{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["45(W1)"],"submitter":["Prytuliak R"],"pubmed_abstract":["Short linear motifs (SLiMs) in proteins are self-sufficient functional sequences that specify interaction sites for other molecules and thus mediate a multitude of functions. Computational, as well as experimental biological research would significantly benefit, if SLiMs in proteins could be correctly predicted de novo with high sensitivity. However, de novo SLiM prediction is a difficult computational task. When considering recall and precision, the performances of published methods indicate remaining challenges in SLiM discovery. We have developed HH-MOTiF, a web-based method for SLiM discovery in sets of mainly unrelated proteins. HH-MOTiF makes use of evolutionary information by creating Hidden Markov Models (HMMs) for each input sequence and its closely related orthologs. HMMs are com"],"journal":["Nucleic acids research"],"pagination":["W470-W477"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5570144"],"repository":["biostudies-literature"],"pubmed_title":["HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons."],"pmcid":["PMC5570144"],"pubmed_authors":["Habermann BH","Prytuliak R","Volkmer M","Meier M"],"additional_accession":[]},"is_claimable":false,"name":"HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons.","description":"Short linear motifs (SLiMs) in proteins are self-sufficient functional sequences that specify interaction sites for other molecules and thus mediate a multitude of functions. Computational, as well as experimental biological research would significantly benefit, if SLiMs in proteins could be correctly predicted de novo with high sensitivity. However, de novo SLiM prediction is a difficult computational task. When considering recall and precision, the performances of published methods indicate remaining challenges in SLiM discovery. We have developed HH-MOTiF, a web-based method for SLiM discovery in sets of mainly unrelated proteins. HH-MOTiF makes use of evolutionary information by creating Hidden Markov Models (HMMs) for each input sequence and its closely related orthologs. HMMs are com","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017 Jul","modification":"2025-04-05T14:17:31.356Z","creation":"2019-03-27T02:54:25Z"},"accession":"S-EPMC5570144","cross_references":{"pubmed":["28460141"],"doi":["10.1093/nar/gkx341"]}}