Lead Chief Investigator

Jill E Gready Computational Proteomics, JCSMR, Australian National University

Project Title

Comparative Genomics for Gene Discovery, Deciphering Gene Regulation and Protein Evolution

Brief Description for General Publications

The recent availability of whole-genome data for model organisms, particularly the vertebrate branch (fish, frog, birds, marsupials, mammals), plant branch (flowering plants, algae and others) and microbes (bacteria, protozoa) and viruses, as well as many gene sequences for individual species within these major classes provides a rich information source for mining by bioinformatics methods. This project aims to mine this information as an integral part of other projects involving both computational and experimental investigations. One project focuses on in silico comparative genomics approaches to study the evolution of genes and gene families and the regulation of their protein expression, for discovery of genes with as-yet unrecognized mammalian homologues, and for understanding the co-evolution of the structure and function of their proteins. We are using sequence-search methods, including BLAST and hidden Markov model (HMM) profile-based methods, to identify and annotate members of particular gene families of interest to us, phylogenetic footprinting to identify non-coding regions which bind transcription-factor proteins, which are involved in regulation of gene transcription, and protein sequence and structure analysis to follow changes during evolution which are reflected in changing functions. Our major interests are in the prion-protein family and C-type-lectin-domain-containing superfamily. A second project is aimed at understanding the functional evolution of the photosynthetic enzyme, Rubisco, by combined phylogenetic and structural analysis using the large number of sequences and structures available in public databases. The third project is aimed at predicting peptide antigens of influenza A viruses, including avian and swine flu, for use in development of new vaccines and diagnostics. This uses whole-genome sequences for a large number of influenza A virus variants ("immuno-informatics") and structural bioinformatics and molecular modelling for preliminary testing of binding of predicted peptides to their protein targets.