{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Boulay A"],"funding":["CREATE Responsible Health and Healthcare Data Science","Natural Sciences and Engineering Research Council of Canada","Fonds de recherche du Québec secteur Santé (FRQS)","Natural Sciences and Engineering Research Council of Canada (NSERC)","Bijzonder Onderzoeksfonds","Research Foundation-Flanders (FWO)","Fonds de recherche du Québec secteur Nature et technologies (FRQNT)","Research Scholars—Junior 1","Fonds de recherche du Québec secteur Nature et technologies","2024 from the European Society of Clinical Microbiology and Infectious Diseases","Research Scholars-Junior 1","CREATE Responsible Health and Healthcare Data Science (RHHDS)","Research Foundation—Flanders","Mitacs Globalink Research program","Compute Ontario","Fonds de recherche du Québec secteur Santé"],"pagination":["btaf531"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12518921"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(10)"],"pubmed_abstract":["<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 SPAED"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["SPAED: harnessing AlphaFold output for accurate segmentation of phage endolysin domains."],"pmcid":["PMC12518921"],"funding_grant_id":["1S15424N","01P10022","307935","325947","IT41138"],"pubmed_authors":["Vazquez R","Cremelie E","Briers Y","Rousseau E","Boulay A","Galiez C"],"additional_accession":[]},"is_claimable":false,"name":"SPAED: harnessing AlphaFold output for accurate segmentation of phage endolysin domains.","description":"<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 SPAED","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-04T13:47:53.802Z","creation":"2026-05-09T03:11:28.134Z"},"accession":"S-EPMC12518921","cross_references":{"pubmed":["40991341"],"doi":["10.1093/bioinformatics/btaf531"]}}