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Short Linear Motifs (SLiMs) are functional protein microdomains that typically mediate interactions between a short linear region in one protein and a globular domain in another. SLiMs usually occur in structurally disordered regions and mediate low affinity interactions. Most SLiMs are 3-15 amino acids in length and have 2-5 defined positions, making them highly likely to occur by chance and extremely difficult to identify. Nevertheless, our knowledge of SLiMs and capacity to predict them from protein sequence data using computational methods has advanced dramatically over the past decade. By considering the biological, structural, and evolutionary context of SLiM occurrences, it is possible to differentiate functional instances from chance matches in many cases and to identify new regions of proteins that have the features consistent with a SLiM-mediated interaction. Their simplicity also makes SLiMs evolutionarily labile and prone to independent origins on different sequence backgrounds through convergent evolution, which can be exploited for predicting novel SLiMs in proteins that share a function or interaction partner. In this review, we explore our current knowledge of SLiMs and how it can be applied to the task of predicting them computationally from protein sequences. Rather than focusing on specific SLiM prediction tools, we provide an overview of the methods available and concentrate on principles that should continue to be paramount even in the light of future developments. We consider the relative merits of using regular expressions or profiles for SLiM discovery and discuss the main considerations for both predicting new instances of known SLiMs, and de novo prediction of novel SLiMs. In particular, we highlight the importance of correctly modelling evolutionary relationships and the probability of false positive predictions.

Many of the specific functions of intrinsically disordered protein segments are mediated by Short Linear Motifs (SLiMs) interacting with other proteins. Well known examples include SLiMs that interact with 14-3-3, PDZ, SH2, SH3, and WW domains but the true extent and diversity of SLiM-mediated interactions is largely unknown. Here, we attempt to expand our knowledge of human SLiMs by applying in silico SLiM prediction to the human interactome. Combining data from seven different interaction databases, we analysed approximately 6000 protein-centred and 1600 domain-centred human interaction datasets of 3+ unrelated proteins that interact with a common partner. Results were placed in context through comparison to randomised datasets of similar size and composition. The search returned thousands of evolutionarily conserved, intrinsically disordered occurrences of hundreds of significantly enriched recurring motifs, including many that have never been previously identified (). In addition to True Positive results for at least 25 different known SLiMs, a striking number of "off-target" proteins/domains also returned significantly enriched known motifs. Often, this was due to the non-independence of the datasets, with many proteins sharing interaction partners or contributing interactions to multiple domain datasets. The majority of these motif classes, however, were also found to be significantly enriched in one or more randomised datasets. This highlights the need for care when interpreting motif predictions of this nature but also raises the possibility that SLiM occurrences may be successfully identified independently of interaction data. Although not as compositionally biased as previous studies, patterns matching known SLiMs tended to cluster into a few large groups of similar sequence, while novel predictions tended to be more distinctive and less abundant. Whether this is due to ascertainment bias or a true functional composition bias of SLiMs is not clear and warrants further investigation.

Emiliania huxleyi is a unicellular marine phytoplankton species known to play a significant role in global biogeochemistry. Through the dual roles of photosynthesis and production of calcium carbonate (calcification), carbon is transferred from the atmosphere to ocean sediments. Almost nothing is known about the molecular mechanisms that control calcification, a process that is tightly regulated within the cell. To initiate proteomic studies on this important and phylogenetically remote organism, we have devised efficient protein extraction protocols and developed a bioinformatics pipeline that allows the statistically robust assignment of proteins from MS/MS data using preexisting EST sequences. The bioinformatics tool, termed BUDAPEST (Bioinformatics Utility for Data Analysis of Proteomics using ESTs), is fully automated and was used to search against data generated from three strains. BUDAPEST increased the number of identifications over standard protein database searches from 37 to 99 proteins when data were amalgamated. Proteins involved in diverse cellular processes were uncovered. For example, experimental evidence was obtained for a novel type I polyketide synthase and for various photosystem components. The proteomic and bioinformatic approaches developed in this study are of wider applicability, particularly to the oceanographic community where genomic sequence data for species of interest are currently scarce.

Integrins are transmembrane proteins regulating cellular shape, mobility and the cell cycle. A highly conserved signature motif in the cytoplasmic tail of the integrin alpha-subunit, KXGFFKR, plays a critical role in regulating integrin function. To date, six proteins have been identified that target this motif of the platelet-specific integrin alpha(IIb)beta(3). We employ peptide-affinity chromatography followed-up with LC-MS/MS analysis as well as protein chips to identify new potential regulators of integrin function in platelets and put them into their biological context using information from protein:protein interaction (PPI) databases. Totally, 44 platelet proteins bind with high affinity to an immobilized LAMWKVGFFKR-peptide. Of these, seven have been reported in the PPI literature as interactors with integrin alpha-subunits. 68 recombinant human proteins expressed on the protein chip specifically bind with high affinity to biotin-tagged alpha-integrin cytoplasmic peptides. Two of these proteins are also identified in the peptide-affinity experiments, one is also found in the PPI databases and a further one is present in the data to all three approaches. Finally, novel short linear interaction motifs are common to a number of proteins identified.

Short linear motifs (SLiMs) in proteins can act as targets for proteolytic cleavage, sites of post-translational modification, determinants of sub-cellular localization, and mediators of protein-protein interactions. Computational discovery of SLiMs involves assembling a group of proteins postulated to share a potential motif, masking out residues less likely to contain such a motif, down-weighting shared motifs arising through common evolutionary descent, and calculation of statistical probabilities allowing for the multiple testing of all possible motifs. Much of the challenge for motif discovery lies in the assembly and masking of datasets of proteins likely to share motifs, since the motifs are typically short (between 3 and 10 amino acids in length), so that potential signals can be easily swamped by the noise of stochastically recurring motifs. Focusing on disordered regions of proteins, where SLiMs are predominantly found, and masking out non-conserved residues can reduce the level of noise but more work is required to improve the quality of high-throughput experimental datasets (e.g. of physical protein interactions) as input for computational discovery.

Background: Large datasets of protein interactions provide a rich resource for the discovery of Short Linear Motifs (SLiMs) that recur in unrelated proteins. However, existing methods for estimating the probability of motif recurrence may be biased by the size and composition of the search dataset, such that p-value estimates from different datasets, or from motifs containing different numbers of non-wildcard positions, are not strictly comparable. Here, we develop more exact methods and explore the potential biases of computationally efficient approximations., Results: A widely used heuristic for the calculation of motif over-representation approximates motif probability by assuming that all proteins have the same length and composition. We introduce pv, which calculates the probability exactly. Secondly, the recently introduced SLiMFinder statistic Sig, accounts for multiple testing (across all possible motifs) in motif discovery. However, it approximates the probability of all other possible motifs, occurring with a score of p or less, as being equal to p. Here, we show that the exhaustive calculation of the probability of all possible motif occurrences that are as rare or rarer than the motif of interest, Sig', may be carried out efficiently by grouping motifs of a common probability (i.e. those which have permuted orders of the same residues). Sig'v, which corrects both approximations, is shown to be uniformly distributed in a random dataset when searching for non-ambiguous motifs, indicating that it is a robust significance measure., Conclusions: A method is presented to compute exactly the true probability of a non-ambiguous short protein sequence motif, and the utility of an approximate approach for novel motif discovery across a large number of datasets is demonstrated.

Unlabelled: CompariMotif is a novel tool for making motif-motif comparisons, identifying and describing similarities between regular expression motifs. CompariMotif can identify a number of different relationships between motifs, including exact matches, variants of degenerate motifs and complex overlapping motifs. Motif relationships are scored using shared information content, allowing the best matches to be easily identified in large comparisons. Many input and search options are available, enabling a list of motifs to be compared to itself (to identify recurring motifs) or to datasets of known motifs., Availability: CompariMotif can be run online at http://bioware.ucd.ie/ and is freely available for academic use as a set of open source Python modules under a GNU General Public License from http://bioinformatics.ucd.ie/shields/software/comparimotif/

Background: Short linear motifs (SLiMs) in proteins are functional microdomains of fundamental importance in many biological systems. SLiMs typically consist of a 3 to 10 amino acid stretch of the primary protein sequence, of which as few as two sites may be important for activity, making identification of novel SLiMs extremely difficult. In particular, it can be very difficult to distinguish a randomly recurring "motif" from a truly over-represented one. Incorporating ambiguous amino acid positions and/or variable-length wildcard spacers between defined residues further complicates the matter., Methodology/principal Findings: In this paper we present two algorithms. SLiMBuild identifies convergently evolved, short motifs in a dataset of proteins. Motifs are built by combining dimers into longer patterns, retaining only those motifs occurring in a sufficient number of unrelated proteins. Motifs with fixed amino acid positions are identified and then combined to incorporate amino acid ambiguity and variable-length wildcard spacers. The algorithm is computationally efficient compared to alternatives, particularly when datasets include homologous proteins, and provides great flexibility in the nature of motifs returned. The SLiMChance algorithm estimates the probability of returned motifs arising by chance, correcting for the size and composition of the dataset, and assigns a significance value to each motif. These algorithms are implemented in a software package, SLiMFinder. SLiMFinder default settings identify known SLiMs with 100% specificity, and have a low false discovery rate on random test data., Conclusions/significance: The efficiency of SLiMBuild and low false discovery rate of SLiMChance make SLiMFinder highly suited to high throughput motif discovery and individual high quality analyses alike. Examples of such analyses on real biological data, and how SLiMFinder results can help direct future discoveries, are provided. SLiMFinder is freely available for download under a GNU license from http://bioinformatics.ucd.ie/shields/software/slimfinder/.

Short, linear motifs (SLiMs) play a critical role in many biological processes, particularly in protein-protein interactions. Overrepresentation of convergent occurrences of motifs in proteins with a common attribute (such as similar subcellular location or a shared interaction partner) provides a feasible means to discover novel occurrences computationally. The SLiMDisc (Short, Linear Motif Discovery) web server corrects for common ancestry in describing shared motifs, concentrating on the convergently evolved motifs. The server returns a listing of the most interesting motifs found within unmasked regions, ranked according to an information content-based scoring scheme. It allows interactive input masking, according to various criteria. Scoring allows for evolutionary relationships in the data sets through treatment of BLAST local alignments. Alongside this ranked list, visualizations of the results improve understanding of the context of suggested motifs, helping to identify true motifs of interest. These visualizations include alignments of motif occurrences, alignments of motifs and their homologues and a visual schematic of the top-ranked motifs. Additional options for filtering and/or re-ranking motifs further permit the user to focus on motifs with desired attributes. Returned motifs can also be compared with known SLiMs from the literature. SLiMDisc is available at: http://bioware.ucd.ie/~slimdisc/.

Gene duplication and loss are predicted to be at least of the order of the substitution rate and are key contributors to the development of novel gene function and overall genome evolution. Although it has been established that proteins evolve more rapidly after gene duplication, we were interested in testing to what extent this reflects causation or association. Therefore, we investigated the rate of evolution prior to gene duplication in chordates. Two patterns emerged; firstly, branches, which are both preceded by a duplication and followed by a duplication, display an elevated rate of amino acid replacement. This is reflected in the ratio of nonsynonymous to synonymous substitution (mean nonsynonymous to synonymous nucleotide substitution rate ratio [Ka:Ks]) of 0.44 compared with branches preceded by and followed by a speciation (mean Ka:Ks of 0.23). The observed patterns suggest that there can be simultaneous alteration in the selection pressures on both gene duplication and amino acid replacement, which may be consistent with co-occurring increases in positive selection, or alternatively with concurrent relaxation of purifying selection. The pattern is largely, but perhaps not completely, explained by the existence of certain families that have elevated rates of both gene duplication and amino acid replacement. Secondly, we observed accelerated amino acid replacement prior to duplication (mean Ka:Ks for postspeciation preduplication branches was 0.27). In some cases, this could reflect adaptive changes in protein function precipitating a gene duplication event. In conclusion, the circumstances surrounding the birth of new proteins may frequently involve a simultaneous change in selection pressures on both gene-copy number and amino acid replacement. More precise modeling of the relative importance of preduplication, postduplication, and simultaneous amino acid replacement will require larger and denser genomic data sets from multiple species, allowing simultaneous estimation of lineage-specific fluctuations in mutation rates and adaptive constraints.

Summary: Burst After Duplication with Ancestral Sequence Predictions (BADASP) is a software package for identifying sites that may confer subfamily-specific biological functions in protein families following functional divergence of duplicated proteins. A given protein phylogeny is grouped into subfamilies based on orthology/paralogy relationships and/or user definitions. Ancestral sequences are then predicted from the sequence alignment and the functional specificity is calculated using variants of the Burst After Duplication method, which tests for radical amino acid substitutions following gene duplications that are subsequently conserved. Statistics are output along with subfamily groupings and ancestral sequences for an easy analysis with other packages., Availability: BADASP is freely available from http://www.bioinformatics.rcsi.ie/~redwards/badasp/

Background: The prediction of ancestral protein sequences from multiple sequence alignments is useful for many bioinformatics analyses. Predicting ancestral sequences is not a simple procedure and relies on accurate alignments and phylogenies. Several algorithms exist based on Maximum Parsimony or Maximum Likelihood methods but many current implementations are unable to process residues with gaps, which may represent insertion/deletion (indel) events or sequence fragments., Results: Here we present a new algorithm, GASP (Gapped Ancestral Sequence Prediction), for predicting ancestral sequences from phylogenetic trees and the corresponding multiple sequence alignments. Alignments may be of any size and contain gaps. GASP first assigns the positions of gaps in the phylogeny before using a likelihood-based approach centred on amino acid substitution matrices to assign ancestral amino acids. Important outgroup information is used by first working down from the tips of the tree to the root, using descendant data only to assign probabilities, and then working back up from the root to the tips using descendant and outgroup data to make predictions. GASP was tested on a number of simulated datasets based on real phylogenies. Prediction accuracy for ungapped data was similar to three alternative algorithms tested, with GASP performing better in some cases and worse in others. Adding simple insertions and deletions to the simulated data did not have a detrimental effect on GASP accuracy., Conclusions: GASP (Gapped Ancestral Sequence Prediction) will predict ancestral sequences from multiple protein alignments of any size. Although not as accurate in all cases as some of the more sophisticated maximum likelihood approaches, it can process a wide range of input phylogenies and will predict ancestral sequences for gapped and ungapped residues alike.