In line with the general retirement of SeqSuite in favour of bundling everything together in SLiMSuite, the old @seqsuite twitter account is also being retired. In its place is a new Edwards Lab account with the twitter handle @slimsuite. The tweeting remit will also be broadened to include a variety of lab-related activities, not just SLiMSuite-related matters.
Sunday, 3 August 2014
New lab Twitter feed (@slimsuite)
Wednesday, 30 July 2014
Edwards Lab at InCoB 2014
Rich and Ranjeeta will be attending the the International Conference on Bioinformatics (InCoB) 2014 conference in Sydney over the next three days. Say hello if you’re there too! Posters to follow.
Monday, 23 June 2014
New SLiMSuite release now available
Friday, 25 April 2014
SLiMSuite Short Linear Motif discovery and analysis: Blog switchover
Thursday, 24 April 2014
SLiMSuite 2014-04-22 now available
Monday, 7 April 2014
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.
