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Here, we systematically evaluate instrument\u2013software coevolution across eight mass spectrometry platforms, three generations of search engines, and multiple rescoring approaches, yielding 72 unique instrument\u2013software combinations spanning from 2004 to 2024. Our results reveal that instrumentation and software improvements produce synergistic rather than substitutive benefits. Here, machine learning-based rescoring consistently recovers identifications from low-intensity precursors that produce noisier, more challenging spectra. Crucially, because of increased sensitivity and speed, modern instruments detect more low-intensity precursors, thereby increasing the population of challenging spectra for which rescoring provides the greatest benefit. However, this expanded detection depth comes at a cost: recovered low-abundance peptides exhibit inherently higher quantification error, creating a fundamental trade-off between proteome coverage and quantification accuracy. Together, these findings provide a systematic overview of how instrument and software advances have jointly shaped proteomics performance over the past two decades.<\/jats:p>","DOI":"10.1021\/acs.jproteome.6c00295","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T19:23:47Z","timestamp":1784057027000},"page":"4251-4261","source":"Crossref","is-referenced-by-count":1,"title":["Instrument\u2013Software\nSynergy in Proteomics:\nSystematic Evaluation across Mass Spectrometry Platforms, Search Engines,\nand Rescoring Methods"],"prefix":"10.1021","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1818-8619","authenticated-orcid":true,"given":"Sander","family":"Heyndrickx","sequence":"first","affiliation":[{"name":"CompOmics, VIB-UGent Center for Medical Biotechnology, VIB , ,","place":["Ghent, Belgium, 9000"]},{"name":"Ghent University , , ,","place":["Ghent, Belgium, 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2020"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tess AV","family":"Afanasyeva","sequence":"additional","affiliation":[{"name":"Amsterdam UMC, University of Amsterdam , , ,","place":["Amsterdam, Netherlands, 1066 CX"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel","family":"Lapin","sequence":"additional","affiliation":[{"name":"Technical University of Munich , , ,","place":["Freising, Germany, 85354"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arzu Tugce","family":"Guler","sequence":"additional","affiliation":[{"name":"Northeastern University , , , ,","place":["Boston, Massachusetts, United States, 02115"]},{"name":"Boston Children\u2019s Hospital , , , ,","place":["Boston, Massachusetts, United States, 02115"]},{"name":"Harvard Medical School , , ,","place":["Boston, Massachusetts, United States, 02115"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre-Olivier","family":"Schmit","sequence":"additional","affiliation":[{"name":"Bruker France SAS , ,","place":["Wissembourg, France, 67160"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0079-319X","authenticated-orcid":true,"given":"Christine","family":"Carapito","sequence":"additional","affiliation":[{"name":"IPHC UMR 7178, University of Strasbourg, CNRS , , ,","place":["Strasbourg, France, 67000"]},{"name":"Infrastructure Nationale de Prote\u0301omique ProFI \u2212 UAR 2048 , ,","place":["Strasbourg, France, 67087"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4277-658X","authenticated-orcid":true,"given":"Lennart","family":"Martens","sequence":"additional","affiliation":[{"name":"CompOmics, VIB-UGent Center for Medical Biotechnology, VIB , ,","place":["Ghent, Belgium, 9000"]},{"name":"Ghent University , , 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