title: Research resource: EPSLiM: ensemble predictor for short linear motifs in nuclear hormone receptors.
abstract: Nuclear receptors (NRs) are a superfamily of transcription factors central to regulating many biological processes, including cell growth, death, metabolism, and immune responses. NR-mediated gene expression can be modulated by coactivators and corepressors through direct physical interaction or protein complexes with functional domains in NRs. One class of these domains includes short linear motifs (SLiMs), which facilitate protein-protein interactions, phosphorylation, and ligand binding primarily in the intrinsically disordered regions (IDRs) of proteins. Across all proteins, the number of known SLiMs is limited due to the difficulty in studying IDRs experimentally. Computational tools provide a systematic and data-driven approach for predicting functional motifs that can be used to prioritize experimental efforts. Accordingly, several tools have been developed based on sequence conservation or biophysical features; however, discrepancies in predictions make it difficult to determine the true candidate SLiMs. In this work, we present the ensemble predictor for short linear motifs (EPSLiM), a novel strategy to prioritize the residues that are most likely to be SLiMs in IDRs. EPSLiM applies a generalized linear model to integrate predictions from individual methodologies. We show that EPSLiM outperforms individual predictors, and we apply our method to NRs. The androgen receptor is an example with an N-terminal domain of 559 disordered amino acids that contains several validated SLiMs important for transcriptional activation. We use the androgen receptor to illustrate the predictive performance of EPSLiM and make the results of all human and mouse NRs publically available through the web service http://epslim.bwh.harvard.edu.
authors.affiliation: Research Program in Men's Health: Aging and Metabolism (R.X., S.B., J.C.C., R.J.), Boston Claude D. Pepper Older Americans Independence Center, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215; The National Library of Medicine (M.N.Z.), National Center for Bioinformation Technology, The National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892; and Department of Bioengineering (Y.X.), Faculty of Engineering, McGill University, Montreal, Quebec H3A 0C3, Canada.
authors.forename: Ran
authors.initials: R
authors.lastname: Xue
authors.forename: Mikhail N
authors.initials: MN
authors.lastname: Zakharov
authors.forename: Yu
authors.initials: Y
authors.lastname: Xia
authors.forename: Shalender
authors.initials: S
authors.lastname: Bhasin
authors.forename: James C
authors.initials: JC
authors.lastname: Costello
authors.forename: Ravi
authors.initials: R
authors.lastname: Jasuja
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D019540:Area Under Curve; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D064267:Intrinsically Disordered Proteins / Q000737:chemistry; D016014:Linear Models; D051379:Mice; D012372:ROC Curve; D011944:Receptors, Androgen / Q000737:chemistry*; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D064267:Intrinsically Disordered Proteins; D011944:Receptors, Androgen