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.
All good things come to an end and so the Edwards lab bids a fond farewell to Dr Nico Palopoli (outside left) and Armella Zadoorian (inside right) who have recently left the lab. Nico returns to his homeland of Argentina today to take up a new position, whilst Armella’s internship came to an end last week and is getting ready for exams. Hopefully, Ranjeeta (outside right) won’t get too lonely without them.
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
Ranjeeta Menon, Nicolas Palopoli & Richard J. Edwards. F1000Research 2014, 5:1027 (poster)
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.
