Bioinformatics Seminar
Time: 11AM
Venue: Zoom Webinar
13 July 2021
Evolving a Deep and meaningful understanding of PARP1
Matthew WakefieldWEHI Bioinformatics
Poly ADP-ribose polymerase inhibitor (PARP) inhibitors are a highly effective therapy in widespread clinical use for breast and ovarian cancer. We are characterising all possible mutations in PARP1 for their impact on inhibitor resistance using a Deep Mutational Scan (DMS). This will provide clinical annotation for making patient care decisions and improve our fundamental understanding of PARP inhibitor function and resistance.
By testing multiple different PARP inhibitors we will determine the mutations that are specific to an individual drug, allowing substitution with a PARP inhibitor that overcomes the resistance.
To enhance the utility of this data I am also constructing and validating multiple types of computational models. Data from our Deep Mutational Scan (DMS) of PARP1 variants will be used to construct a computational molecular dynamics model of PARP1-HPF1 interaction. This model will be validated by making computational predictions of additional inhibitors prior to testing these inhibitors experimentally.
Evolutionary conservation is also being incorporated into the models using a deep learning approach that aims to create a 'neutral' model of protein constraint that can be combined with DMS data or applied independently to any sequence.