Edwards RJ, Paulsen K, Aguilar Gomez CM & Pérez-Bercoff Å (2020): Computational Prediction of Disordered Protein Motifs using SLiMSuite. Methods Mol Biol. 2141:37-72. doi: 10.1007/978-1-0716-0524-0_3. [PubMed]
Abstract
Short linear motifs (SLiMs) are important mediators of interactions between intrinsically disordered regions of proteins and their interaction partners. Here, we detail instructions for the computational prediction of SLiMs in disordered protein regions, using the main tools of the SLiMSuite package: (1) SLiMProb identifies and calculates enrichment of predefined motifs in a set of proteins; (2) SLiMFinder predicts SLiMs de novo in a set of proteins, accounting for evolutionary relationships; (3) QSLiMFinder increases SLiMFinder sensitivity by focusing SLiM prediction on a specific query protein/region; (4) CompariMotif compares predicted SLiMs to known SLiMs or other SLiM predictions to identify common patterns. For each tool, command-line and online server examples are provided. Detailed notes provide additional advice on different applications of SLiMSuite, including batch running of multiple datasets and conservation masking using alignments of predicted orthologues.
