CCAIM Seminar Series – Dr. David Ascher, University of Queensland

Topic: Unravelling the molecular mechanisms behind mutations and their link to phenotypes using graph-based signatures
For this event, Prof. Ascher is hosted by Prof. Andres Floto of the University of Cambridge, Co-Director of the Cambridge Centre for AI in Medicine (CCAIM).
• Structural bioinformatics tools (freely available): http://biosig.unimelb.edu.au/biosig/tools
• University of Queensland School of Chemistry and Molecular Biosciences: https://scmb.uq.edu.au/
• Baker Institute: https://baker.edu.au/research/staff/david-ascher
• CCAIM: https://ccaim.cam.ac.uk/
About the speaker

Professor David Ascher is a National Health and Medical Research Council (NHMRC) Investigator, Deputy Director of Biotechnology at the University of Queensland, and Head of Computational Biology and Clinical Informatics at the Baker Institute and Systems and Computational Biology at Bio21 Institute. He is an Associate Editor of Progress in Biophysics & Molecular Biology and Frontiers in Bioinformatics, and holds positions at FIOCRUZ (Brazil), and the Tuscany University Network (Italy).

Prof. Ascher's research focuses on leveraging computational and experimental approaches to guide rational development, optimisation and validation of the next generation of drugs, biologics and agrochemicals. He has pioneered the development of the most comprehensive computational platform (including 43 widely used programs) for assessing the molecular consequences of coding variants and modelling and optimising small molecule pharmacokinetics and biological activity (receiving more than 6 million hits yearly). These methods have been incorporated and implemented into industry pipelines and translated to guide diagnosis, management and treatment of a number of hereditary diseases, rare cancers and drug resistant infections across Europe, Africa, South America and Australia.
Details of presentation

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 (VHL Syndrome and Alkaptonuria), and in the pre-emptive detection of drug resistance mutations in tuberculosis (rifampicin and pyrazinamide resistance). It has also been applied as part of the HIT-TB and MM4TB programs to guide development 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.
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