An emerging field of biology is the role of intrinsically disordered regions in protein function and, specifically, protein-protein interactions (PPI) [1-2]. Of particular interest, Short, Linear Motifs (SLiMs) playing a vital role in disorder-mediated PPI, acting as ligands for molecular signalling, post-translational modifications and subcellular targeting [3]. SLiMs have extremely compact protein interaction interfaces, generally encoded by less than 4 major affinity-/specificity-determining residues within a stretch of 2-10 residues [4]. Their small size enables high functional density and evolutionary plasticity, which is frequently exploited by rapidly evolving pathogens that use them to hijack cellular processes [5]. These same features also make experimental discovery a challenge and considerable attention has therefore been given to computational methods for SLiM prediction and analysis [6].
A number of these tools have been developed by the Edwards and Shields labs [7-11] and made available as part of the
SLiMSuite package and online as webservers (
http://bioware.ucd.ie) [9-10,12-14], with two new tools, SLiMPrints and QSLiMFinder, currently in preparation for submission, and
SLiMMaker to be added soon. The main tools that form the SLiMSuite package/servers are as follows:
- SLiMFinder [8,13]: de novo SLiM prediction based on a statistical model of over-represented motifs in unrelated proteins.
- SLiMDisc [7,12]: de novo SLiM prediction based on heuristic ranking of over-represented motifs in unrelated proteins.
- SLiMPred [11]: de novo SLiM/MoRF prediction in single proteins based machine learning of motif attributes.
- SLiMSearch [10]: biological context (disorder & conservation) for searches of pre-defined motifs with under- and over-representation statistics, correcting for evolutionary relationships.
- SLiMSearch 2.0 [14]: biological context (disorder & conservation) and ranking for proteome-wide searches of pre-defined motifs.
- SLiMPrints (in prep.): de novo SLiM/MoRF prediction in single proteins from statistical clustering of conserved disordered residues.
- QSLiMFinder (server coming soon): Query-based variant of SLiMFinder with increased sensitivity and specificity.
- CompariMotif [9]: Motif-motif comparison tool.
- SLiMMaker (coming soon): Simple tool for converting aligned peptides or SLiM occurrences into a regular expression motif.
- GOPHER [12]: Automated orthologue prediction and alignment algorithm. Used for conservation-based masking (SLiMFinder/SLiMSearch) and prediction (SLiMPrints).
- GABLAM [7] (server coming soon): BLAST-based protein similarity scoring and clustering. Used for SLiMFinder and SLiMSearch adjustments for evolutionary relationships.
Personnel (and funding applications) permitting, a number of improvements for these resources are planned, including updates to the underlying databases for proteome-wide predictions (SLiMSearch 1.0 & 2.0), conservation analyses (SLiMSearch 1.0 & 2.0, SLiMPrints, GOPHER) and SLiM comparisons (CompariMotif). We also intend to improve the integration of different tools, allowing seamless continuation of analyses. Motif predictions ((Q)SLiMFinder/SLiMPrints/SLiMPred) will be able to be searched directly against known motifs (CompariMotif) or proteomes (SLiMSearch); GOPHER alignments will be accessible for SLiMPrints analyses and even SLiMSearch/(Q)SLiMFinder input; outputs of motif occurrences ((Q)SLiMFinder/SLiMSearch) can be used to redefine motifs using SLiMMaker etc. If you have any other suggestions for improvements, please let us know.
References:
[1] Tompa P (2011) Unstructural biology coming of age. Curr Opin Struct Biol 21: 419; [2] Babu MM et al. (2011) Intrinsically disordered proteins: regulation and disease. Curr Opin Struct Biol 21:432; [3] Diella F et al. (2008) Understanding eukaryotic linear motifs and their role in cell signaling and regulation. Front Biosci 13:6580; [4] Davey NE et al. (2012) Attributes of short linear motifs. Mol Biosyst 8:268; [5] Davey NE, Trave G & Gibson TJ (2011) How viruses hijack cell regulation. Trends Biochem Sci 36:159; [6] Davey NE, Edwards RJ & Shields DC (2010) Computational identification and analysis of protein short linear motifs. Front Biosci 15:801; [7] Davey NE, Shields DC & Edwards RJ (2006): SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent. Nucleic Acids Res. 34:3546; [8] Edwards RJ, Davey NE & Shields DC (2007): SLiMFinder: A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins. PLoS ONE 2:e967; [9] Edwards RJ, Davey NE & Shields DC (2008): CompariMotif: Quick and easy comparisons of sequence motifs. Bioinformatics 24:1307; [10] Davey NE et al. (2010): SLiMSearch: a webserver for finding novel occurrences of short linear motifs in proteins, incorporating sequence context. Lecture Notes in Bioinformatics 6282:50; [11] Mooney C et al. (2012): Prediction of short linear protein binding regions. J Mol Biol 415:193; [12] Davey NE, Edwards RJ & Shields DC (2007): The SLiMDisc server: short, linear motif discovery in proteins. Nuc Acids Res 35:W455; [13] Davey NE et al. (2010): SLiMFinder: a web server to find novel, significantly over-represented, short protein motifs. Nuc Acids Res 38:W534; [14] Davey NE et al. (2011): SLiMSearch 2.0: biological context for short linear motifs in proteins. Nuc Acids Res 39:W56.
