PPI-Net Poster: Computational prediction of short linear motifs integrating protein-protein interactions, sequence and structural data

Nico’s poster from the 3rd Protein-Protein Interaction Network (PPI-Net) Young Researchers Symposium is now available on F1000Posters:

Nicolas Palopoli & Richard J Edwards (2014) Computational prediction of short linear motifs integrating protein-protein interactions, sequence and structural data. F1000Posters 5: 407.

Abstract

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 presented SLiMFinder, a motif discovery tool that applies a model of convergent evolution to estimate the statistical significance of over-represented motifs. In this project 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. Using putative SLiM-carrying regions extracted from protein structures as queries and targeting data from public protein-protein interaction databases, we applied QSLiMFinder to find over-represented recurring sequence patterns from proteins that all share a common interaction partner.

Benchmarking of QSLiMFinder shows that specific domain-motif interaction data help in finding novel instances of known motifs or entire de novo SLiMs, improving over results returned by PPI data alone. SLiM discovery capitalizes from the availability of experimentally identified PPIs with high-quality predictions of the interacting sites. The methods developed here help to enhance annotation in public databases of SLiMs and could be used to mine new PPI data as it becomes available, adding molecular detail to interactome networks.