Professor David Ascher
Professor

David Ascher

Email: 
Phone: 
+61 7 336 53991

Background

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.

Availability

Professor David Ascher is:
Available for supervision
Media expert

Fields of research

Applications in life sciences Artificial intelligence Biochemistry and cell biology Bioinformatic methods development Bioinformatics and computational biology Biological Sciences Biological network analysis Biomedical and Clinical Sciences Cardiovascular medicine and haematology Data engineering and data science Data management and data science Gene mapping Genetics Genomics Genomics and transcriptomics Industrial biotechnology Industrial molecular engineering of nucleic acids and proteins Information and Computing Sciences Medical biochemistry and metabolomics Microbial genetics Microbiology Sequence analysis Statistical and quantitative genetics Structural biology (incl. macromolecular modelling) Translational and applied bioinformatics

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.

Search Professor David Ascher’s works on UQ eSpace

183 works between 2008 and 2024
All (183) Journal Article (175) Book Chapter (7) Conference Publication (1)

1 - 20 of 183 works

2024

Journal Article

Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus

Soh, Cheng Hwee, de Sá, Alex G. C., Potter, Elizabeth, Halabi, Amera, Ascher, David B. and Marwick, Thomas H. (2024). Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus. Cardiovascular Diabetology, 23 (1) 91. doi: 10.1186/s12933-024-02141-1

Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus

2024

Journal Article

Insights into the structure of NLR family member X1: Paving the way for innovative drug discovery

Jewell, Shannon, Nguyen, Thanh Binh, Ascher, David B. and Robertson, Avril A.B. (2024). Insights into the structure of NLR family member X1: Paving the way for innovative drug discovery. Computational and Structural Biotechnology Journal, 23, 3506-3513. doi: 10.1016/j.csbj.2024.09.013

Insights into the structure of NLR family member X1: Paving the way for innovative drug discovery

2024

Journal Article

piscesCSM: prediction of anticancer synergistic drug combinations

AlJarf, Raghad, Rodrigues, Carlos H. M., Myung, Yoochan, Pires, Douglas E. V. and Ascher, David B. (2024). piscesCSM: prediction of anticancer synergistic drug combinations. Journal of Cheminformatics, 16 (1) 81. doi: 10.1186/s13321-024-00859-4

piscesCSM: prediction of anticancer synergistic drug combinations

2024

Journal Article

Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank

Huang, Katherine, G. C. de Sá, Alex, Thomas, Natalie, Phair, Robert D., Gooley, Paul R., Ascher, David B. and Armstrong, Christopher W. (2024). Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank. Communications Medicine, 4 (1) 248, 248. doi: 10.1038/s43856-024-00669-7

Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank

2024

Journal Article

Rifaximin prophylaxis causes resistance to the last-resort antibiotic daptomycin

Turner, Adrianna M., Li, Lucy, Monk, Ian R., Lee, Jean Y. H., Ingle, Danielle J., Portelli, Stephanie, Sherry, Norelle L., Isles, Nicole, Seemann, Torsten, Sharkey, Liam K., Walsh, Calum J., Reid, Gavin E., Nie, Shuai, Eijkelkamp, Bart A., Holmes, Natasha E., Collis, Brennan, Vogrin, Sara, Hiergeist, Andreas, Weber, Daniela, Gessner, Andre, Holler, Ernst, Ascher, David B., Duchene, Sebastian, Scott, Nichollas E., Stinear, Timothy P., Kwong, Jason C., Gorrie, Claire L., Howden, Benjamin P. and Carter, Glen P. (2024). Rifaximin prophylaxis causes resistance to the last-resort antibiotic daptomycin. Nature, 635 (8040), 969-977. doi: 10.1038/s41586-024-08095-4

Rifaximin prophylaxis causes resistance to the last-resort antibiotic daptomycin

2024

Journal Article

AlzDiscovery: A computational tool to identify Alzheimer's disease‐causing missense mutations using protein structure information

Pan, Qisheng, Parra, Georgina Becerra, Myung, Yoochan, Portelli, Stephanie, Nguyen, Thanh Binh and Ascher, David B. (2024). AlzDiscovery: A computational tool to identify Alzheimer's disease‐causing missense mutations using protein structure information. Protein Science, 33 (10) e5147, e5147. doi: 10.1002/pro.5147

AlzDiscovery: A computational tool to identify Alzheimer's disease‐causing missense mutations using protein structure information

2024

Journal Article

Definition and validation of prognostic phenotypes in moderate aortic stenosis

Sen, Jonathan, Wahi, Sudhir, Vollbon, William, Prior, Marcus, de Sá, Alex G.C., Ascher, David B., Huynh, Quan and Marwick, Thomas H. (2024). Definition and validation of prognostic phenotypes in moderate aortic stenosis. JACC: Cardiovascular Imaging. doi: 10.1016/j.jcmg.2024.06.013

Definition and validation of prognostic phenotypes in moderate aortic stenosis

2024

Journal Article

MTR3D‐AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2

Kovacs, Aaron S., Portelli, Stephanie, Silk, Michael, Rodrigues, Carlos H. M. and Ascher, David B. (2024). MTR3D‐AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2. Protein Science, 33 (8) e5112, e5112. doi: 10.1002/pro.5112

MTR3D‐AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2

2024

Journal Article

EFG‐CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

Gu, Xiaotong, Myung, Yoochan, Rodrigues, Carlos H. M. and Ascher, David B. (2024). EFG‐CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models. Protein Science, 33 (8) e5096, e5096. doi: 10.1002/pro.5096

EFG‐CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

2024

Conference Publication

Towards evolutionary-based automated machine learning for small molecule pharmacokinetic prediction

de Sá, Alex G. C. and Ascher, David B. (2024). Towards evolutionary-based automated machine learning for small molecule pharmacokinetic prediction. GECCO '24 Companion, Melbourne, VIC, Australia, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3638530.3664166

Towards evolutionary-based automated machine learning for small molecule pharmacokinetic prediction

2024

Journal Article

A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma

Craig, Olivia, Lee, Samuel, Pilcher, Courtney, Saoud, Rita, Abdirahman, Suad, Salazar, Carolina, Williams, Nathan, Ascher, David B, Vary, Robert, Luu, Jennii, Cowley, Karla J, Ramm, Susanne, Li, Mark Xiang, Thio, Niko, Li, Jason, Semple, Tim, Simpson, Kaylene J, Gorringe, Kylie L and Holien, Jessica K (2024). A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma. NAR Genomics and Bioinformatics, 6 (3) lqae096, lqae096. doi: 10.1093/nargab/lqae096

A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma

2024

Journal Article

PRIMITI: a computational approach for accurate prediction of miRNA-target mRNA interaction

Uthayopas, Korawich, de Sá, Alex G.C., Alavi, Azadeh, Pires, Douglas E.V. and Ascher, David B. (2024). PRIMITI: a computational approach for accurate prediction of miRNA-target mRNA interaction. Computational and Structural Biotechnology Journal, 23, 3030-3039. doi: 10.1016/j.csbj.2024.06.030

PRIMITI: a computational approach for accurate prediction of miRNA-target mRNA interaction

2024

Journal Article

DDMut-PPI: predicting effects of mutations on protein–protein interactions using graph-based deep learning

Zhou, Yunzhuo, Myung, YooChan, Rodrigues, Carlos H M and Ascher, David B (2024). DDMut-PPI: predicting effects of mutations on protein–protein interactions using graph-based deep learning. Nucleic Acids Research, 52 (W1), W207-W214. doi: 10.1093/nar/gkae412

DDMut-PPI: predicting effects of mutations on protein–protein interactions using graph-based deep learning

2024

Journal Article

Targeting the Plasmodium falciparum UCHL3 ubiquitin hydrolase using chemically constrained peptides

King, Harry R., Bycroft, Mark, Nguyen, Thanh-Binh, Kelly, Geoff, Vinogradov, Alexander A., Rowling, Pamela J E, Stott, Katherine, Ascher, David B., Suga, Hiroaki, Itzhaki, Laura S. and Artavanis-Tsakonas, Katerina (2024). Targeting the Plasmodium falciparum UCHL3 ubiquitin hydrolase using chemically constrained peptides. Proceedings of the National Academy of Sciences of the United States of America, 121 (21) e2322923121. doi: 10.1073/pnas.2322923121

Targeting the Plasmodium falciparum UCHL3 ubiquitin hydrolase using chemically constrained peptides

2024

Journal Article

Engineering G protein‐coupled receptors for stabilization

Velloso, João Paulo L., de Sá, Alex G. C., Pires, Douglas E. V. and Ascher, David B. (2024). Engineering G protein‐coupled receptors for stabilization. Protein Science, 33 (6) e5000, 1-13. doi: 10.1002/pro.5000

Engineering G protein‐coupled receptors for stabilization

2024

Journal Article

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

Ryu, Jayoung, Barkal, Sam, Yu, Tian, Jankowiak, Martin, Zhou, Yunzhuo, Francoeur, Matthew, Phan, Quang Vinh, Li, Zhijian, Tognon, Manuel, Brown, Lara, Love, Michael I., Bhat, Vineel, Lettre, Guillaume, Ascher, David B., Cassa, Christopher A., Sherwood, Richard I. and Pinello, Luca (2024). Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification. Nature Genetics, 56 (5), 925-937. doi: 10.1038/s41588-024-01726-6

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

2024

Journal Article

Mutations in glycosyltransferases and glycosidases: implications for associated diseases

Gu, Xiaotong, Kovacs, Aaron S., Myung, Yoochan and Ascher, David B. (2024). Mutations in glycosyltransferases and glycosidases: implications for associated diseases. Biomolecules, 14 (4) 497. doi: 10.3390/biom14040497

Mutations in glycosyltransferases and glycosidases: implications for associated diseases

2024

Journal Article

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

Myung, Yoochan, de Sá, Alex G C and Ascher, David B (2024). Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction. Nucleic Acids Research, 52 (W1), W469-W475. doi: 10.1093/nar/gkae254

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

2024

Journal Article

Are manufacturing patents to blame for biosimilar market launch delays?

Williamson, Rhys, Munro, Trent, Ascher, David, Robertson, Avril and Pregelj, Lisette (2024). Are manufacturing patents to blame for biosimilar market launch delays?. Value in Health, 27 (3), 287-293. doi: 10.1016/j.jval.2023.12.005

Are manufacturing patents to blame for biosimilar market launch delays?

2024

Journal Article

A metabolic signature for NADSYN1-dependent congenital NAD deficiency disorder

Szot, Justin O., Cuny, Hartmut, Martin, Ella M.M.A., Sheng, Delicia Z., Iyer, Kavitha, Portelli, Stephanie, Nguyen, Vivien, Gereis, Jessica M., Alankarage, Dimuthu, Chitayat, David, Chong, Karen, Wentzensen, Ingrid M., Vincent-Delormé, Catherine, Lermine, Alban, Burkitt-Wright, Emma, Ji, Weizhen, Jeffries, Lauren, Pais, Lynn S., Tan, Tiong Y., Pitt, James, Wise, Cheryl A., Wright, Helen, Andrews, Israel D., Pruniski, Brianna, Grebe, Theresa A., Corsten-Janssen, Nicole, Bouman, Katelijne, Poulton, Cathryn, Prakash, Supraja ... Dunwoodie, Sally L. (2024). A metabolic signature for NADSYN1-dependent congenital NAD deficiency disorder. Journal of Clinical Investigation, 134 (4) 174824. doi: 10.1172/jci174824

A metabolic signature for NADSYN1-dependent congenital NAD deficiency disorder

Current funding

  • 2023 - 2027
    Improving genetic diagnosis of autoimmune and autoinflammatory disease through an integrated multi-omics approach (MRFF 2022 GHFM - administered by ANU)
    The Australian National University
    Open grant

Past funding

  • 2024
    Development of Molecular Property Prediction Models for Exploring Alternative Chemicals
    Korea Research Institute of Chemical Technology
    Open grant

Availability

Professor David Ascher is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Supervision history

Current supervision

  • Doctor Philosophy

    Developing structure-based deep learning methods to predict mutation effects on proteins

    Principal Advisor

  • Doctor Philosophy

    Computer-aided drug design: predicting and mitigating drug toxicity

    Principal Advisor

    Other advisors: Dr Stephanie Portelli

  • Doctor Philosophy

    Exploring Cardiotoxicity Risk Factors

    Principal Advisor

    Other advisors: Dr Thanh-Binh Nguyen

  • Master Philosophy

    Explore the dark spots in PDB

    Principal Advisor

  • Doctor Philosophy

    Post-transcriptional gene regulation: towards a better understanding of pathogenesis and medical applications

    Principal Advisor

  • Doctor Philosophy

    Computational approaches to engineer and modulate G protein-coupled receptors

    Principal Advisor

  • Doctor Philosophy

    Personalising treatments for genetic diseases

    Principal Advisor

    Other advisors: Dr Stephanie Portelli

  • Doctor Philosophy

    Deep Learning Algorithms for Polygenic Genotype-Phenotype Predictions and the development of genetics computation tools

    Principal Advisor

  • Doctor Philosophy

    Towards the accurate functional characterisation of protein coding mutations

    Principal Advisor

    Other advisors: Dr Stephanie Portelli, Dr Thanh-Binh Nguyen

  • Doctor Philosophy

    Exploring Cardiotoxicity Risk Factors

    Principal Advisor

    Other advisors: Dr Thanh-Binh Nguyen

  • Doctor Philosophy

    Improving rational antibody design using machine learning

    Principal Advisor

  • Doctor Philosophy

    Machine Learning for Protein Dynamics: Predicting Post-Translational Modifications and Mutation Effects

    Principal Advisor

  • Doctor Philosophy

    Using Deep Learning in Cell & Gene Therapy

    Principal Advisor

    Other advisors: Dr Thanh-Binh Nguyen, Dr Stephanie Portelli

  • Doctor Philosophy

    Protein structure guided precision medicine

    Principal Advisor

    Other advisors: Professor Phil Hugenholtz, Dr Stephanie Portelli

  • Master Philosophy

    Explore the dark spots in PDB

    Principal Advisor

  • Doctor Philosophy

    Rational protein engineering and inhibition

    Principal Advisor

  • Doctor Philosophy

    Post-transcriptional gene regulation: towards a better understanding of pathogenesis and medical applications

    Principal Advisor

  • Doctor Philosophy

    Unravelling the Physicochemical Drivers of Biomolecular Self-Assembly though Multiscale Simulations

    Associate Advisor

    Other advisors: Dr Evelyne Deplazes, Professor Megan O'Mara

  • Doctor Philosophy

    Breaking the chain of inflammation through targetting NLR proteins

    Associate Advisor

    Other advisors: Professor Avril Robertson

  • Doctor Philosophy

    Therapeutic Resolution of Inflammation in the Central Nervous System for Neuroprotection in Parkinson's Disease

    Associate Advisor

    Other advisors: Professor Avril Robertson

  • Doctor Philosophy

    Use of structural phylogeny and reconciliation in molecular phylogenetics

    Associate Advisor

    Other advisors: Dr Kate Bowerman, Professor Phil Hugenholtz

  • Doctor Philosophy

    Therapeutic Resolution of Inflammation in the Central Nervous System for Neuroprotection in Parkinson's Disease

    Associate Advisor

    Other advisors: Professor Avril Robertson

  • Doctor Philosophy

    Computational design of targeted lipid technologies

    Associate Advisor

    Other advisors: Professor Megan O'Mara

Enquiries

Contact Professor David Ascher directly for media enquiries about their areas of expertise.

Need help?

For help with finding experts, story ideas and media enquiries, contact our Media team:

communications@uq.edu.au