Nicolas Palopoli & Richard J Edwards
Protein-protein interactions (PPI) between globular domains and Short Linear Motifs (SLiMs) play a crucial part in many biological processes. SLiMs are short stretches of 5 to 15 amino acids with high evolutionary plasticity that are usually found in disordered regions of proteins. Their role as ligands for molecular signalling, post-translational modifications and subcellular targeting has been increasingly studied over recent years, but experimental discovery of SLiMs remains a challenging task due to their small size and high degeneracy. As a consequence, computational tools for prediction and analysis of SLiMs are a valuable resource.
We have previously developed SLiMFinder, a motif discovery tool that applies a model of convergent evolution to estimate the statistical significance of over-represented motifs with high specificity. Here, we aim to improve motif discovery by integrating SLiMFinder with methods that predict new domain-motif interactions directly from structural features in high-resolution 3D data. To this end we have developed “Query” SLiMFinder (QSLiMFinder), which uses knowledge of the interaction interface to constrain the motif search space and thereby increase search sensitivity. We have benchmarked QSLiMFinder using the Eukaryotic Linear Motif (ELM) database and simulated data. As expected, specific domain-motif interaction data can increase the power of de novo SLiM prediction from a set of proteins with a common PPI partner.
We are now applying QSLiMFinder to large-scale analysis of public PPI and 3D structure data. Domain-motif interactions are predicted from structures in the protein data bank (PDB). QSLiMFinder then identifies patterns within the putative motif region that are over-represented in the other known PPI partners of the domain-containing protein. This will add crucial molecular details to the interactome.