2026-08-27T21:19:53Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27507 2026-08-27T06:04:07Z ddc:004 open_access

GSI: A New Approach to the Protein Inference Problem Berthier, Aurélien Benoist, Émile Fertin, Guillaume Jean, Géraldine Tandem Mass Spectrometry Peptide identification Protein inference Optimization Algorithmic complexity Mixed Integer Linear Programming The protein inference problem, i.e., determining which proteins are present in a biological sample, is key to understanding the roles of proteins and, more broadly, many biological processes. Protein identification is typically achieved by first cleaving proteins into smaller sequences called peptides. Peptides are then identified using tandem mass spectrometry, a process that produces mass spectra, and in which peptide identification consists of associating, via dedicated tools, a mass spectrum to a peptide sequence. Protein inference consists of identifying, from a list of identified peptides, the proteins that most likely produced them, and were therefore present in the original sample. Usually, peptide identification and protein inference are two separate steps, which are sequentially achieved. However, by proceeding in such a way, a significant amount of potentially useful information contained in the spectra may be discarded in the second step. Moreover, AI-based tools can now predict the likelihood of a peptide’s identification when its parent protein is present in the sample. In this paper, we present the Global Spectrum Interpretation (GSI) model, a protein inference model that integrates all this information to produce more accurate protein identifications. We show that GSI is NP-hard and provide a Mixed Integer Linear Program (MILP) formulation for it. This MILP is then benchmarked against state-of-the-art protein inference models on several datasets. Our results show that GSI’s promising and original approach achieves performance comparable to current models and outperforms other widely used ones, while being more explainable. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Aurélien Berthier and Émile Benoist and Guillaume Fertin and Géraldine Jean 2026 Is Part Of LIPIcs, Volume 390, 26th International Conference on Algorithms for Bioinformatics (WABI 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.WABI.2026.3 urn:nbn:de:0030-drops-275072 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2026.3 eng https://creativecommons.org/licenses/by/4.0/legalcode