SLiMScape: a protein short linear motif analysis plugin for Cytoscape.

New paper published!

O’Brien KT, Haslam NJ & Shields DC (2013). SLiMScape: a protein short linear motif analysis plugin for Cytoscape. BMC Bioinformatics 14(1):224. [Epub ahead of print]

BACKGROUND: Computational protein short linear motif discovery can use protein interaction information to search for motifs among proteins which share a common interactor. Cytoscape provides a visual interface for protein networks but there is no streamlined way to rapidly visualize motifs in a network of proteins, or to integrate computational discovery with such visualizations.

RESULTS: We present SLiMScape, a Cytoscape plugin, which enables both de novo motif discovery and searches for instances of known motifs. Data is presented using Cytoscape’s visualization features thus providing an intuitive interface for interpreting results. The distribution of discovered or user defined motifs may be selectively displayed and the distribution of protein domains may be viewed simultaneously. To facilitate this SLiMScape automatically retrieves domains for each protein.

CONCLUSION: SLiMScape provides a platform for performing short linear motif analyses of protein interaction networks by integrating motif discovery and searchtools in a network visualization environment. This significantly aids in the discovery of novel short linear motifs and in visualizing the distributionof known motifs.

PMID: 23855714

Second BUDAPEST paper published

A second paper using BUDAPEST, "Responses of the Emiliania huxleyi proteome to ocean acidification" came out on Friday. An overview can be found in a University of Southampton press release, Marine algae show resilience to carbon dioxide emissions.

A type of marine algae could become bigger as increasing carbon dioxide emissions are absorbed by the oceans, according to research led by scientists based at the National Oceanography Centre, Southampton (NOCS). The study, published this month in PLoS ONE, investigated how a strain of the coccolithophore Emiliania huxleyi might respond if all fossil fuels are burned by the year 2100 – predicted to drive up atmospheric CO2 levels to over four times the present day.

You can read the rest of the press release here.

There are some additional images and a video in a UC Santa Barbara press release, which gives a good summary of the science in the study.

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Bethan M. Jones, M. Debora Iglesias-Rodriguez, Paul J. Skipp, Richard J. Edwards, Mervyn J. Greaves, Jeremy R. Young, Henry Elderfield, C. David O’Connor (2013) Responses of the Emiliania huxleyi proteome to ocean acidification. PLoS ONE, dx.plos.org/10.1371/journal.pone.0061868.

Using SLiMFinder to discover "local motifs" in protein sequences

The makers of the highly successful MEME Suite have another tool out:

DLocalMotif: A discriminative approach for discovering local motifs in protein sequences

I've not had a chance to go over it in detail but it looks like it could be pretty useful, especially for subcellular targeting motifs. There is one thing that rankles me slightly, though. They define a "local motif" as

"patterns in DNA or protein sequences that occur in a short sequence interval relative to a sequence anchor or landmark."

They then go on to say:

"We believe that DLocalMotif is the only tool for discovering local motifs in protein sequences."

This is just a quick post to point out that SLiMFinder will happily find "local motifs" in protein sequences using the start and end of the sequence as an anchor or landmark. I think it is more limited than DLocalMotif as it is restricted to SLiMs that are very proximal to the sequence termini but it features the usual SLiMChance probability calculations and corrections for evolutionary relationships. (Even without restricting to searches relative to anchor points, SLiMFinder is very successful at finding the KDEL motif and C-terminal PDZ ligand motifs.) The max distance from the termini can be set by maxwild=X up to a limit of 9aa.

If you want to restrict yourself to just N- or C-terminal motifs, use the musthave=LIST option:

musthave="^" for N-terminal motifs.

musthave="$" for C-terminal motifs.

musthave="^,$" for both.

If you want to anchor the motifs internally, this can be done too with a bit of imagination. Just insert an non-standard amino acid character (e.g. Z) at the anchor position, set the expanded alphabet using alphabet=LIST and then force the motif to have the new symbol using musthave=X, e.g.:

alphabet="A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y,Z" musthave=Z

I must confess that I have never tried this but it should work and I am happy to help iron out any wrinkles.

(BTW, SLiMFinder also has an experimental feature for using a negative dataset (negatives=FILE if anyone wants to try it out.)

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.