SLiMPrints paper published

The SLiMPrints paper is now available online at Nucleic Acids Research (doi: 10.1093/nar/gks854; PMID: 22977176).

SLiMPrints: conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions

Davey NE, Cowan JL, Shields DC, Gibson TJ, Coldwell MJ, Edwards RJ.

Large portions of higher eukaryotic proteomes are intrinsically disordered, and abundant evidence suggests that these unstructured regions of proteins are rich in regulatory interaction interfaces. A major class of disordered interaction interfaces are the compact and degenerate modules known as short linear motifs (SLiMs). As a result of the difficulties associated with the experimental identification and validation of SLiMs, our understanding of these modules is limited, advocating the use of computational methods to focus experimental discovery. This article evaluates the use of evolutionary conservation as a discriminatory technique for motif discovery. A statistical framework is introduced to assess the significance of relatively conserved residues, quantifying the likelihood a residue will have a particular level of conservation given the conservation of the surrounding residues. The framework is expanded to assess the significance of groupings of conserved residues, a metric that forms the basis of SLiMPrints (short linear motif fingerprints), a de novo motif discovery tool. SLiMPrints identifies relatively overconstrained proximal groupings of residues within intrinsically disordered regions, indicative of putatively functional motifs. Finally, the human proteome is analysed to create a set of highly conserved putative motif instances, including a novel site on translation initiation factor eIF2A that may regulate translation through binding of eIF4E.

Server available at bioware.ucd.ie.

SLiMSuite servers and programs

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.