Profile:
David Ascher is Director of the Biotechnology Program at The University of Queensland, Head of Computational Biology and Clinical Informatics at the Baker Heart and Diabetes Institute and an NHMRC Investigator. David’s research focus is in translational computational biology, in particular in unravelling the link between genotype and phenotype by using computational and experimental approaches to understand the effects of mutations on protein structure and function. His group has developed a platform of over 70 widely used programs for assessing the molecular consequences of coding variants (>7 million hits/year). Working with clinical collaborators in Australia, Brazil and UK, these methods have been translated into the clinic to guide the diagnosis, management and treatment of a number of hereditary diseases, rare cancers and drug resistant infections.
Talk title:
Predictive tools for personalised medicine
Abstract:
The vast majority of coding variants are rare, and assessment of the contribution of rare variants is hampered by low statistical power and limited functional data. Missense mutations can be particularly challenging due to their subtle changes to protein sequence. Elucidating the molecular mechanisms linking a mutation’s impact with phenotype is very often non-trivial, and functional interpretation of mutation data has consequently lagged behind generation of the data from modern high-throughput techniques. This is complicated by the multitude of effects a mutation may have on a proteins function.
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).