Exploring Short Linear Motifs Using the ELM Database and Tools. Issue 1 (27th June 2017)

• Record Type: Journal Article
• Authors: Gouw, Marc; Sámano‐Sánchez, Hugo; Van Roey, Kim; Diella, Francesca; Gibson, Toby J.; Dinkel, Holger
• Editors: Bateman, Alex; Pearson, William R.; Stein, Lincoln D.; Stormo, Gary D.; Yates, John R.
• Is Part Of: Current protocols in bioinformatics. Volume 58:Issue 1(2017)
• Page Start: 8.22.1
• Page End: 8.22.35
• Publication Date: 2017-06-27
• Subjects: short linear motifs -- bioinformatics -- protein‐protein interaction -- molecular switches -- cell regulation; Bioinformatics -- Laboratory manuals; Nucleotide sequence -- Laboratory manuals; Amino acid sequence -- Laboratory manuals; Base Sequence; Amino Acid Sequence; Computational Biology -- methods; Databases, Genetic; Proteins -- analysis; Sequence Analysis -- methods; Sequence Homology; Amino acid sequence; Bioinformatics; Nucleotide sequence; Laboratory Manuals; Laboratory manuals; 570.285
• DOI: 10.1002/cpbi.26
• Languages: English
• ISSNs: 1934-3396

Abstract

Abstract: The Eukaryotic Linear Motif (ELM) resource is dedicated to the characterization and prediction of short linear motifs (SLiMs). SLiMs are compact, degenerate peptide segments found in many proteins and essential to almost all cellular processes. However, despite their abundance, SLiMs remain largely uncharacterized. The ELM database is a collection of manually annotated SLiM instances curated from experimental literature. In this article we illustrate how to browse and search the database for curated SLiM data, and cover the different types of data integrated in the resource. We also cover how to use this resource in order to predict SLiMs in known as well as novel proteins, and how to interpret the results generated by the ELM prediction pipeline. The ELM database is a very rich resource, and in the following protocols we give helpful examples to demonstrate how this knowledge can be used to improve your own research. © 2017 by John Wiley & Sons, Inc.