim", "initials": "W", "orcid": "0000-0001-6440-9794", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3d788a6b62134f5492b2c31aec9a1625.json"}}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}], "type": "journal article", "published": "2022-05-06", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "21", "issue": "5", "pages": "1359-1364", "issn-l": "1535-3893"}, "abstract": "Machine learning has been an integral part of interpreting data from mass spectrometry (MS)-based proteomics for a long time. Relatively recently, a machine-learning structure appeared successful in other areas of bioinformatics, Transformers. Furthermore, the implementation of Transformers within bioinformatics has become relatively convenient due to transfer learning, i.e., adapting a network trained for other tasks to new functionality. Transfer learning makes these relatively large networks more accessible as it generally requires less data, and the training time improves substantially. We implemented a Transformer based on the pretrained model TAPE to predict MS2 intensities. TAPE is a general model trained to predict missing residues from protein sequences. Despite being trained for a different task, we could modify its behavior by adding a prediction head at the end of the TAPE model and fine-tune it using the spectrum intensity from the training set to the well-known predictor Prosit. We demonstrate that the predictor, which we call Prosit Transformer, outperforms the recurrent neural-network-based predictor Prosit, increasing the median angular similarity on its hold-out set from 0.908 to 0.929. We believe that Transformers will significantly increase prediction accuracy for other types of predictions within MS-based proteomics.", "doi": "10.1021/acs.jproteome.1c00870", "pmid": "35413196", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC9087333"}], "notes": [], "created": "2026-08-20T08:11:24.560Z", "modified": "2026-08-20T08:11:24.708Z"}, {"entity": "publication", "iuid": "6b3c9d1faf054f2caf91c423ec008722", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/6b3c9d1faf054f2caf91c423ec008722.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/6b3c9d1faf054f2caf91c423ec008722"}}, "title": "Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics.", "authors": [{"family": "Palmblad", "given": "Magnus", "initials": "M", "orcid": "0000-0002-5865-8994", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/80053e2256cd4e55afc7ba916821602c.json"}}, {"family": "B\u00f6cker", "given": "Sebastian", "initials": "S", "orcid": "0000-0002-9304-8091", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c71ab14b72a540f3be202f873d6c7dea.json"}}, {"family": "Degroeve", "given": "Sven", "initials": "S"}, {"family": "Kohlbacher", "given": "Oliver", "initials": "O", "orcid": "0000-0003-1739-4598", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9317a8da5d9d4df782158d7555f229eb.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Noble", "given": "William Stafford", "initials": "WS", "orcid": "0000-0001-7283-4715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}], "type": "journal article", "published": "2022-04-01", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "21", "issue": "4", "pages": "1204-1207", "issn-l": "1535-3893"}, "abstract": "Machine learning is increasingly applied in proteomics and metabolomics to predict molecular structure, function, and physicochemical properties, including behavior in chromatography, ion mobility, and tandem mass spectrometry. These must be described in sufficient detail to apply or evaluate the performance of trained models. Here we look at and interpret the recently published and general DOME (Data, Optimization, Model, Evaluation) recommendations for conducting and reporting on machine learning in the specific context of proteomics and metabolomics.", "doi": "10.1021/acs.jproteome.1c00900", "pmid": "35119864", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8981311"}], "notes": [], "created": "2026-08-20T08:11:33.301Z", "modified": "2026-08-20T08:11:33.445Z"}, {"entity": "publication", "iuid": "ea525cca041145b7bdfe210040715780", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ea525cca041145b7bdfe210040715780.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ea525cca041145b7bdfe210040715780"}}, "title": "Expanding the Use of Spectral Libraries in Proteomics.", "authors": [{"family": "Deutsch", "given": "Eric W", "initials": "EW", "orcid": "0000-0001-8732-0928", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2463c206245a4dbb979cad9d201d73c4.json"}}, {"family": "Perez-Riverol", "given": "Yasset", "initials": "Y", "orcid": "0000-0001-6579-6941", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cb4449bad29741b195a7593fc39a769d.json"}}, {"family": "Chalkley", "given": "Robert J", "initials": "RJ"}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}, {"family": "Tate", "given": "Stephen", "initials": "S"}, {"family": "Sachsenberg", "given": "Timo", "initials": "T", "orcid": "0000-0002-2833-6070", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d5425fd7b0434642b4c6f7aef67b5f23.json"}}, {"family": "Walzer", "given": "Mathias", "initials": "M"}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Delanghe", "given": "Bernard", "initials": "B"}, {"family": "B\u00f6cker", "given": "Sebastian", "initials": "S", "orcid": "0000-0002-9304-8091", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c71ab14b72a540f3be202f873d6c7dea.json"}}, {"family": "Schymanski", "given": "Emma L", "initials": "EL", "orcid": "0000-0001-6868-8145", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cc533a4744624b80960f4cf28cabdd64.json"}}, {"family": "Wilmes", "given": "Paul", "initials": "P", "orcid": "0000-0002-6478-2924", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e169727cc0f9482baf98944388a77b24.json"}}, {"family": "Dorfer", "given": "Viktoria", "initials": "V", "orcid": "0000-0002-5332-5701", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b2a0a15b0b2945238e7a39343b5c2a29.json"}}, {"family": "Kuster", "given": "Bernhard", "initials": "B", "orcid": "0000-0002-9094-1677", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5196477f29b345ccb3f4a8fc32c9fac4.json"}}, {"family": "Volders", "given": "Pieter-Jan", "initials": "PJ", "orcid": "0000-0002-2685-2637", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6fac44563b164dae90052b9764a8f9ab.json"}}, {"family": "Jehmlich", "given": "Nico", "initials": "N", "orcid": "0000-0002-5638-6868", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b1c3e5702c6740c6a4d553b60eee62e3.json"}}, {"family": "Vissers", "given": "Johannes P C", "initials": "JPC", "orcid": "0000-0001-6283-8456", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3cde55fba2234534a8ce709f13478e42.json"}}, {"family": "Wolan", "given": "Dennis W", "initials": "DW", "orcid": "0000-0001-9879-8353", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/635cea76374a4c90a9514aa845b7f59c.json"}}, {"family": "Wang", "given": "Ana Y", "initials": "AY"}, {"family": "Mendoza", "given": "Luis", "initials": "L", "orcid": "0000-0003-0128-8643", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b6e8a7feab0f481eaf6acd7ec097c5c5.json"}}, {"family": "Shofstahl", "given": "Jim", "initials": "J"}, {"family": "Dowsey", "given": "Andrew W", "initials": "AW", "orcid": "0000-0002-7404-9128", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a1cb70e527d84d32b1e995014007efb8.json"}}, {"family": "Griss", "given": "Johannes", "initials": "J", "orcid": "0000-0003-2206-9511", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b001048c5a5148bdb9bd16ff46e067db.json"}}, {"family": "Salek", "given": "Reza M", "initials": "RM", "orcid": "0000-0001-8604-1732", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/62799f5748ef484b816827380b723b2c.json"}}, {"family": "Neumann", "given": "Steffen", "initials": "S", "orcid": "0000-0002-7899-7192", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3fd85c99a6364305ba466010ed3012ec.json"}}, {"family": "Binz", "given": "Pierre-Alain", "initials": "PA", "orcid": "0000-0002-0045-7698", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f857e16ce82b4242ab0e780c8bec9539.json"}}, {"family": "Lam", "given": "Henry", "initials": "H", "orcid": "0000-0001-7928-0364", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5f27cfbc77fe4122977457e89968814c.json"}}, {"family": "Vizca\u00edno", "given": "Juan Antonio", "initials": "JA", "orcid": "0000-0002-3905-4335", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/81245410f3234e658147bc94f6b3dad2.json"}}, {"family": "Bandeira", "given": "Nuno", "initials": "N"}, {"family": "R\u00f6st", "given": "Hannes", "initials": "H", "orcid": "0000-0003-0990-7488", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4579af772375404da76c447b23c658a3.json"}}], "type": "journal article", "published": "2018-12-07", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "17", "issue": "12", "pages": "4051-4060", "issn-l": "1535-3893"}, "abstract": "The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.", "doi": "10.1021/acs.jproteome.8b00485", "pmid": "30270626", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS1015163"}, {"db": "pmc", "key": "PMC6443480"}], "notes": [], "created": "2026-08-20T08:12:14.655Z", "modified": "2026-08-20T08:12:15.306Z"}, {"entity": "publication", "iuid": "ac2ff1585c4b42eb9bd7e15857fc8385", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ac2ff1585c4b42eb9bd7e15857fc8385.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ac2ff1585c4b42eb9bd7e15857fc8385"}}, "title": "Mining the Human Tissue Proteome for Protein Citrullination.", "authors": [{"family": "Lee", "given": "Chien-Yun", "initials": "CY"}, {"family": "Wang", "given": "Dongxue", "initials": "D"}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}, {"family": "Zolg", "given": "Daniel P", "initials": "DP"}, {"family": "Schmidt", "given": "Tobias", "initials": "T"}, {"family": "Schnatbaum", "given": "Karsten", "initials": "K"}, {"family": "Reimer", "given": "Ulf", "initials": "U"}, {"family": "Pont\u00e9n", "given": "Fredrik", "initials": "F"}, {"family": "Uhl\u00e9n", "given": "Mathias", "initials": "M"}, {"family": "Hahne", "given": "Hannes", "initials": "H"}, {"family": "Kuster", "given": "Bernhard", "initials": "B"}], "type": "journal article", "published": "2018-07-00", "journal": {"title": "Mol. Cell Proteomics", "issn": "1535-9484", "volume": "17", "issue": "7", "pages": "1378-1391", "issn-l": "1535-9476"}, "abstract": "Citrullination is a posttranslational modification of arginine catalyzed by five peptidylarginine deiminases (PADs) in humans. The loss of a positive charge may cause structural or functional alterations, and while the modification has been linked to several diseases, including rheumatoid arthritis (RA) and cancer, its physiological or pathophysiological roles remain largely unclear. In part, this is owing to limitations in available methodology to robustly enrich, detect, and localize the modification. As a result, only a few citrullination sites have been identified on human proteins with high confidence. In this study, we mined data from mass-spectrometry-based deep proteomic profiling of 30 human tissues to identify citrullination sites on endogenous proteins. Database searching of \u223c70 million tandem mass spectra yielded \u223c13,000 candidate spectra, which were further triaged by spectrum quality metrics and the detection of the specific neutral loss of isocyanic acid from citrullinated peptides to reduce false positives. Because citrullination is easily confused with deamidation, we synthetized \u223c2,200 citrullinated and 1,300 deamidated peptides to build a library of reference spectra. This led to the validation of 375 citrullination sites on 209 human proteins. Further analysis showed that >80% of the identified modifications sites were new, and for 56% of the proteins, citrullination was detected for the first time. Sequence motif analysis revealed a strong preference for Asp and Gly, residues around the citrullination site. Interestingly, while the modification was detected in 26 human tissues with the highest levels found in the brain and lung, citrullination levels did not correlate well with protein expression of the PAD enzymes. Even though the current work represents the largest survey of protein citrullination to date, the modification was mostly detected on high abundant proteins, arguing that the development of specific enrichment methods would be required in order to study the full extent of cellular protein citrullination.", "doi": "10.1074/mcp.RA118.000696", "pmid": "29610271", "labels": {"Affiliated researcher": null}, "xrefs": [{"db": "pmc", "key": "PMC6030718"}, {"db": "pii", "key": "S1535-9476(20)32222-2"}], "notes": [], "created": "2019-01-17T13:52:51.528Z", "modified": "2026-08-20T09:32:37.872Z"}]}