Aran is a fourth year medical student at James Cook University in Townsville. He graduated from Trinity Grammar School in Sydney, but was born in Singapore and lived in London. During his spare time he enjoys running and playing chess online.
Aran’s project is predicting protein-protein interaction motifs in KLF transcription factors.
This got a bit buried in all the conference excitement but congratulations to our lab neighbours and journal club buddies for winning the Research Category at the inaugural #BABSFEST video competition:
Early bird deadline is 1st November 2015 so register now for $$$ savings.
The Australian Bioinformatics And Computational Biology Society (ABACBS) 2015 Conference is almost upon us and the lab has four posters. Come visit us if you are attending! If not, you can check them out on the Australian Bioinformatics And Computational Biology Society Conference F1000Research channel and tweet or email with questions:
FF5. The SMRT Way to Sequence a Yeast Genome. Åsa Pérez-Bercoff, Tonia L. Russell, Paul V. Attfield, Philip J.L. Bell & Richard J. Edwards. http://f1000research.com/posters/4-1022.
FF6. Evidence for viral causes of cancer in prostate cancer transcriptomes and genomes. Timothy G. Amos, James S. Lawson, Wendy K. Glenn, Noel J. Whitaker & Richard J. Edwards. http://f1000research.com/posters/4-1023.
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.
P27. Genetic Characterisation of the Evolution of a Novel Metabolic Function in Yeast. Åsa Pérez-Bercoff, Tonia L. Russell, Paul V. Attfield, Philip J.L. Bell & Richard J. Edwards. http://f1000research.com/posters/4-1025.
Åsa Pérez-Bercoff, Tonia L. Russell, Paul V. Attfield, Philip J.L. Bell & Richard J. Edwards. http://f1000research.com/posters/4-1025.
We have a unique opportunity to study how new biological pathways have evolved to produce a yeast with a novel metabolic activity. Thirty-seven Saccharomyces cerevisiae strains were grown as a mixed population, and adapted on a specific media for 1463 days, undergoing sexual mating every two months. As a pilot study, three of the ancestral strains from the starting population, including two diploids, were selected for new PacBio RSII long-read single molecule real time (SMRT) sequencing at the Ramaciotti Centre for Genomics. High quality complete genomes were assembled de novo using the hierarchical genome-assembly process (HGAP3) using only PacBio non-hybrid long-read SMRT sequencing data, and corrected using Quiver. In addition, we have shotgun metagenomic Illumina data from a population exhibiting early adaptation to growth on the selective media. We are developing methods to map these short-read data onto several high quality ancestral genomes in order to estimate the relative contribution of each ancestor’s genetic variation to the evolved population, and identify possible sites of recombination. In addition, metagenomic data is being mapped against the official S. cerevisiae reference strain S288c to conduct variant calling to identify single nucleotide polymorphisms (SNPs) that are not present in any of our ancestral or reference genomes. These will be compared to the publicly available “100 yeast genomes”, and partitioned into natural variation and candidates for novel mutations. By doing these comparisons we hope to elucidate how the population has evolved to acquire its novel characteristics.
Emily Olorin, Kevin T. O’Brien, Nicolas Palopoli, Åsa Pérez-Bercoff, Denis C. Shields & Richard J. Edwards. http://f1000research.com/posters/4-1024.
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.
Timothy G. Amos, James S. Lawson, Wendy K. Glenn, Noel J. Whitaker & Richard J. Edwards. http://f1000research.com/posters/4-1023.
Viruses are known to cause 10-15% of human cancers. Some ongoing viral infections such as HPV in cervical cancer can be easily detected in the cancer transcriptome. However, in other situations viruses contribute to cancer causation without sustained high expression levels. PCR has detected HPV in prostate cancers but RNA-Seq has not shown strong evidence of continuing infections. We analysed prostate cancer transcriptomes and genomes from The Cancer Genome Atlas to further investigate HPV’s role in prostate cancer causation. Previous studies have filtered out all reads that might not be viral and then set thresholds for real infections by comparing to positive controls. In contrast, we found all possible viral reads and then evaluated multiple streams of evidence that they were genuine. Sequences were evaluated on the uniqueness of alignments to human, viral and vector sequences. They were also evaluated for sequence complexity, sequence quality, alignment quality, relative alignment locations of paired-end reads and the presence of chimeric reads that indicate viral integration sites. By screening cancer transcriptomes and genomes against all viruses in the NCBI database (c. N = 5766), including non-human viruses, we are also able to compare the strength of evidence against known false positives. We discuss the results of 22 viral candidates for oncogenesis in 558 prostate cancer paired RNA-Seq and WGS datasets.
Åsa Pérez-Bercoff, Tonia L. Russell, Paul V. Attfield, Philip J.L. Bell & Richard J. Edwards. http://f1000research.com/posters/4-1022.
We have performed PacBio single molecule real time (SMRT) sequencing of three yeast whole genomes. A haploid reference yeast strain (S288C) and two novel diploid strains were sequenced as part of a larger functional genomics project. For each strain, 20kb SMRT Bell library preps were performed and sequenced on two SMRT Cells using the P6-C4 chemistry. Between 1.74 Gb and 2.55 Gb of usable sequence data was generated for each strain, with read lengths of up to 53.3 kb. Pure PacBio whole genome de novo assemblies were generated using the HGAP3 pipeline. We are using the S288C data to explore performance in comparison to the published genome as a reference. An initial assembly of S288C yielded over 99.9% genome coverage at 99.997% accuracy on only 26 unitigs, versus 17 reference chromosomes (16 nuclear chromosomes plus mitochondrion). Of these, 16 chromosomes were essentially returned as a single, complete unitig. We are now using the S288C data to optimise the assembly process and derive assembly settings for two novel strains. To this end, we have developed a new pipeline for the comparative assessment of high quality whole genomes against a reference. In this poster, we explore how good PacBio sequencing is for de novo genome sequencing in yeast with examples of both the power and limits of the technology.
Rich will be giving this week’s BABS School seminar (as part of a double header),
Rountree Room Level 3
D26 Biological Sciences Building, UNSW
Friday 4 Sept 3.00pm
We have performed PacBio single molecule real time (SMRT) sequencing of three yeast whole genomes. A haploid reference yeast strain (S288C) and two novel diploid strains were sequenced as part of a larger functional genomics project. For each strain, 2-2.6 Gb of usable sequence data was generated with read lengths of up to 53.3 kb. Pure PacBio whole genome de novo assemblies were generated using the HGAP3 pipeline. initial assembly of S288C yielded over 99.9% genome coverage at 99.997% accuracy with 15 of 17 reference chromosomes (16 nuclear chromosomes plus mitochondrion) essentially returned as a single, complete unitig. We are now using the S288C data to optimise the assembly process and derive assembly settings for the two novel strains. To this end, we have developed a new pipeline for the comparative assessment of high quality whole genomes against a reference. We are also exploring the trade-off between accuracy and sequencing depth of the PacBio “pre-assembly” and how this affects the final assembly.
[This work will also be presented at AGTA 2015, if you are interested and miss it.]
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.
ABACBS 2015, the national conference for bioinformatics, will be held in Sydney this year. Registration and abstract submissions are now open! Closing date for abstract submissions is the 14th of August.
If the low cost isn’t enough incentive, there will be prizes for best poster, fast forward poster presentation and oral presentation…
Date: 10-11 October 2015 (preceding AGTA 2015) Venue: Garvan Institute of Medical Research, Sydney Cost: $99 (or $124 including a BBQ Dinner).
More details (and registration links): www.abacbs.org/conference
Questions? Email us: conference@abacbs.org
Additional events in Sydney this year:
COMBINE student symposium Oct 9, registration now open: www.abacbs.org/combine BioInfoSummer Dec 7-11 at the University of Sydney: http://bis15.amsi.org.au/
Congratulations to Alex Watson-Lazowski, who recently passed his PhD viva with minor corrections.
Alex was based in the Taylor Lab at the University of Southampton, where he applied Next Generation Sequencing to understand acclimation and adaption of Plantago lanceolata to a changing environment - and learnt a lot about the challenges of using RNA-Seq data without an available genome sequence!
Alex will be moving to Australia in July to take up a postdoctoral position in the Hawkesbury Institute for the Environment at the University of Western Sydney.
Research interests in the Edwards lab stem from a fascination with 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.
Another area of research concerns the post-transcriptional regulation of protein expression. In collaboration with Dr Mark Coldwell (University of Southampton), we are asking the question: how does the ribosome choose where to start translating a protein? By combining bioinformatics screens with laboratory reporter assays, we are identifying proteins that are translated from non-canonical and/or multiple initiation codons. Possible roles of N-terminal variability in protein interactions and subcellular localisation are now under investigation.
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.
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.
The EdwardsLab has a new homepage:
http://www.slimsuite.unsw.edu.auThis blog will remain as the primary source of news and information but the new page gives a fresh landing page and some additional links to resources:
The old pages should now redirect but please point any out of date pages and/or link rot that you come across.
There are several research opportunities for students in the Edwards Lab with deadlines coming up:
Mid-session Honours entry. Deadline: 4pm Friday 1st of June. Please see the BABS website for more information.
BABS3301 Biomolecular Science Laboratory Project (Advanced) course. (See below.)
BABS Second-year student internships. Deadline: COB Friday 22/5/2015.
The lab has a number of projects available, of which three examples are listed below. I am happy to discuss other bioinformatics project options around the general theme of sequence analysis and/or protein-protein interactions. There are also some website/software engineering projects available.
Applying biological sequence analysis and molecular evolution to study the molecular basis of protein-protein interactions.
Suitable for students who have majored in Biochemistry, Molecular Biology, Microbiology or Genetics. Projects are 100% computational; would suit students with computer programming experience and an interest in molecular evolution, or vice versa.
Project 1: Molecular mimicry in host-pathogen interactions Many viruses hijack host cellular machinery through the molecular mimicry of host Short Linear Motifs (SLiMs). It is likely that pathogenic bacteria may employ similar strategies. This project will apply state-of-the-art SLiM prediction tools developed in our lab to published datasets of host-pathogen protein-protein interactions. This will help us understand how pathogens mess with their hosts – and how to stop them!
Project 2: Mining cancer genomics for disease mutations that disrupt protein function SLiMs tend to be involved in low affinity interactions and have a small number of amino acid residues that are required for function. These attributes make them potential sites of mutations that slightly disrupt cellular function, sometimes only in specific genetic backgrounds or environments. This project will combine methods for proteome-wide SLiM prediction with human genomics data and genetic variants associated with disease. This will focus on mutations in cancers, which affect many of the same pathways targeted by molecular mimicry in viruses.
Project 3: Yeast as a model for protein interaction dynamics In addition to giving us bread and beer, the yeast Saccharomyces cerevisiae is an awesome eukaryotic model organism. This project will compare proteinprotein interactions in humans and yeast to learn how both organisms exploit SLiMs and post-translational modifications to dynamically control the complex inner workings of their cells.
Students with a WAM of 75 or more who are enrolled in a Biochemistry, Genetics or Molecular Biology major in one of the BSc, BSc(Adv) or BMedSc programs should consider enrolling in the BABS3301 (Biomolecular Science Laboratory Project (Advanced) course. This course is designed to introduce you to research methodology, and to stimulate critical and lateral thinking in the context of problem solving. The course involves directed reading, laboratory work and use of internet resources. You will work on a research project under the supervision of a member of the academic staff. Enrolment in this course is by invitation and is based on academic performance and is restricted to Science and Advanced students enrolled in one of the BABS majors (i.e. Biotechnology, Genetics, Microbiology, Molecular Biology and Cellular Biology Major or Plans or the Biochemistry and Molecular Biology, Genetics or Microbiology and Immunology specialisations).
Dillon J, Franks CJ, Murray C, Edwards RJ, Calahorro F, Ishihara T, Katsura I, Holden-Dye L, O’Connor V (in press). Metabotropic glutamate receptors: modulators of context-dependent feeding behaviour in C. elegans. J Biol Chem. Apr 13. pii: jbc.M114.606608.
Glutamatergic neurotransmission is evolutionarily conserved across animal phyla. A major class of glutamate receptors are the metabotropic glutamate receptors (mGluRs). In C. elegans three mGluR genes mgl-1, mgl-2 and mgl-3 are organised into three sub-groups, similar to their mammalian counterparts. Cellular reporters identified expression of the mgls in the nervous system of C. elegans and overlapping expression in the pharyngeal microcircuit that controls pharyngeal muscle activity and feeding behaviour. The overlapping expression of mgls within this circuit allowed investigation of receptor signalling per se and in the context of receptor interactions within a neural network that regulates feeding. We utilized the pharmacological manipulation of neuronally regulated pumping of the pharyngeal muscle in wild type and mutants to investigate mgl function. This defined a net mgl-1 dependent inhibition of pharyngeal pumping which is modulated by mgl-3 excitation. Optogenetic activation of the pharyngeal glutamatergic inputs combined with electrophysiological recordings from the isolated pharyngeal preparations provided further evidence for a presynaptic mgl-1 dependent regulation of pharyngeal activity. Analysis of mgl-1, mgl-2 and mgl-3 mutant feeding behaviour in the intact organism after acute food removal identified a significant role for mgl-1 in the regulation of an adaptive feeding response. Our data describes the molecular and cellular organisation of mgl-1, mgl-2 and mgl-3. Pharmacological analysis identified that in these paradigms mgl-1 and mgl-3, but not mgl-2, can modulate the pharyngeal microcircuit. Behavioural analysis identified mgl-1 as a significant determinant of the glutamate-dependent modulation of feeding, further highlighting the significance of mGluRs in complex C. elegans behaviour.
PMID: 25869139
Åsa Pérez-Bercoff has a background in molecular biology and bioinformatics, having completed an MSc in Molecular Biology (2003) and an MSc in Engineering (2006) in Sweden. She completed a PhD in the research group of Associate Professor Aoife McLysaght at the Genetics department, Trinity College Dublin. Her PhD research was on the function and evolution of
human protein networks, using publicly available datasets for modelling and hypothesis testing, and included a brief collaborative visit to Associate Professor Gavin Conant at the University of Missouri.
Following her PhD, Åsa worked as a bioinformatician in the Cancer Proteomics Mass Spectrometry research group of Associate Professor Janne Lehtiö (Karolinska Institute) in Sweden before moving to Australia in 2012 to join the research research group of Associate Professor Gavin Huttley at the ANU, in collaboration with Professor Wieland Meyer, at the Westmead Millenium Institute and University of Sydney.
Åsa joined the Edwards Lab in 2015 and worked on viral motif mimicry for a while before switching to work on yeast genomics in collaboration with Microbiogen Pty Ltd.
[LinkedIn]
Palopoli N, Lythgow KT & Edwards RJ (2015). QSLiMFinder: improved short linear motif prediction using specific query protein data. Bioinformatics 31(14): 2284-2293. [PDF]
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
The UNSW deadline for Semester 2 PhD applications for International Students is 26 Feb 2015. To allow sufficient time for assessment and processing, any Edwards Lab applications must be received TODAY (23 Feb 2015) for Semester 2, 2015. (Supported applicants will still need to make a UNSW application for the deadline.) Any applications received after this date will be considered for Semester 1, 2016. Unfortunately, these things need to be planned and researched a long time in advance!
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.
Science News have picked up the recent Molecular Ecology paper, Fluorescent protein-mediated colour polymorphism in reef corals: Multi-copy genes extend the adaptation/acclimatization potential to variable light environments, including a lovely photo from corresponding author Prof Jörg Wiedenmann and Cecilia d’Angelo, who heads up the Coral Reef Laboratory at Southampton.
Choosing the right lab and project in which to do a PhD is one of the most important decisions in the life of a scientist. It is in the best interests of all concerned to make sure that there is a good fit. To this end, there is now a lab PhD application form for all interested applicants. (Click the link or image to download.)
The purpose of the form is two-fold:
The lab does not currently have any funding for students but I am happy to receive applications from funded students and students wishing to apply for UNSW scholarships or other schemes. Please check the UNSW key dates page for application deadlines etc. - students may want to delay their application and strengthen their experience/CV in the meantime.
Informal enquiries are welcome but generic “Dear Sir/Professor” emails will be ignored. Please read this blog post, “How (not) to apply for a PhD”, before applying.
Note: Applicants with insufficient bioinformatics experience will not be considered (see below). It is simply too much of a risk (for both student and supervisor) to take such a student on, as not everyone takes to purely computational work. You must also demonstrate good communication in English, which includes all email communication.
There are no specific projects on offer and PhD research topics will ultimately be a collaborative decision based on the skills and interests of the student as well as the current status of various research in the lab. Available projects range from algorithm and bioinformatics resource development to primarily data analysis projects. Examples include, but are not limited to:
There is no single perfect applicant profile: please provide a frank and honest appraisal of your interests, skills and future goals in your application.
Completed applications forms should be emailed with a CV and degree transcript to richard.edwards@unsw.edu.au. Please name each file with your family name, initials and document type, e.g.
PDFs are preferred but MS WordEdwardsRJ.Application.docxEdwardsRJ.CV.pdfEdwardsRJ.Transcript.pdf
*.docx files are also OK. Remember: attention to detail is very important in bioinformatics. Boxes in the application form may be resized but please keep answers succinct; you will be judged on the quality of your writing.
Note: Applicants will also need to submit a formal application through the UNSW Graduate Research School. This is not recommended until an agreement has been made to sponsor your application. Please also note that any agreement to sponsor your application is not agreement to take you on as a student. The final decision regarding supervision will not be made until after all of the applications have been received and processed by UNSW.
Applicants for undergraduate projects (Honours/SVRS) or Masters programs should use the same form but indicate the program that they are applying for.
All projects are 100% computational. To be considered as an international PhD applicant, you must have completed at least one 100% computational project as part of your undergrad or masters, or have equivalent computational experience (e.g. work placement as a programmer or data analyst). Taught courses are not sufficient at this level unless you can also provide some evidence of skill at scripting/programming, such as an extensive body of work at Rosalind. Unfortunately, the risks are simply too high otherwise.
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.
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.
Edwards RJ & Palopoli N (2015): Computational Prediction of Short Linear Motifs from Protein Sequences. Methods Mol Biol. 1268:89-141.
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.