Professor David Ascher

NHMRC Emerging Leadership Fellow

School of Chemistry and Molecular Biosciences
Faculty of Science
d.ascher@uq.edu.au
+61 7 336 53991

Overview

Prof David Ascher is currently an NHMRC Investigator and Director of the Biotechnology Program at the University of Queensland. He is also Head of Computational Biology and Clinical Informatics 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 over 40 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.

David has a B.Biotech from the University of Adelaide, majoring in Biochemistry, Biotechnology and Pharmacology and Toxicology; and a B.Sci(Hon) from the University of Queensland, majoring in Biochemistry, where he worked with Luke Guddat and Ron Duggleby on the structural and functional characterization of enzymes in the branched-chain amino acid biosynthetic pathway. David then went to St Vincent’s Institute of Medical Research to undertake a PhD at the University of Melbourne in Biochemistry. There he worked under the supervision of Michael Parker using computational, biochemical and structural tools to develop small molecules drugs to improve memory.

In 2013 David went to the University of Cambridge to work with Sir Tom Blundell on using fragment based drug development techniques to target protein-protein interactions; and subsequently on the structural characterisation of proteins involved in non-homologous DNA repair. He returned to Cambridge in 2014 to establish a research platform to characterise the molecular effects of mutations on protein structure and function- using this information to gain insight into the link between genetic changes and phenotypes. He was subsequently recruited as a lab head in the Department of Biochemistry and Molecular Biology at the University of Melbourne in 2016, before joining the Baker Institute in 2019 and the University of Queensland in 2021.

He is an Associate Editor of PBMB and Fronteirs in Bioinformatics, and holds honorary positions at Bio21 Institute, Cambridge University, FIOCRUZ, and the Tuscany University Network.

Research Impacts

We have successfully translated our computational tools into the clinic and industry, including:

  • Clinical detection of drug resistance from whole-genome sequencing of pathogens, including Tuburculosis and Leprosy
  • Genetic counselling for rare diseases and cancers with Addenbrooke's Hospital and Brazilian Ministry of Health
  • Patient stratification within clinical trials
  • Implementation within industry drug and biologics development programs

The tools we have developed have also been widely adopted within existing academic programs including:

  • Integration of intermolecular interaction calculations using our tool Arpeggio in the PDBe, the European resource for the collection, organisation and dissemination of data on biological macromolecular structures.
  • Integration of our missense tolerance scores within the widely used VEP tool for variant characterisation.
  • Implementation of our resistance prediction tools within the London School of Hygiene & Tropical Medicine's TB-Profiler tool.

Publications

View all Publications

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Available Projects

  • Even though patients may present with the same disease, underlying genetic differences may alter a patient’s outcome or how they respond to a particular treatment. We have been developing approaches to analyse these genetic differences in order to predict and understand their molecular consequences and biological perturbations. In this way, we can have mechanistic insights underlying diseases and phenotypes, evaluate gene function in the context of their molecular interactions, and identify molecular relationships among apparently distinct phenotypes. These tools have also enabled the interpretation of heterogeneity among biological and clinical samples, identification of drug targets and drug repurposing as well as biomarker discovery. As our ability to profile biological samples increases, these approaches can be used to inform treatment strategies and personalised medicine.

  • Studying the architecture, shape, and dynamics of biological macromolecules is paramount to understanding the basic mechanisms that drive the essential processes of all life. Structural biology is concerned with the driving forces and interactions that determine the three-dimensional shapes and dynamics of biomolecules. Moreover, by applying the fundamental principles of the physical sciences, we are beginning to establish sequence-structure-dynamics-function relationships that enable deeper levels of discoveries, and summon the possibility of de novo structural and functional predictions at the proteome level.

  • A significant proportion of drug candidates fail clinical trials due to issues with efficacy and safety. We are using computational approaches to guide development of better drugs, including (a) improving binding affinity; (b) improving pharmacokinetic profiles; and (c) reducing toxicity, all early in development reducing overall cost and time to market. We are also developing tools to pre-emptively predict and identify likely resistance mutations, and using this insight to guide the development of ‘resistance-resistant’ treatments, contributing to maintaining the longevity of developed treatments.

View all Available Projects

Publications

Book Chapter

  • Serghini, Adam, Portelli, Stephanie and Ascher, David B. (2024). AI-driven enhancements in drug screening and optimization. Computational drug discovery and design. (pp. 269-294) edited by Mohini Gore and Umesh B. Jagtap. New York, NY, United States: Humana. doi: 10.1007/978-1-0716-3441-7_15

  • Ascher, David B., Kaminskas, Lisa M., Myung, Yoochan and Pires, Douglas E. V. (2022). Using graph-based signatures to guide rational antibody engineering. Computer-aided antibody design. (pp. 375-397) New York, NY, United States: Humana Press. doi: 10.1007/978-1-0716-2609-2_21

  • Airey, Edward, Portelli, Stephanie, Xavier, Joicymara S, Myung, Yoo Chan, Silk, Michael, Karmakar, Malancha, Velloso, João P L, Rodrigues, Carlos H M, Parate, Hardik H, Garg, Anjali, Al-Jarf, Raghad, Barr, Lucy, Geraldo, Juliana A, Rezende, Pâmela M, Pires, Douglas E V and Ascher, David B (2021). Identifying genotype-phenotype correlations via integrative mutation analysis. Artificial neural networks. (pp. 1-32) edited by Hugh Cartwright. New York, NY, United States: Humana. doi: 10.1007/978-1-0716-0826-5_1

  • Pires, Douglas E. V., Portelli, Stephanie, Rezende, Pâmela M., Veloso, Wandré N. P., Xavier, Joicymara S., Karmakar, Malancha, Myung, Yoochan, Linhares, João P. V., Rodrigues, Carlos H. M., Silk, Michael and Ascher, David B. (2020). A comprehensive computational platform to guide drug development using graph-based signature methods. Structural bioinformatics: methods and protocols. (pp. 91-106) New York, NY, United States: Humana. doi: 10.1007/978-1-0716-0270-6_7

  • Pires, Douglas E. V., Rodrigues, Carlos H. M., Albanaz, Amanda T. S., Karmakar, Malancha, Myung, Yoochan, Xavier, Joicymara, Michanetzi, Eleni-Maria, Portelli, Stephanie and Ascher, David B. (2019). Exploring protein supersecondary structure through changes in protein folding, stability, and flexibility. Protein Supersecondary Structures: Methods and Protocols. (pp. 173-185) edited by Alexander E. Kister. New York, NY, United States: Springer. doi: 10.1007/978-1-4939-9161-7_9

  • Pires, Douglas E. V., Kaminskas, Lisa M. and Ascher, David B. (2018). Prediction and optimization of pharmacokinetic and toxicity properties of the ligand. Computational Drug Discovery and Design. (pp. 271-284) New York, NY United States: Humana Press. doi: 10.1007/978-1-4939-7756-7_14

  • Ascher, David B., Jubb, Harry C., Pires, Douglas E. V., Ochi, Takashi, Higueruelo, Alicia and Blundell, Tom L. (2015). Protein-protein interactions: structures and druggability. Multifaceted roles of crystallography in modern drug discovery. (pp. 141-163) Dordrecht, Netherlands: Springer Netherlands. doi: 10.1007/978-94-017-9719-1_12

Journal Article

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Even though patients may present with the same disease, underlying genetic differences may alter a patient’s outcome or how they respond to a particular treatment. We have been developing approaches to analyse these genetic differences in order to predict and understand their molecular consequences and biological perturbations. In this way, we can have mechanistic insights underlying diseases and phenotypes, evaluate gene function in the context of their molecular interactions, and identify molecular relationships among apparently distinct phenotypes. These tools have also enabled the interpretation of heterogeneity among biological and clinical samples, identification of drug targets and drug repurposing as well as biomarker discovery. As our ability to profile biological samples increases, these approaches can be used to inform treatment strategies and personalised medicine.

  • Studying the architecture, shape, and dynamics of biological macromolecules is paramount to understanding the basic mechanisms that drive the essential processes of all life. Structural biology is concerned with the driving forces and interactions that determine the three-dimensional shapes and dynamics of biomolecules. Moreover, by applying the fundamental principles of the physical sciences, we are beginning to establish sequence-structure-dynamics-function relationships that enable deeper levels of discoveries, and summon the possibility of de novo structural and functional predictions at the proteome level.

  • A significant proportion of drug candidates fail clinical trials due to issues with efficacy and safety. We are using computational approaches to guide development of better drugs, including (a) improving binding affinity; (b) improving pharmacokinetic profiles; and (c) reducing toxicity, all early in development reducing overall cost and time to market. We are also developing tools to pre-emptively predict and identify likely resistance mutations, and using this insight to guide the development of ‘resistance-resistant’ treatments, contributing to maintaining the longevity of developed treatments.

  • We use the power of evolution and insights from protein structure to design new proteins and therapeutic biologics. From the earliest proteins to modern synthetic biology and chemical biology, understanding evolution at the molecular level is fundamental to engineering biology. What we understand, we can make: Knowledge derived from the reconstruction of past evolutionary events enables us to engineer new proteins with tailor-made properties, for various applications including. nerve agent detoxification and enzyme replacement therapies.