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Professor David Ascher
Professor

David Ascher

Email: 
Phone: 
+61 7 336 53991

Overview

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

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.

Works

Search Professor David Ascher’s works on UQ eSpace

177 works between 2008 and 2024

1 - 20 of 177 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

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

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

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

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

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. New York, NY, USA: ACM. doi: 10.1145/3638530.3664166

Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction

2024

Journal Article

<scp>EFG</scp>‐<scp>CS</scp>: 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

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

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. 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, e5000. 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), 1-13. 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, 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

2024

Journal Article

AI-driven GPCR analysis, engineering, and targeting

Velloso, João P.L., Kovacs, Aaron S., Pires, Douglas E.V. and Ascher, David B. (2024). AI-driven GPCR analysis, engineering, and targeting. Current Opinion in Pharmacology, 74 102427. doi: 10.1016/j.coph.2023.102427

AI-driven GPCR analysis, engineering, and targeting

2024

Journal Article

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

Rodrigues, Carlos H. M., Portelli, Stephanie and Ascher, David B. (2024). Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges. Human Genetics, 1-9. doi: 10.1007/s00439-023-02623-4

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

2024

Journal Article

A broad-spectrum α-glucosidase of glycoside hydrolase family 13 from Marinovum sp., a member of the Roseobacter clade

Li, Jinling, Mui, Janice W.-Y., da Silva, Bruna M., Pires, Douglas E.V., Ascher, David B., Madiedo Soler, Niccolay, Goddard-Borger, Ethan D. and Williams, Spencer J. (2024). A broad-spectrum α-glucosidase of glycoside hydrolase family 13 from Marinovum sp., a member of the Roseobacter clade. Applied Biochemistry and Biotechnology, 1-13. doi: 10.1007/s12010-023-04820-3

A broad-spectrum α-glucosidase of glycoside hydrolase family 13 from Marinovum sp., a member of the Roseobacter clade

2024

Journal Article

Lipid sulfoxide polymers as potential inhalable drug delivery platforms with differential albumin binding affinity

Ediriweera, Gayathri R., Butcher, Neville J., Kothapalli, Ashok, Zhao, Jiacheng, Blanchfield, Joanne T., Subasic, Christopher N., Grace, James L., Fu, Changkui, Tan, Xiao, Quinn, John F., Ascher, David B., Whittaker, Michael R., Whittaker, Andrew K. and Kaminskas, Lisa M. (2024). Lipid sulfoxide polymers as potential inhalable drug delivery platforms with differential albumin binding affinity. Biomaterials Science, 12 (11), 2978-2992. doi: 10.1039/d3bm02020g

Lipid sulfoxide polymers as potential inhalable drug delivery platforms with differential albumin binding affinity

2024

Journal Article

Characterization on the oncogenic effect of the missense mutations of p53 via machine learning

Pan, Qisheng, Portelli, Stephanie, Nguyen, Thanh Binh and Ascher, David B. (2024). Characterization on the oncogenic effect of the missense mutations of p53 via machine learning. Briefings in Bioinformatics, 25 (1) bbad428, 1-13. doi: 10.1093/bib/bbad428

Characterization on the oncogenic effect of the missense mutations of p53 via machine learning

Funding

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

Supervision

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

    Towards the accurate functional characterisation of protein coding mutations

    Principal Advisor

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

  • Doctor Philosophy

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

    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

  • Doctor Philosophy

    Rational protein engineering and inhibition

    Principal Advisor

  • Doctor Philosophy

    Computer-aided drug design: predicting and mitigating drug toxicity

    Principal Advisor

    Other advisors: Dr Stephanie Portelli

  • Doctor Philosophy

    Improving rational antibody design using machine learning

    Principal Advisor

  • Doctor Philosophy

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

    Principal Advisor

  • Doctor Philosophy

    Exploring Cardiotoxicity Risk Factors

    Principal Advisor

    Other advisors: Dr Thanh-Binh Nguyen

  • 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

    Allosteric modulation of synaptic proteins by endogenous and modified sterols

    Associate Advisor

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

  • Doctor Philosophy

    Computational design of targeted lipid technologies

    Associate Advisor

    Other advisors: Professor Megan O'Mara

  • 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

    Breaking the chain of inflammation through targetting NLR proteins

    Associate Advisor

    Other advisors: Professor Avril Robertson

Media

Enquiries

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