{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T22:24:20Z","timestamp":1780093460845,"version":"3.54.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010457","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000}}],"reference-count":69,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004063","name":"Knut och Alice Wallenbergs Stiftelse","doi-asserted-by":"publisher","award":["2016.0023"],"award-info":[{"award-number":["2016.0023"]}],"id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2020-02419"],"award-info":[{"award-number":["2020-02419"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005390","name":"Alfred \u00d6sterlunds Stiftelse","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005390","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for\n                    <jats:italic>de novo<\/jats:italic>\n                    protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software,\n                    <jats:italic>de novo<\/jats:italic>\n                    MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into\n                    <jats:italic>de novo<\/jats:italic>\n                    protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models\u2019 performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set\u2019s size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2\u20133 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully\n                    <jats:italic>de novo<\/jats:italic>\n                    sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs\u2019 proven improvements to\n                    <jats:italic>de novo<\/jats:italic>\n                    analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010457","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T14:04:48Z","timestamp":1674223488000},"page":"e1010457","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry 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