Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/btq065
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dc.titleSLiM on diet: Finding short linear motifs on domain interaction interfaces in protein data bank
dc.contributor.authorHugo, W.
dc.contributor.authorSong, F.
dc.contributor.authorAung, Z.
dc.contributor.authorNg, S.-K.
dc.contributor.authorSung, W.-K.
dc.date.accessioned2013-07-04T07:47:19Z
dc.date.available2013-07-04T07:47:19Z
dc.date.issued2010
dc.identifier.citationHugo, W., Song, F., Aung, Z., Ng, S.-K., Sung, W.-K. (2010). SLiM on diet: Finding short linear motifs on domain interaction interfaces in protein data bank. Bioinformatics 26 (8) : 1036-1042. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/btq065
dc.identifier.issn13674803
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39689
dc.description.abstractMotivation: An important class of protein interactions involves the binding of a protein's domain to a short linear motif (SLiM) on its interacting partner. Extracting such motifs, either experimentally or computationally, is challenging because of their weak binding and high degree of degeneracy. Recent rapid increase of available protein structures provides an excellent opportunity to study SLiMs directly from their 3D structures. Results: Using domain interface extraction (Diet), we characterized 452 distinct SLiMs from the Protein Data Bank (PDB), of which 155 are validated in varying degrees-40 have literature validation, 54 are supported by at least one domain-peptide structural instance, and another 61 have overrepresentation in high-throughput PPI data. We further observed that the lacklustre coverage of existing computational SLiM detection methods could be due to the common assumption that most SLiMs occur outside globular domain regions. 198 of 452 SLiM that we reported are actually found on domain-domain interface; some of them are implicated in autoimmune and neurodegenerative diseases. We suggest that these SLiMs would be useful for designing inhibitors against the pathogenic protein complexes underlying these diseases. Our findings show that 3D structure-based SLiM detection algorithms can provide a more complete coverage of SLiM-mediated protein interactions than current sequence-based approaches. Contact: ksung@comp.nus.edu.sg. Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1093/bioinformatics/btq065
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1093/bioinformatics/btq065
dc.description.sourcetitleBioinformatics
dc.description.volume26
dc.description.issue8
dc.description.page1036-1042
dc.description.codenBOINF
dc.identifier.isiut000276737100009
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