Anissa Benkaza joined the lab as a volunteer during her final year as a genetics student in the School of BABS. She worked to help annotate snake venom proteins as part of the BABS Genome Project. Anissa returned in 2020 as an Honours student, co-supervised by Dr Emily Oates in BABS, looking at Titin protein-protein interactions and the prediction of muscle-disease-causing genes.
[LinkedIn]
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.
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
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/.
Sobia Idrees, Richard J Edwards
This work was presented at the Sydney Bioinformatics Research Symposium 2018. (Abstract below.) Click on thumbnail for full resolution PDF.
One of the main pursuits in proteomics is to understand the complex network of protein-protein Interactions (PPI) that underpin biological processes. Two major classes of PPI are domain-domain interactions (DDI) between globular proteins, and domain-motif interactions (DMI) between a globular domain and a short linear motif (SLiM) in its partner. Advances in high-throughput experimental techniques have been applied at large-scale in an attempt to characterise the interactome of various organisms. However, PPI networks being identified by these high-throughput experiments have low resolution as compared to low-throughput technologies, such as protein co-crystallization. Furthermore, large-scale approaches may be poor at capturing low affinity or transient interactions, which includes the majority of known DMI. To date, several studies have been conducted to identify how well these PPI data can capture protein complexes, but the ability of high-throughput PPI-detection methods to capture DMI remains a largely unanswered question.
To help system biologists choose appropriate methods for predicting different types of interactions, we conducted a comprehensive comparison study on existing high-throughput PPI datasets. We have integrated PPI data, SLiM predictions, domain compositions and known SLiM-domain binding partnerships to identify possible DMI and DDI within interactomes. We identify PPI data that are enriched for DMI or DDI versus a background expectation generated by randomising the PPI within the network. Despite returning relatively few experimentally validated DMI when compared to interaction databases, we present evidence that high-throughput PPI data is enriched for DMI and thus potentially useful for the prediction of novel SLiMs. We discuss the relative merits of co-fractionation followed by mass spectrometry (CoFrac-MS), affinity purification coupled mass spectrometry (AP-MS), and yeast two hybrid (Y2H) for capturing DMI and DDI, as well as potential quality versus quantity trade-offs in DMI prediction.
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:
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.
Sobia will be presenting the initial work from her PhD project today at the EMBO Workshop, The modularity of signalling proteins and networks at Seefeld in Tirol, Austria. Her talk is on:
Viruses mimic host motifs to hijack the host cellular machinery. Their interaction with host protein domains is through Short Linear Motifs (SLiMs). SLiMs are short stretches of amino acids (~3-10) which are involved in post translational modifications (PTMs), protein-protein Interactions (PPIs), cell regulation and cell compartment targeting. To date, several studies have been conducted to identify PPIs, but no specific study to see how well different PPI capturing methods capture SLiMs-mediated interactions. The main objectives of this study are 1) to predict Domain Motif Interactions (DMIs) among viral and host proteins 2) to find whether virhostome (virus-human interaction) data is enriched for DMIs and, 3) to see which PPI method is better for studying DMIs. Results have shown that virhostome data is enriched for DMIs and can be a good source to study motif mimicry in viruses. The permutation test showed more enrichment for TAP data as compared to the Y2H data. Moreover, novel candidate DMIs have been discovered which need further validations. The outcome of this study will be helpful in uncovering unique strategies of viruses to interact with human proteins which will eventually be significant for pathogen research.
Poster to follow.
The latest paper from the lab (featuring work from Emily Olirin’s summer project) is now out at F1000Research. We’ve not gone down the post-publication peer review route before, so it will be interesting to see how that goes, but it made sense in this case as it’s part of a special Cytoscape Apps channel. Rest assured that it has undergone technical review, just not scientific review. [At time of submission: peer review now complete.]
Olorin E, O’Brien KT, Palopoli N, Pérez-Bercoff A, Shields DC, Edwards RJ (2015): SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks [version 1; referees: 2 approved]. F1000Research 4:477. (doi: 10.12688/f1000research.6773.1)
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. SLiMScape and SLiMSuite are open source and freely available via GitHub under GNU licenses.
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.
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:
Nicolas Palopoli & Richard J Edwards
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.
Richard J Edwards, Ranjeeta Menon, Nicolas Palopoli & Jason WH Wong
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
Nico’s poster from the 3rd Protein-Protein Interaction Network (PPI-Net) Young Researchers Symposium is now available on F1000Posters:
Nicolas Palopoli & Richard J Edwards (2014) Computational prediction of short linear motifs integrating protein-protein interactions, sequence and structural data. F1000Posters 5: 407.
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 presented SLiMFinder, a motif discovery tool that applies a model of convergent evolution to estimate the statistical significance of over-represented motifs. In this project 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. Using putative SLiM-carrying regions extracted from protein structures as queries and targeting data from public protein-protein interaction databases, we applied QSLiMFinder to find over-represented recurring sequence patterns from proteins that all share a common interaction partner.
Benchmarking of QSLiMFinder shows that specific domain-motif interaction data help in finding novel instances of known motifs or entire de novo SLiMs, improving over results returned by PPI data alone. SLiM discovery capitalizes from the availability of experimentally identified PPIs with high-quality predictions of the interacting sites. The methods developed here help to enhance annotation in public databases of SLiMs and could be used to mine new PPI data as it becomes available, adding molecular detail to interactome networks.
As part of the move to UNSW, a 10 month computational postdoc position is available in the lab. The position is not attached to a specific grant and thus the research focus of the position is flexible and open for negotiation. It will, however, be something related to the lab’s primary research focus of computational Short Linear Motif (SLiM) discovery.
Possible projects include (but are not limited to): molecular mimicry by viral or bacterial pathogens; the role of SLiMs in cancer; interrogating protein-protein interaction networks to predict SLiM function; SLiM prediction database/visualisation development. For more on the research of the lab, please visit my old University of Southampton and/or new UNSW pages or email for more information.
Short-listing will (probably!) begin on 1/12/13 but applications are welcome until the position is filled. To apply, or find out more, please email a copy of your CV and your research interests. Candidate should have good computational skills. Start date is flexible but likely to be around January 2014.
Nico Palopoli started training in Computational Biology with his undergraduate thesis project on the structural modeling and characterization of starch-synthase III from Arabidopsis thaliana, under the guidance of Dr. Gustavo Parisi at the Structural Bioinformatics Group from Universidad Nacional de Quilmes (Argentina). He stayed at the group to fulfill his PhD thesis on the validation of protein 3D models using a structurally constrained protein evolution model.
While still a PhD student, Nico was awarded an Erasmus Mundus Sandwich PhD fellowship to spend an 8-month stay at Dr. Rita Casadio's Biocomputing Group from University of Bologna (Italy) where he started to develop structurally constrained, evolutionary-simulated Hidden Markov Models of protein families. His first Postdoctoral fellowship was awarded to take part in PhasIbeAm, the common bean genome sequencing project. Most of his work was conducted at the Protein Physiology Lab from Universidad de Buenos Aires (Argentina), where he also collaborated with Dr. Ignacio Sanchez on the application of information theory-based methods to study protein interactions by linear motifs.
Nico moved to the University of Southampton in October 2012 to work as a Research Fellow in the Edwards Lab on the project 'Integrated in silico prediction of protein-protein interaction motifs'. He left the lab in late 2014.
Dinkel H, Michael S, Weatheritt RJ, Davey NE, Van Roey K, Altenberg B, Toedt G, Uyar B, Seiler M, Budd A, Jödicke L, Dammert MA, Schroeter C, Hammer M, Schmidt T, Jehl P, McGuigan C, Dymecka M, Chica C, Luck K, Via A, Chatr-Aryamontri A, Haslam N, Grebnev G, Edwards RJ, Steinmetz MO, Meiselbach H, Diella F & Gibson TJ (2012): ELM—the database of eukaryotic linear motifs. Nucleic Acids Research 40(D1): D242-D251.
Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.
PMID: 22110040
Edwards RJ, Davey NE, O’Brien K & Shields DC (2012): Interactome-wide prediction of short, disordered protein interaction motifs in humans. Molecular Biosystems 8: 282-95.
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.
PMID: 21879107
Raab M, Daxecker H, Edwards RJ, Treumann A, Murphy D & Moran N (2010): Protein interactions with the platelet integrin alpha(IIb) regulatory motif. Proteomics 10: 2790-2800.
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.
PMID: 20486118
