New look Bioware

The Bioware server has a new(ish!) look! The function of the tools should be much the same (although various updates are in progress) but the feel of the site should hopefully be cleaner and more consistent on mobile devices. Feedback welcome!

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.

Bioware servers back up

Following the recent move-induced technical issues, the Bioware webservers are now back on line, with the exception of SLiMSearch 1.0. SLiMSearch2 is also operating with reduced function until another upgrade can be performed in the near future. CompariMotif has been upgraded to Version 3.6 in line with the recent package updates.

A few of the links to help pages etc. might still be broken, so please report anything that is not quite behaving as expected.

SLiMSuite servers temporarily down

Following some technical issues that have arisen due to some quite major behind-the-scenes re-organisation, the Bioware webserver implementations of SLiMSuite programs have been temporarily taken off-line.

The plan is to give them a bit of attention, iron out any wrinkles that have developed, and then hopefully get them back online over the next week or so. The servers affected are:

• CompariMotif
  
• GOPHER
  
• SLiMDisc (now part of SLiMFinder)
  
• SLiMFinder
  
• SLiMSearch
  
• SLiMSearch 2.0
  

These programs (except SLiMSearch 2.0) are available for download. If you have a particular need for any of these servers, please contact me and I will accelerate its re-appearance.

As each server becomes available, it will be posted here.

Bioware servers behaving erratically

The SLiMSuite servers housed at bioware.ucd.ie are currently behaving a bit strangely. This is under investigation and we hope to have it sorted out soon. If you spot any odd or unexpected behaviour with the servers, please let us know.

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.

SLiMMaker: regular expressions from aligned peptide sequences

SLiMMaker has a fairly simple function of reading in a set of sequences and generating a regular expression motif from them. It is designed with protein sequences in mind but should work for DNA sequences too. Input sequences can be in fasta format or just plain text (with no sequence headers) and should be aligned already. Gapped positions will be ignored (treated as Xs) and variable length wildcards are not returned.

SLiMMaker considers each column of the input in turn and compresses it into a regular expression element according to some simple rules, screening out rare amino acids and converting particularly degenerate positions into wildcards. Each amino acid in the column that occurs at least X times (as defined by minseq=X) is considered for the regular expression definition for that position. The full set of amino acids meeting this criterion is then assessed for whether to keep it as a defined position, or convert into a wildcard.

First, if the number of different amino acids meeting this criterion is zero or above a second threshold (maxaa=X), the position is defined as a wildcard. Second, the proportion of input sequences matching the amino acid set is compared to a minimum frequency criterion (minfreq=X). Failing to meet this minimum frequency will again result in a wildcard. Otherwise, the amino acid set is added to the SLiM definition as either a fixed position (if only one amino acid met the minseq criterion) or as a degenerate position. Finally, leading and trailing wildcards are removed.

By default, each defined position in a motif will contain amino acids that (a) occur in at least three sequences each, (b) have a combined frequency of >=75%, and (c) have 5 or fewer different amino acids (that occur in 3+ sequences).

Note. The final motif only contains defined positions that match a given frequency of the input (75% by default). Because positions are considered independently, however, the final motif might occur in fewer than 75% of the input sequences. Results will indicate the coverage of the input data but SLiMSearch can be used to check the occurrence stats more thoroughly.

Citation: SLiMMaker is part of the ongoing benchmarking of QSLiMFinder, which should be submitted for publication soon. In the meantime, please cite the SLiMMaker URL: http://bioware.soton.ac.uk/slimmaker.html.

Availability: SLiMMaker is available on request and will shortly be part of the SLiMSuite package.