Research snapshot - January 2020

One of the most important, interesting and challenging questions in biology is how new traits evolve at the molecular level. My lab employs sequence analysis techniques to interrogate DNA and protein sequences for the signals left behind by evolution. We are a bioinformatics lab but like to incorporate bench/field data through collaboration wherever possible.

Main Research

Building on a solid foundation of bioinformatics and evolutionary theory, we apply genomics, transcriptomics, proteomics and interactomics and systems analysis to understand complex biological systems. Core research activities can be broadly divided into two main themes:

1. Evolutionary Genomics, with a focus on applying de novo genome assembly and population genomics to problems in ecology and biotechnology.

2. Protein-protein interactions, with a focus on the prediction of short linear interaction motifs and their role in human health and disease, including host-pathogen interactions.

These are explored in more detail, below.

1. Evolutionary Genomics.

The main research focus of the lab is the exploitation of genomic and post-genomic data to understand biological function and adaptation to novel environments. We work closely with the Ramaciotti Centre for Genomics and are involved in numerous de novo whole genome sequencing and assembly projects, using short read (Illumina), long read (PacBio & Nanopore) and linked read (10x Chromium) sequencing. One of the biggest of these is leading the bioinformatics and assembly effort in a consortium to sequence the cane toad genome, and leading the BABS Genome project to sequence two iconic Australian snakes. We are a member of the Oz Mammals Genomics initiative, assisting with the sequencing and assembly of Australia’s unique marsupial fauna. In 2018, we were selected as part of a team to sequence the Waratah genome as part of the pilot phase for the new Genomics of Australian Plants initiative.

We enjoy bringing our bioinformatics to bear on a variety of collaborative research projects. Most notably, we have an ARC Linkage Grant with Microbiogen Pty Ltd to understand how a strain of Saccharomyces cerevisiae has evolved to efficiently use xylose as a sole carbon source: something vital for second generation biofuel production that wild yeast cannot do. We are combining comparative genomics, evolutionary genetics, RNA-Seq transcriptomics, and competition assays to understand how the novel metabolism evolved. Through deep Illumina resequencing of evolving populations, and assembling reliable complete genomes of the founding ancestors, the ultimate goal is to trace how mutations have interacted with existing genetic variation during adaptive evolution. More recently, we have received an ARC Linkage Grant with the Royal Botanic Gardens and Domain Trust, to apply genomics approaches to the challenges of rainforest tree conservation in the face of climate change and invasive pathogens. We are also 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.

2. Short Linear Motifs (SLiMs).

Many protein-protein interactions are mediated by Short Linear Motifs (SLiMs): short stretches of proteins (5-15 amino acids long), of which only a few positions are critical to function. These motifs are vital for biological processes of fundamental importance, acting as ligands for molecular signalling, post-translational modifications and subcellular targeting. SLiMs have extremely compact protein interaction interfaces, generally encoded by less than 4 major affinity-/specificity-determining residues. Their small size enables high functional density and evolutionary plasticity, making them frequent products of convergent “ex nihilo” evolution. It also makes them challenging to identify, both experimentally and computationally.

A major focus of the lab is the computational prediction of SLiMs from protein sequences. This research originated with Rich’s postdoctoral research, during which he developed a sequence analysis methods for the 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. This subsequently lead to the development of SLiMFinder, the first SLiM prediction algorithm able to estimate the statistical significance of motif predictions. SLiMFinder 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. Methods are made available through the SLiMSuite bioinformatics package and webservers.

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.

OTHER RESEARCH PROJECTS

In addition to the main research in the lab, 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. Please see the Publications and Lab software pages for more detail, or get in touch if something catches your eye and you want to find out more. We frequently have small collaborations and/or undergraduate student research projects. Many of these are “on hold” waiting for the right person, or sometimes data, to come along. If you think that you have what it needs, get in touch!

Research snapshot - July 2019

One of the most important, interesting and challenging questions in biology is how new traits evolve at the molecular level. My lab employs sequence analysis techniques to interrogate protein and DNA sequences for the signals left behind by evolution. We are a bioinformatics lab but like to incorporate bench data through collaboration wherever possible.

Main Research

The core research in the lab is broadly divided into two main themes:

1. Evolutionary Genomics.

Since moving to UNSW, a major focus of the lab has been the exploitation of genomic and post-genomic data to understand biological function and adaptation to novel environments. We work closely with the Ramaciotti Centre for Genomics and are involved in numerous de novo whole genome sequencing and assembly projects, using short read (Illumina), long read (PacBio & Nanopore) and linked read (10x Chromium) sequencing. The biggest of these is leading the bioinformatics and assembly effort in a consortium to sequence the cane toad genome, and leading the BABS Genome project to sequence two iconic Australian snakes. We are a member of the Oz Mammals Genomics initiative, assisting with the sequencing and assembly of Australia’s unique marsupial fauna. In 2018, we were selected as part of a team to sequence the Waratah genome as part of the pilot phase for the new Genomics of Australian Plants initiative.

We enjoy bringing our bioinformatics to bear on a variety of collaborative research projects. Most notably, we have an ARC Linkage Grant with Microbiogen Pty Ltd to understand how a strain of Saccharomyces cerevisiae has evolved to efficiently use xylose as a sole carbon source: something vital for second-generation biofuel production that wild yeast cannot do. We are combining comparative genomics, evolutionary genetics, RNA-Seq transcriptomics, and competition assays to understand how the novel metabolism evolved. Through deep Illumina resequencing of evolving populations, and assembling reliable complete genomes of the founding ancestors, the ultimate goal is to trace how mutations have interacted with existing genetic variation during adaptive evolution. More recently, we have received an ARC Linkage Grant with the Royal Botanic Gardens and Domain Trust, to apply genomics approaches to the challenges of rainforest tree conservation in the face of climate change and invasive pathogens. We are also 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.

2. Short Linear Motifs (SLiMs).

Many protein-protein interactions are mediated by Short Linear Motifs (SLiMs): short stretches of proteins (5-15 amino acids long), of which only a few positions are critical to function. These motifs are vital for biological processes of fundamental importance, acting as ligands for molecular signalling, post-translational modifications and subcellular targeting. SLiMs have extremely compact protein interaction interfaces, generally encoded by less than 4 major affinity-/specificity-determining residues. Their small size enables high functional density and evolutionary plasticity, making them frequent products of convergent “ex nihilo” evolution. It also makes them challenging to identify, both experimentally and computationally.

A major focus of the lab is the computational prediction of SLiMs from protein sequences. This research originated with Rich’s postdoctoral research, during which he developed a sequence analysis methods for the 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. This subsequently lead to the development of SLiMFinder, the first SLiM prediction algorithm able to estimate the statistical significance of motif predictions. SLiMFinder 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. Methods are made available through the SLiMSuite bioinformatics package and webservers.

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.

OTHER RESEARCH PROJECTS

In addition to the main research in the lab, 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. Please see the Publications and Lab software pages for more detail, or get in touch if something catches your eye and you want to find out more. We frequently have small collaborations and/or undergraduate student research projects. Many of these are “on hold” waiting for the right person, or sometimes data, to come along. If you think that you have what it needs, get in touch!

Research snapshot - October 2018

One of the most important, interesting and challenging questions in biology is how new traits evolve at the molecular level. My lab employs sequence analysis techniques to interrogate protein and DNA sequences for the signals left behind by evolution. We are a bioinformatics lab but like to incorporate bench data through collaboration wherever possible.

Main Research

The core research in the lab is broadly divided into three main themes:

1. Short Linear Motifs (SLiMs)

Many protein-protein interactions are mediated by Short Linear Motifs (SLiMs): short stretches of proteins (5-15 amino acids long), of which only a few positions are critical to function. These motifs are vital for biological processes of fundamental importance, acting as ligands for molecular signalling, post-translational modifications and subcellular targeting. SLiMs have extremely compact protein interaction interfaces, generally encoded by less than 4 major affinity-/specificity-determining residues. Their small size enables high functional density and evolutionary plasticity, making them frequent products of convergent "ex nihilo" evolution. It also makes them challenging to identify, both experimentally and computationally.

A major focus of the lab is the computational prediction of SLiMs from protein sequences. This research originated with Rich’s postdoctoral research, during which he developed a sequence analysis methods for the 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. This subsequently lead to the development of SLiMFinder, the first SLiM prediction algorithm able to estimate the statistical significance of motif predictions. SLiMFinder 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.

2. The evolution of novel functions.

Previous work in the lab has focused on the evolution of functional specificity following gene duplication. Since moving to UNSW, activities have shifted more towards the use of PacBio long read sequencing and other cutting-edge sequencing technologies, working closely with the Ramaciotti Centre for Genomics. 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. Most notably, we have an ARC Linkage Grant with Microbiogen Pty Ltd. to understand how a strain of Saccharomyces cerevisiae has evolved to efficiently use xylose as a sole carbon source: something vital for second generation biofuel production that wild yeast cannot do. We are combining comparative genomics, evolutionary genetics, RNA-Seq transcriptomics, and competition assays to understand how the novel metabolism evolved. Through deep Illumina resequencing of evolving populations, and assembling reliable complete genomes of the founding ancestors, the ultimate goal is to trace how mutations have interacted with existing genetic variation during adaptive evolution.

3. Whole genome sequencing and assembly.

Following our experiences with de novo whole genome assembly in yeast, the lab is getting involved in an increasing number of genome sequencing projects. The biggest of these is leading the bioinformatics and assembly effort in a consortium to sequence the cane toad genome. The lab is also leading the BABS Genome project two iconic Australian snakes for use in teaching and public engagement. We are a member of the Oz Mammals Genomics initiative, assisting with the sequencing and assembly of Australia's unique marsupial fauna. We also have an number of bacterial long-read whole genome sequencing collaborations.

Other Research Projects

In addition to the main research in the lab, 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. We frequently have small collaborations and/or undergraduate student research projects. Many of these are “on hold” waiting for the right person, or sometimes data, to come along. If you think that you have what it needs, get in touch!

Previous Research

The lab has been involved in 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 involved the development of bespoke bioinformatics pipelines and a number of open source bioinformatics tools have been generated as a result. Please see the Publications and Lab software pages for more detail, or get in touch if something catches your eye and you want to find out more.

High risk human papilloma viruses (HPVs) are present in benign prostate tissues before development of HPV associated prostate cancer

Glenn WK, Ngan CC, Amos TG, Edwards RJ, Swift J, Lutze-Mann L, Shang F, Whitaker NJ & Lawson JS (2017): High risk human papilloma viruses (HPVs) are present in benign prostate tissues before development of HPV associated prostate cancer. Infectious Agents and Cancer 12:46.

Abstract

Background. Although high risk HPVs are associated with an increased risk of prostate cancer it is not known if they have a causal role. The purpose of this study is to investigate the potential role of human papilloma viruses (HPVs) in prostate cancer. The aims are (i) to investigate the presence and confirm the identity of high risk HPVs in benign prostate tissues prior to the development of HPV positive prostate cancer in the same patients, and (ii) to determine if HPVs are biologically active.

Methods. We used polymerase chain reaction (PCR) to identify HPVs in specimens from 52 Australian men with benign prostate biopsies who 1 to 10 years later developed prostate cancer. Immunohistochemistry (IHC) was used to assess the expression of HPV E7 oncoproteins, cytokeratin and prostate specific antigen (PSA).

We used RNASeq data from The Cancer Genome Atlas (TCGA) to identify possible HPV RNA sequences in prostate cancer.

Results. HPV screening using standard PCR was conducted on 28 of the 52 sets of benign and later prostate cancers. HPV L1 genes were identified in 13 (46%) benign and 8 (29%) of 28 later prostate cancers in the same patients. HPV E7 genes were identified in 23 (82%) benign and 19 (68%) of 28 subsequent prostate cancers in the same patients. The same HPV types were present in both the benign and subsequent prostate cancers in 9 sets of specimens. HPV type 16 was identified in 15% of benign and 3% of prostate cancers. HPV type 18 was identified in 26% of benign and 16% of prostate cancers. Small numbers of HPV types 45, 47, 76 and 115 were also identified.

High confidence RNA-Seq evidence for high risk HPV types 16 and 18 was identified in 12 (2%) of the 502 TCGA prostate cancer transcriptomes.

High risk HPV E7 oncoprotein was positively expressed in 23 (82%) of 28 benign prostate specimens but only in 8 (29%) of 28 of the later prostate cancer specimens. This difference is statistically significant (p = 0.001). Prostate specific antigen (PSA) was more highly expressed in 26 (50%) of 52 prostate cancer specimens as compared to prior benign prostate specimens in the same patients.

Conclusions. High risk HPVs are present in benign prostate tissues prior to the development of HPV positive prostate cancer. There is a significantly higher expression of HPV E7 oncoproteins in benign prostate tissues as compared to late prostate cancer that subsequently developed in the same patients. This observation suggests that HPV oncogenic activity is an early phenomenon in a majority of prostate oncogenesis. TCGA RNA-Seq data suggests that HPV is biologically active in some prostate tumour samples.

Emeritus Professor Jim Lawson has this to say about the study:

“Human papilloma viruses are the cause of cervical cancer in women. These viruses are sexually transmitted. Infections by human papilloma viruses can be prevented by effective vaccines including the Australian developed Gardosil.

Scientists from the University of New South Wales, Sydney, Australia, have identified high risk human papilloma viruses in normal prostate tissues 2 to 12 years before the development of human papilloma virus positive prostate cancer. While the scientists do not claim this is evidence that human papilloma viruses are a direct cause of prostate cancer, they advise that it is prudent for both men and women to act with caution.

Human papilloma viruses are present in semen in over 15% of men and can be readily transmitted during sexual activities.

Vaccines such as the Australian developed Gardosil, are safe and effective in preventing infections by human papilloma viruses. The University of New South Wales scientists strongly encourage both young men and women to prevent human papilloma virus infections by vaccination at a young age.”

Research Snapshot - June 2017

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. We are a bioinformatics lab but like to incorporate bench data through collaboration wherever possible.

Main Research

The core research in the lab is broadly divided into three main themes:

1. Short Linear Motifs (SLiMs)

SLiMs are short regions of proteins that mediate interactions with other proteins. A major focus of the lab is the computational prediction of SLiMs from protein sequences. This research originated with Rich’s postdoctoral research, during which he developed a sequence analysis methods for the 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. This subsequently lead to the development of SLiMFinder, the first SLiM prediction algorithm able to estimate the statistical significance of motif predictions. SLiMFinder 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.

2. The evolution of novel functions.

Previous work in the lab has focused on the evolution of functional specificity following gene duplication. Since moving to UNSW, activities have shifted more towards the use of PacBio long read sequencing and other cutting-edge sequencing technologies, working closely with the Ramaciotti Centre for Genomics. 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. Most notably, we have an ARC Linkage Grant with Microbiogen Pty Ltd. to understand how a strain of Saccharomyces cerevisiae has evolved to efficiently use xylose as a sole carbon source: something vital for second generation biofuel production that wild yeast cannot do. This project combines detailed molecular characterisation of highly adapted yeast strains with “molecular palaeontology” to trace the evolutionary process and identify functionally significant loci under selection.

3. Whole genome sequencing and assembly.

Following our experiences with de novo whole genome assembly in yeast, the lab is getting involved in an increasing number of genome sequencing projects. The biggest of these is leading the bioinformatics and assembly effort in a consortium to sequence the cane toad genome. The lab is also leading the BABS Genome project to sequence iconic Australian species for use in teaching and public engagement.

Previous Research

The lab has been involved in 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 involved the development of bespoke bioinformatics pipelines and a number of open source bioinformatics tools have been generated as a result. Please see the Publications and Lab software pages for more detail, or get in touch if something catches your eye and you want to find out more.

Predicting Motif Mimicry in Viruses

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:

Predicting Motif Mimicry in Viruses

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.

Sobia Idrees (PhD student)

Sobia Idrees has a Virology and Bioinformatics background. She completed her MPhil in Biotechnology and BS in Bioinformatics from GC University, Faisalabad, Pakistan. During her MPhil, she worked on various Virology and Bioinformatics projects which helped her in learning Molecular biology as well as Computational biology techniques. She joined UNSW in October 2015 with a particular interest in learning new Bioinformatics approaches especially to understand host-pathogen interactions, joining the Edwards Lab in January 2016. Her PhD is focused on the bioinformatic prediction of molecular mimicry in human viruses.

SUMMARY OF ACADEMIC QUALIFICATIONS

1. MPhil. IN BIOTECHNOLOGY (Virology) — GC University, Faisalabad, Pakistan
2. BS (Hons. ) IN BIOINFORMATICS — GC University, Faisalabad, Pakistan

[LinkedIn|ResearchGate]

#ABACBS Poster 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.

Abstract

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.

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.

On the hunt for molecular mimicry in viral pathogens

Yesterday’s excellent Australian Pathogen Bioinformatics Symposium 2014 (#APBS2014) reminded me that this post was a bit overdue: originally posted at the UNSW Science Wavelength blog on October 30, 2014. Following APBS2014, we are also looking into how we can identify which other pathogens might be using a similar strategy.

---

Every living cell is packed full of tiny molecular machines. Many of these machines are made from proteins: assembled chains of amino acid building blocks, which twist, wrap and fold up into complex three-dimensional shapes. The way that these machines interact with each other, and other molecules in the cell, ultimately determines how a cell behaves, divides (or not) and dies.

Viruses hijack this cellular machinery and reprogram it to their own ends using their own set of proteins. Many viruses are tiny, with genomes that encode only a handful of proteins, and yet they need to take control of a multitude of host processes. The way that they achieve this is by mimicking small host interaction motifs called SLiMs (short linear motifs), which are the focus of study in my lab.

For example, some viruses use the host DNA replication machinery for their own replication and mimic a SLiM that interacts with an important cell cycle checkpoint protein called retinoblastoma. By mimicking the retinoblastoma-binding motif, the virus tricks the cell into entering the DNA replication phase of the cell cycle. Other SLiMs are mimicked to prevent host cells committing suicide through a process called apoptosis, or to hijack cellular transport machinery and get the viral proteins to the right place.

My lab is a bioinformatics group, using computational tools to analyse biological data. As their name suggests, SLiMs are very short regions of the protein chain that have a small number of specific amino acids in specific linear configuration that enables them to interact with particular proteins in the cell. Not all protein folds up into complex shapes; SLiMs are generally found in regions that remain in a flexible, linear “intrinsically disordered” state. This means that we can potentially identify SLiMs by careful examination of the protein “sequence”: the precise composition and order of amino acids from a pool of twenty common types, as encoded by the DNA. This is challenging because one stretch of protein looks much like any other when written as a string of letters. In reality, these letters represent chemical groups with distinct properties and so our tools also consider evolutionary and structural signals in the data.

My background is in genetics and I consider myself an evolutionary biologist at heart. The famous adage of Theodosius Dobzhansky, “Nothing in biology makes sense except in the light of evolution” is particularly true of sequence analysis. The evolutionary process leaves clues in the sequences and it is those clues that my lab seeks to exploit.

Many people are familiar with the concept of evolutionary conservation, in which functionally important traits (or sequences) are maintained through time by natural selection. Mutations of these sites are detrimental to reproductive success and do not have a long-term future in the population. By aligning and comparing proteins that share ancestry, it is possible to identify regions and sites under such constraint as those that have not changed. However, there is an important time component with such analyses; whilst some sites will be unchanged due to constraints, others may simply reflect shared ancestry with insufficient time to accumulate mutations. Conservation alone also says nothing about the function of the conserved site and there are many functional and structural constraints that would drown out any signal left by very small functional elements like SLiMs.

For SLiM discovery, we also harness a second evolutionary process: “convergent” evolution, in which the same trait independently arises on a different background. In the context of SLiMs, a specific configuration of amino acids is independently acquired by proteins that do not share identifiable common ancestry. Because SLiMs are so small and involve so few positions, a single amino acid substitution can create (or destroy) a SLiM in a protein, which is precisely how viruses are able to stumble across useful host motifs. If useful, such accidental discoveries are captured by natural selection.

We identify candidates for convergent evolution as sequence patterns that are over-represented in a set of otherwise unrelated proteins that interact with a common partner protein. To identify molecular mimicry, we make use of experimental data in which a viral protein has been shown to interact with a human target protein. We look for sequence patterns that are found in the viral protein and enriched in the human proteins that interact with the target.

We still have a long way to go but if we can work out how viruses are disrupting the cellular machinery of their hosts, we might be able to work out how to stop them.

#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

InCoB 2014: Computational prediction of molecular mimicry in host-pathogen protein-protein interactions

Ranjeeta Menon, Nicolas Palopoli & Richard J. Edwards. F1000Research 2014, 5:1027 (poster)

Abstract

Many viruses hijack important cellular machinery of their hosts through the convergent evolution of host short linear motifs (SLiMs). We are combining host-pathogen and host-host protein-protein interaction data to explore such “molecular mimicry” across a broad range of human viruses. Computational tools from the SLiMSuite package are being used to assess the potential for bioinformatic analysis of high-throughput experiments to identify novel cases of molecular mimicry.