The Eukaryotic Linear Motif Resource (ELM): Regulatory Sites in Proteins

AbstractLinear motifs are short and evolutionarily variable sequence patterns associated with particular functions often involving post-translational modifications, such as phosphorylation, acetylation, glycosylation, targeting signals for cellular compartments, protein cleavage sites and protein–protein interaction.Experimentally they are often neglected because their short length (4-10 residues long), and the fact that they often reside in disordered regions in proteins makes them difficult to detect. For a similar reason, using the sole regular expression to detect linear motifs matches in sequences has almost no predictive power because they are both statistically insignificant and prone to massive over-prediction.The Eukaryotic Linear Motif resource (ELM - "http://elm.eu.org":http://elm.eu.org) is a bioinformatics facility for investigating candidate short functional motifs in eukaryotic proteins. The ELM database to date has collected more than 140 motifs and their regular expressions patterns as well as information about their instances of occurrence, distribution, crystal structure, publications, etc.In order to reduce the over-prediction inherent to pattern matching against protein sequences and to discriminate true from false positive motif matches, context-based rules and logical filters are applied. The current version includes cell compartment, phylogeny, globular domain clash filters and the more recent structural filter based on known three-dimensional information that relies on structural information, such as residue solvent accessibility and secondary structure features. This implies that a candidate motif can be excluded from further consideration if the protein resides in the wrong cellular compartment or the motif is buried in the core of a globular domain. By considering additional types of context information, we expect that prediction of functional sites by ELM can be considerably improved. In cases where the user cannot provide relevant context information, we consider providing predictions of contextual information in order to improve the ELM performance. For example, since the ELM motif database has been annotated with biological process GO terms, the system could be prepared for addition of a new context filter using biological process.