{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["6"],"submitter":["Idrees S"],"funding":["University International Postgraduate Award to Sobia Idrees"],"pubmed_abstract":["Many important cellular processes involve protein-protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (S"],"journal":["PeerJ"],"pagination":["e5858"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6215436"],"repository":["biostudies-literature"],"pubmed_title":["SLiM-Enrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions."],"pmcid":["PMC6215436"],"pubmed_authors":["Idrees S","Perez-Bercoff A","Edwards RJ"],"additional_accession":[]},"is_claimable":false,"name":"SLiM-Enrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions.","description":"Many important cellular processes involve protein-protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (S","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018","modification":"2025-04-04T09:14:48.219Z","creation":"2019-03-27T00:06:14Z"},"accession":"S-EPMC6215436","cross_references":{"pubmed":["30402352"],"doi":["10.7717/peerj.5858"]}}