biostudies-literatureBoulay ACREATE Responsible Health and Healthcare Data ScienceNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec secteur Santé (FRQS)Natural Sciences and Engineering Research Council of Canada (NSERC)Bijzonder OnderzoeksfondsResearch Foundation-Flanders (FWO)Fonds de recherche du Québec secteur Nature et technologies (FRQNT)Research Scholars—Junior 1Fonds de recherche du Québec secteur Nature et technologies2024 from the European Society of Clinical Microbiology and Infectious DiseasesResearch Scholars-Junior 1CREATE Responsible Health and Healthcare Data Science (RHHDS)Research Foundation—FlandersMitacs Globalink Research programCompute OntarioFonds de recherche du Québec secteur Santébtaf531https://www.ebi.ac.uk/biostudies/studies/S-EPMC12518921biostudies-literatureUnknown41(10)<h4>Summary</h4>SPAED is an accessible tool for the accurate segmentation of protein domains that leverages information contained in the predicted aligned error (PAE) matrix obtained from AlphaFold to better identify domain-linker boundaries and detect terminal disordered regions. On a dataset of 376 bacteriophage endolysins (proteins that degrade the bacterial cell wall), SPAED achieves a mean intersect-over-union score of 96% and a domain-boundary-distance score of 89% compared to 94% and 70%, respectively, for the state-of-the-art tool Chainsaw.<h4>Availability and implementation</h4>Implemented in Python, SPAED is accessible on the web (https://spaed.ca) and available for download from https://github.com/Rousseau-Team/spaed or https://pypi.org/project/spaed. The data used to test SPAEDBioinformatics (Oxford, England)SPAED: harnessing AlphaFold output for accurate segmentation of phage endolysin domains.PMC125189211S15424N01P10022307935325947IT41138Vazquez RCremelie EBriers YRousseau EBoulay AGaliez CfalseSPAED: harnessing AlphaFold output for accurate segmentation of phage endolysin domains.<h4>Summary</h4>SPAED is an accessible tool for the accurate segmentation of protein domains that leverages information contained in the predicted aligned error (PAE) matrix obtained from AlphaFold to better identify domain-linker boundaries and detect terminal disordered regions. On a dataset of 376 bacteriophage endolysins (proteins that degrade the bacterial cell wall), SPAED achieves a mean intersect-over-union score of 96% and a domain-boundary-distance score of 89% compared to 94% and 70%, respectively, for the state-of-the-art tool Chainsaw.<h4>Availability and implementation</h4>Implemented in Python, SPAED is accessible on the web (https://spaed.ca) and available for download from https://github.com/Rousseau-Team/spaed or https://pypi.org/project/spaed. The data used to test SPAED2025-01-01T00:00:00Z2025 Oct2026-06-04T13:47:53.802Z2026-05-09T03:11:28.134ZS-EPMC125189214099134110.1093/bioinformatics/btaf531