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. 2012 Jan;40(Database issue):D242-51.
doi: 10.1093/nar/gkr1064. Epub 2011 Nov 21.

ELM--the database of eukaryotic linear motifs

Holger Dinkel  1 , Sushama Michael, Robert J Weatheritt, Norman E Davey, Kim Van Roey, Brigitte Altenberg, Grischa Toedt, Bora Uyar, Markus Seiler, Aidan Budd, Lisa Jödicke, Marcel A Dammert, Christian Schroeter, Maria Hammer, Tobias Schmidt, Peter Jehl, Caroline McGuigan, Magdalena Dymecka, Claudia Chica, Katja Luck, Allegra Via, Andrew Chatr-Aryamontri, Niall Haslam, Gleb Grebnev, Richard J Edwards, Michel O Steinmetz, Heike Meiselbach, Francesca Diella, Toby J Gibson
Affiliations

ELM--the database of eukaryotic linear motifs

Holger Dinkel et al. Nucleic Acids Res. 2012 Jan.

Abstract

Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.

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Figures

Figure 1.
Figure 1.
ELM start page. The user can submit a query sequence to the motif detection pipeline either as UniProt accession number or in FASTA format. Filtering criteria such as taxonomic range or cellular compartment should be activated to limit the resulting list of SLiM instances.
Figure 2.
Figure 2.
ELM motif detection pipeline output page. The top legend explains the different colors/symbols used. The graphical output of ELM concentrates the output of multiple sequence classification algorithms; phosphorylation sites from Phospho.ELM, protein domains detected by SMART/Pfam, disorder predictions by GlobPlot and IUPred and secondary structure (18). The lower part contains the annotated and putative ELM instances for the given protein sequence (Epsin1, UniProt accession Q9Y6I3). The background is colored according to the structural information available. Each box represents one ELM instance, the color of which indicates the likelihood that this instance is functional: grey instances are buried within structured regions, while shades of blue represent instances outside of structured regions and hint on sequence conservation, with pale blue representing weak sequence conservation and dark blue indicating strong sequence conservation. Red ellipses or boxes mark instances that are annotated in the query sequence or a homologous sequence, respectively.
Figure 3.
Figure 3.
ELM detail page showing information about the ELM class TRG_AP2beta_CARGO_1.
Figure 4.
Figure 4.
ELM instances browse page. A full-text search (here, search term used was ‘AP2’, filtering for ‘true positive’ instances in taxon ‘Homo sapiens’, yielding 58 instances) assists in finding annotated instances. A search can be restricted to a particular taxonomy or instance logic (top) or ELM class type (buttons on the left). The list can also be exported to TSV or FASTA format for further processing.
Figure 5.
Figure 5.
Schema of the ELM resource and data life cycle. Annotated ELM classes, and instances thereof, can be searched by database query. Via sequence search by the motif detection pipeline, annotated ELM classes yield putative instances in query sequences. By adding experimental evidence and references, these putative instances become candidate instances for annotation, and, with further curation, ultimately become fully annotated instances.
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