SLiMSuite & SeqSuite sequence analysis tools: SLiMSuite release v1.11.0 (2022-01-12)

SLiMSuite has (finally!) been updated in line with the recent waratah genome paper. See the SLiMSuite blog for more details:

SLiMSuite & SeqSuite sequence analysis tools: SLiMSuite release v1.11.0 (2022-01-12)

Carla Aguilar Gomez (Visiting researcher)

Carla Aguilar Gomez was a visiting researcher in the lab from May to July 2019, working on SLiM prediction from cross-linking mass spectrometry data in yeast. Carla left the lab to move to Austria for a PhD in biotechnology.

EMBL-ABR network: an interview with Richard Edwards

From the EMBL-ABR February newsletter:

On 3 February we released an interview with Richard Edwards, the developer of SLiMSuite, an open source bioinformatics tool for the prediction of short linear motifs (SLiMs) and related sequence analysis. In this interview Richard reflects on bioinformatics in Australia as well as the realities of building a sustainable model for the development and maintenance of useful bioinformatics tools such as his.

You can read the full interview: here.

Honours and undergrad research opportunities

Honours

BABS are currently recruiting the next cohort of Honours students for Semester 1 2017. As usual, the EdwardsLab is looking to recruit enthusiastic students in two main areas:

1. Functional genomics using long-read PacBio sequencing. We are particularly keen to get a student to work on either (a) aspects of our ARC Linkage grant, investigating the evolution of a novel biochemical pathway in yeast, or (b) de novo whole genome sequencing of the cane toad. We also have a number of projects with bacteria for those with a keen interest in microbiology. In each case, the lab is collaborating with experts in the relevant organisms.

2. Applying biological sequence analysis and molecular evolution to study the molecular basis of protein-protein interactions. The main lab software, SLiMSuite has a number of improvements and developments that would benefit from some dedicated attention from a research student. We are also looking for someone who might want to help develop the lab servers.

More details of honours can be found on the BABS website, or please get in touch if you have questions about specific projects. Applications from non-UNSW students are also encouraged.

* BABS are also running an Honours information and networking night on 16^th September.*

Summer Vacation Research Scholarships

BABS is once again running its highly successful Summer Vacation Research Scholarship (SVRS) scheme and the EdwardsLab are looking to take on one or two students in the same areas as indicated above.

How to apply

We do not yet have a specific undergraduate application form but it is helpful if you can follow the PhD application process and just make it clear that you are interested in Honours or SVRS. As well as helping select between applicants, this form is also useful for me to make sure that students are assigned an appropriate project.

Research snapshot: June 2016

Research interests in the Edwards lab stem from a fascination with the molecular basis of evolutionary change and how we can harness the genetic sequence patterns left behind to make useful predictions about contemporary biological systems.

The core research in the lab is the study of Short Linear Motifs (SLiMs), which are short regions of proteins that mediate interactions with other proteins. This research originated with Rich’s postdoctoral research, during which he developed a bioinformatics (sequence analysis) method for rational design of biologically active short peptides. He subsequently developed SLiMDisc, one of the first algorithms for successfully predicting novel SLiMs from sequence data - and coined the term “SLiM” into the bargain - before developing the first SLiM prediction algorithm able to estimate the statistical significance of motif predictions (SLiMFinder), which greatly increased the reliability of predictions. SLiMFinder has since spawned a number of motif discovery tools and webservers and is still arguably the most successful SLiM prediction tool on benchmarking data.

Current research is looking to develop these SLiM prediction tools further and apply them to important biological questions. Of particular interest is the molecular mimicry employed by viruses to interact with host proteins and the role of SLiMs in other diseases, such as cancer. Other work is concerned with the evolutionary dynamics of SLiMs within protein interaction networks.

A new and exciting area of research in the lab is functional genomics with PacBio long-read sequencing. We are collaborating with industrial and academic partners to de novo sequence, assemble, annotate and interrogate the genomes of a selection of microbes with interesting metabolic abilities.

Finally, the lab has a number of interdisciplinary collaborative projects applying bioinformatics tools and molecular evolution theory to experimental biology, often using large genomic, transcriptomic and/or proteomic datasets. These projects often involve the development of bespoke bioinformatics pipelines and a number of open source bioinformatics tools have been generated as a result.

#ABACBS2015 Poster P20: SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks

Emily Olorin, Kevin T. O’Brien, Nicolas Palopoli, Åsa Pérez-Bercoff, Denis C. Shields & Richard J. Edwards. http://f1000research.com/posters/4-1024.

Abstract

Short linear motifs (SLiMs) are small protein sequence patterns that mediate a large number of critical protein-protein interactions, involved in processes such as complex formation, signal transduction, localisation and stabilisation. SLiMs show rapid evolutionary dynamics and are frequently the targets of molecular mimicry by pathogens. Identifying enriched sequence patterns due to convergent evolution in non-homologous proteins has proven to be a successful strategy for computational SLiM prediction. Tools of the SLiMSuite package use this strategy, using a statistical model to identify SLiM enrichment based on the evolutionary relationships, amino acid composition and predicted disorder of the input proteins. The quality of input data is critical for successful SLiM prediction. Cytoscape provides a user-friendly, interactive environment to explore interaction networks and select proteins based on common features, such as shared interaction partners. SLiMScape embeds tools of the SLiMSuite package for de novo SLiM discovery (SLiMFinder and QSLiMFinder) and identifying occurrences/enrichment of known SLiMs (SLiMProb) within this interactive framework. SLiMScape makes it easier to (1) generate high quality hypothesis-driven datasets for these tools, and (2) visualise predicted SLiM occurrences within the context of the network. To generate new predictions, users can select nodes from a protein network or provide a set of Uniprot identifiers. SLiMProb also requires additional query motif input. Jobs are then run remotely on the SLiMSuite server (http://rest.slimsuite.unsw.edu.au) for subsequent retrieval and visualisation. SLiMScape can also be used to retrieve and visualise results from jobs run directly on the server.

New SLiMSuite release and GitHub site

A new download of SLiMSuite (release 2015-06-01) is now available. This is the first release in the new git repository at https://github.com/slimsuite/SLiMSuite. A tarball slimsuite.2015-06-01.tgz is also available, containing the same code. Once unpacked, it should be possible to pull down additional updates with git.

QSLiMFinder: improved short linear motif prediction using specific query protein data

Palopoli N, Lythgow KT & Edwards RJ (2015). QSLiMFinder: improved short linear motif prediction using specific query protein data. Bioinformatics 31(14): 2284-2293. [PDF]

Abstract

Motivation: The sensitivity of de novo short linear motif (SLiM) prediction is limited by the number of patterns (the motif space) being assessed for enrichment. QSLiMFinder uses specific query protein information to restrict the motif space and thereby increase the sensitivity and specificity of predictions.

Results: QSLiMFinder was extensively benchmarked using known SLiM-containing proteins and simulated protein interaction datasets of real human proteins. Exploiting prior knowledge of a query protein likely to be involved in a SLiM-mediated interaction increased the proportion of true positives correctly returned and reduced the proportion of datasets returning a false positive prediction. The biggest improvement was seen if a short region of the query protein flanking the interaction site was known.

Availability and Implementation: All the tools and data used in this study, including QSLiMFinder and the SLiMBench benchmarking software, are freely available under a GNU license as part of SLiMSuite, at: http://bioware.soton.ac.uk.

Supplementary information: Supplementary data are available at the journal’s web site and at http://bioware.soton.ac.uk/research/qslimfinder/.

PMID: 25792551

ABiC2014 posters on F1000Posters

Better late than never, both posters from ABiC2014 are now on F1000Posters, along with many other great posters from the conference:

Richard J Edwards, Ranjeeta Menon, Nicolas Palopoli & Jason FH Wong (2015) Molecular mimicry in viruses and cancer. F1000Posters 6: 101.

Nicolas Palopoli & Richard J Edwards (2014) Computational prediction of protein interaction motifs using interaction networks and 3-dimensional structures. F1000Posters 5: 1682.

Full text available for SLiM prediction review

For those who cannot access Methods in Molecular Biology, the final submitted draft of our recent paper is now available here.

The original publication is available at www.springerlink.com:

Edwards RJ & Palopoli N (2015): Computational Prediction of Short Linear Motifs from Protein Sequences. Methods Mol Biol. 1268:89-141.

SLiMSuite Short Linear Motif discovery and analysis: SLiMSuite release 2015-01-07 now available

A new download of SLiMSuite (release 2015-01-07 ) is now available at both UK (U. Southampton) and a new Australia (UNSW) mirror. See the SLiMSuite Blog for more info.

Computational Prediction of Short Linear Motifs from Protein Sequences

Edwards RJ & Palopoli N (2015): Computational Prediction of Short Linear Motifs from Protein Sequences. Methods Mol Biol. 1268:89-141.

Abstract

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.

PMID: 25555723

Update: Full text (PDF) available here.

Emily Olorin (Student Research Assistant)

Emily Olorin joined the lab on 1st December 2014 on a summer vacation research scholarship. Her primary project is to update the SLiMScape App for the latest release of Cytoscape. Emily previously worked as part of the Cysique research group at NeuRA and for Google as an Engineering Practicum Intern. Her research interests include short linear motifs (SLiM) in proteins, as well as MRI analyses.

Links:

• LinkedIn: https://www.linkedin.com/pub/emily-saunders-walmsley/84/b87/a36
• Github: https://github.com/RayneCatseye
• Homepage: http://insearchofspoons.com/

#ABiC14 Poster 46: Computational prediction of protein interaction motifs from integrated protein sequence, structure and interaction data

Nicolas Palopoli & Richard J Edwards

Abstract

Protein-protein interactions (PPI) between globular domains and Short Linear Motifs (SLiMs) play a crucial part in many biological processes. SLiMs are short stretches of 5 to 15 amino acids with high evolutionary plasticity that are usually found in disordered regions of proteins. Their role as ligands for molecular signalling, post-translational modifications and subcellular targeting has been increasingly studied over recent years, but experimental discovery of SLiMs remains a challenging task due to their small size and high degeneracy. As a consequence, computational tools for prediction and analysis of SLiMs are a valuable resource.

We have previously developed SLiMFinder, a motif discovery tool that applies a model of convergent evolution to estimate the statistical significance of over-represented motifs with high specificity. Here, we aim to improve motif discovery by integrating SLiMFinder with methods that predict new domain-motif interactions directly from structural features in high-resolution 3D data. To this end we have developed “Query” SLiMFinder (QSLiMFinder), which uses knowledge of the interaction interface to constrain the motif search space and thereby increase search sensitivity. We have benchmarked QSLiMFinder using the Eukaryotic Linear Motif (ELM) database and simulated data. As expected, specific domain-motif interaction data can increase the power of de novo SLiM prediction from a set of proteins with a common PPI partner.

We are now applying QSLiMFinder to large-scale analysis of public PPI and 3D structure data. Domain-motif interactions are predicted from structures in the protein data bank (PDB). QSLiMFinder then identifies patterns within the putative motif region that are over-represented in the other known PPI partners of the domain-containing protein. This will add crucial molecular details to the interactome.

#ABiC14 Poster 45: Molecular mimicry in viruses and cancer

Richard J Edwards, Ranjeeta Menon, Nicolas Palopoli & Jason WH Wong

Abstract

Molecular mimicry is a well-established example of convergent evolution by which viruses evolve protein motifs that interact with host molecular machinery to hijack cellular functions. These short linear motifs (SLiMs) have only a handful of critical positions and a single point mutation is often sufficient to create or destroy a motif occurrence. This may have direct effects, such as eliminating a key regulatory interaction, or more subtle indirect effects by altering/blocking neighbouring interactions. There is much overlap in the breakdown of regulation caused by viruses and cancers - including oncogenic viruses - and so it is likely that many cancers are exploiting similar molecular mimicry mechanisms of protein interaction motifs during tumour development and progression.

We are combining publicly available datasets of host-host and host-pathogen PPI with recently developed tools from the SLiMSuite package to (1) identify novel candidates for viral mimicry of known host SLiMs, and (2) predict entirely new SLiM classes. In each case, signals of convergent evolution are identified using statistical over-representation of motifs in unrelated proteins. We are also using SLiM analysis tools to identify putative gain- and loss-of-function mutations from public cancer mutation databases. SLiM predictions and mutations will be placed in context using the human protein-protein interaction network and cross-referenced to known viral molecular mimicry and proteins/pathways affected in cancer. Simulated mutation data will be used to test for possible enrichment of mutations that create or destroy SLiMs.

Keywords: molecular mimicry, cancer, viruses, protein-protein interactions, short linear motifs, SLiMs

New SLiMSuite release now available

SLiMSuite Short Linear Motif discovery and analysis: New SLiMSuite release now available: A new download of SLiMSuite (release 2014-06-22 ) is now available. As well as fixing the minor GOPHER output bug , a new Taxonomy proces...

SLiMSuite 2014-04-22 now available

SLiMSuite 2014-04-22 now available: A new download of SLiMSuite (release 2014-04-22) is now available. As well as fixing the gopher.py error, the download page and readme ha...

SLiMSuite & SeqSuite: New downloads and fixed webpages

SLiMSuite & SeqSuite: open-source bioinformatics in Python: New downloads and fixed webpages: New releases of SeqSuite and SLiMSuite are now available. The webpages have now hopefully been fixed too, including the broken Manual link...

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