Computational Prediction of Disordered Protein Motifs Using SLiMSuite

Edwards RJ, Paulsen K, Aguilar Gomez CM & Pérez-Bercoff Å (2020): Computational Prediction of Disordered Protein Motifs using SLiMSuite. Methods Mol Biol. 2141:37-72. doi: 10.1007/978-1-0716-0524-0_3. [PubMed]

Abstract

Short linear motifs (SLiMs) are important mediators of interactions between intrinsically disordered regions of proteins and their interaction partners. Here, we detail instructions for the computational prediction of SLiMs in disordered protein regions, using the main tools of the SLiMSuite package: (1) SLiMProb identifies and calculates enrichment of predefined motifs in a set of proteins; (2) SLiMFinder predicts SLiMs de novo in a set of proteins, accounting for evolutionary relationships; (3) QSLiMFinder increases SLiMFinder sensitivity by focusing SLiM prediction on a specific query protein/region; (4) CompariMotif compares predicted SLiMs to known SLiMs or other SLiM predictions to identify common patterns. For each tool, command-line and online server examples are provided. Detailed notes provide additional advice on different applications of SLiMSuite, including batch running of multiple datasets and conservation masking using alignments of predicted orthologues.

SLiMEnrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

Idrees S, Pérez-Bercoff Å & Edwards RJ. (2018) SLiMEnrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions. PeerJ 6:e5858 https://doi.org/10.7717/peerj.5858

Abstract

Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiMEnrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiMEnrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiMEnrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiMEnrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.

The SLiMEnrich Shiny App is now live

Sobia’s first Shiny App is now up and running for final pre-publication testing on our new EdwardsLab RShiny server. Please feel free to try it out. Any comments and suggestions can be posted here, by email, or via the GitHub issues page.

The SLiMEnrich App allows users to predict Domain-Motif Interactions (DMIs) from Protein-Protein Interaction data (PPI) and to estimate the background distribution of expected DMI by chance through a randomisation approach. The walkthrough has more details.

SLiMEnrich can be used to:

Estimate the enrichment of SLiM-mediated DMI in a given PPI dataset.

Generate predictions for DMI-mediated from a PPI dataset. Predictions are based on known SLiM-Domain interactions.

Estimate the False Positive Rate of those DMI predictions.

With a bit of imagination, SLiMEnrich can be adapted to generate and assess predictions for other kinds of interactions. See docs for details.

SLiMEnrich is available via the SLiMEnrich RShiny webserver and can be downloaded for local running from the SLiMEnrich GitHub repo.

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 (2012): SLiMPrints: conservation-based discovery of functional motif fingerprints in intrinsically disordered protein regions. Nucleic Acids Research 40(21):10628-41.

Abstract

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.

PMID: 22977176

SLiMSearch 2.0: biological context for short linear motifs in proteins

Davey NE, Haslam NJ, Shields DC & Edwards RJ (2011): SLiMSearch 2.0: biological context for short linear motifs in proteins. Nucleic Acids Research 39: W56-W60.

Abstract

Short, linear motifs (SLiMs) play a critical role in many biological processes. The SLiMSearch 2.0 (Short, Linear Motif Search) web server allows researchers to identify occurrences of a user-defined SLiM in a proteome, using conservation and protein disorder context statistics to rank occurrences. User-friendly output and visualizations of motif context allow the user to quickly gain insight into the validity of a putatively functional motif occurrence. For each motif occurrence, overlapping UniProt features and annotated SLiMs are displayed. Visualization also includes annotated multiple sequence alignments surrounding each occurrence, showing conservation and protein disorder statistics in addition to known and predicted SLiMs, protein domains and known post-translational modifications. In addition, enrichment of Gene Ontology terms and protein interaction partners are provided as indicators of possible motif function. All web server results are available for download. Users can search motifs against the human proteome or a subset thereof defined by Uniprot accession numbers or GO term. The SLiMSearch server is available at: http://bioware.ucd.ie/slimsearch2.html.

PMID: 21622654

SLiMSearch: a webserver for finding novel occurrences of short linear motifs in proteins, incorporating sequence context

Davey NE, Haslam NJ, Shields DC & Edwards RJ (2010): SLiMSearch: a webserver for finding novel occurrences of short linear motifs in proteins, incorporating sequence context. In: Pattern Recognition in Bioinformatics Edited by Dijkstra TMH, Tsivtsivadze E, Marchiori E & Heskes T. Springer-Verlag, Berlin. Lecture Notes in Bioinformatics 6282: 50-61.

Abstract

Short, linear motifs (SLiMs) play a critical role in many biological processes. The SLiMSearch (Short, Linear Motif Search) webserver is a flexible tool that enables researchers to identify novel occurrences of predefined SLiMs in sets of proteins. Numerous masking options give the user great control over the contextual information to be included in the analyses, including evolutionary filtering and protein structural disorder. User-friendly output and visualizations of motif context allow the user to quickly gain insight into the validity of a putatively functional motif occurrence. Users can search motifs against the human proteome, or submit their own datasets of UniProt proteins, in which case motif support within the dataset is statistically assessed for over- and under-representation, accounting for evolutionary relationships between input proteins. SLiMSearch is freely available as open source Python modules and all webserver results are available for download. The SLiMSearch server is available at: http://bioware.ucd.ie/slimsearch.html.

SLiMFinder: a web server to find novel, significantly over-represented, short protein motifs

Davey NE, Haslam NJ, Shields DC & Edwards RJ (2010): SLiMFinder: a web server to find novel, significantly over-represented, short protein motifs. Nucleic Acids Research 38: W534-W539.

Abstract

Short, linear motifs (SLiMs) play a critical role in many biological processes, particularly in protein-protein interactions. The Short, Linear Motif Finder (SLiMFinder) web server is a de novo motif discovery tool that identifies statistically over-represented motifs in a set of protein sequences, accounting for the evolutionary relationships between them. Motifs are returned with an intuitive P-value that greatly reduces the problem of false positives and is accessible to biologists of all disciplines. Input can be uploaded by the user or extracted directly from UniProt. Numerous masking options give the user great control over the contextual information to be included in the analyses. The SLiMFinder server combines these with user-friendly output and visualizations of motif context to allow the user to quickly gain insight into the validity of a putatively functional motif. These visualizations include alignments of motif occurrences, alignments of motifs and their homologues and a visual schematic of the top-ranked motifs. Returned motifs can also be compared with known SLiMs from the literature using CompariMotif. All results are available for download. The SLiMFinder server is available at: http://bioware.ucd.ie/slimfinder.html.

PMID: 20497999

CompariMotif: quick and easy comparisons of sequence motifs

Edwards RJ, Davey NE & Shields DC (2008): CompariMotif: Quick and easy comparisons of sequence motifs. Bioinformatics 24(10):1307-9.

Abstract

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/

PMID: 18375965

The SLiMDisc server: short, linear motif discovery in proteins

Davey NE*, Edwards RJ* & Shields DC (2007): The SLiMDisc server: short, linear motif discovery in proteins. Nucleic Acids Res. 35(Web Server issue):W455-9. *Joint first authors

Abstract

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/.

PMID: 17576682