Harnessing the power of AI in biotechnology, personalised medicine, drug development and beyond

Statistical Bioinformatics Seminar: Judith and David Coffey Speaker

Speaker: Prof David Ascher (University of Queensland)

Abstract: We have developed a comprehensive computational platform that uses graph-based signatures to represent the wild-type environment of a residue in order to predict the structural and functional effects of mutations. This platform has been used to explore the effects of genetic disease and drug resistance mutations on protein folding, stability, dynamics and interactions, and their links to mutational tolerance and phenotypes. Mutations leading to larger molecular consequences, tended to be rarer, and needed the presence of compensatory mutations balancing these fitness costs to become fixed in a population.

We have now successfully clinically translated methods that use insights on the 3D effects of mutations to guide patient risk management in genetic diseases, and in the pre-emptive detection of drug resistance mutations in tuberculosis (rifampicin and pyrazinamide resistance). It has also been applied as part of drug development pipelines to guide design of drugs less prone to resistance.

This work has highlighted that structural bioinformatics tools, when applied in a systematic, integrated way, can provide a powerful and scalable approach for predicting structural and functional consequences of mutations in order to reveal molecular mechanisms leading to clinical and experimental phenotypes. These computational tools are freely available (http://biosig.unimelb.edu.au/biosig/tools).

About the speaker: David Ascher is Deputy Associate Dean of Research and Director of Biotechnology Program at The University of Queensland, Deputy Director of the Australian Centre for Ecogenomics and Head of the Computational Biology and Clinical Informatics laboratory at the Baker Institute. David’s research focus is in modelling biological data to gain insight into fundamental biological processes. One of his primary research interests has been developing tools to unravel the link between genotype and phenotype, using computational and experimental approaches to understand the effects of mutations on protein structure and function. His group has developed a platform of 65 widely used programs for assessing the molecular consequences of coding variants. This platform has now been successfully translated into clinical use to guide the diagnosis, management and treatment of a number of hereditary diseases, rare cancers and drug resistant infections, and into industry as part of drug development pipelines to guide design of safer and more effective therapeutics.