Mr Ashar Malik
Mr

Ashar Malik

Email: 

Availability

Mr Ashar Malik is:
Available for supervision

Qualifications

  • Doctor of Philosophy of Biochemistry, Massey University

Research interests

  • Structural Phylogenetics and Molecular Evolution

    I develop methods and tools for inferring evolutionary relationships directly from protein structures — an approach that recovers deep evolutionary signal lost to sequence divergence. This includes introducing structural alphabets to phylogenetics and building widely used open-access tools for the global research community.

  • Pathogen Genomics / Genomic Surveillance

    I build tools for tracking pathogen mutations in real time to support public health responses, drawing on experience from SARS-CoV-2 surveillance during the pandemic. Current work extends this toward a pan-pathogen monitoring system for emerging infectious disease threats.

  • Protein Language Models

    I explore how protein language models and high-dimensional embeddings can be used to infer deep evolutionary relationships, predict structural neighbourhoods, and assess the effects of mutations on protein function.

  • AI in Biology

    My work applies deep learning and AI, including transformers and now state-space models, to problems in structural biology, molecular evolution, and protein engineering, building practical, accessible tools for the broader research community.

  • Precision Medicine

    I develop computational tools that integrate protein structure with genomic variant data to help interpret the effects of mutations, supporting variant annotation and large-scale genomic screening programs.

  • Quantum Computing

    I research how near-term quantum computing and quantum-inspired algorithms can be applied to biomolecular problems, including encoding molecular data for quantum hardware and exploring quantum approaches to biophysical optimisation.

Search Professor Ashar Malik’s works on UQ eSpace

11 works between 2020 and 2026
All (11) Journal Article (10) Book Chapter (1)

1 - 11 of 11 works

2026

Journal Article

Transformers as a substrate for structural biology

Malik, Ashar J., Portelli, Stephanie and Ascher, David B. (2026). Transformers as a substrate for structural biology. Current Opinion in Structural Biology, 97 103218, 1-9. doi: 10.1016/j.sbi.2025.103218

Transformers as a substrate for structural biology

2026

Journal Article

Structome-TM: Complementing dataset assembly for structural phylogenetics by addressing size-based biases

Malik, Ashar J and Ascher, David B (2026). Structome-TM: Complementing dataset assembly for structural phylogenetics by addressing size-based biases. Bioinformatics Advances vbag035. doi: 10.1093/bioadv/vbag035

Structome-TM: Complementing dataset assembly for structural phylogenetics by addressing size-based biases

2026

Journal Article

mCSM-metal: A Deep Learning Resource to Predict Effect of Mutations on Metal Ion Binding

Kumar, Akshita, Malik, Ashar J. and Ascher, David B. (2026). mCSM-metal: A Deep Learning Resource to Predict Effect of Mutations on Metal Ion Binding. Journal of Molecular Biology 169678, 169678. doi: 10.1016/j.jmb.2026.169678

mCSM-metal: A Deep Learning Resource to Predict Effect of Mutations on Metal Ion Binding

2026

Journal Article

Structome-AlignViewer: On Confidence Assessment in Structure-Aware Alignments

Malik, Ashar J., Mao, Siying, Hugenholtz, Philip and Ascher, David B. (2026). Structome-AlignViewer: On Confidence Assessment in Structure-Aware Alignments. Genome Biology and Evolution, 18 (1) evag004, 1-8. doi: 10.1093/gbe/evag004

Structome-AlignViewer: On Confidence Assessment in Structure-Aware Alignments

2026

Book Chapter

MD-Phylogeny: Constructing Statistically Supported Phylogenetic Trees from Protein Structures Using Molecular Dynamics

Langer, Désirée B., Malik, Ashar J., Klemm, Paul, Allison, Jane R. and Poole, Anthony M. (2026). MD-Phylogeny: Constructing Statistically Supported Phylogenetic Trees from Protein Structures Using Molecular Dynamics. Methods in Molecular Biology. (pp. 275-290) New York, NY: Springer US. doi: 10.1007/978-1-0716-4836-0_15

MD-Phylogeny: Constructing Statistically Supported Phylogenetic Trees from Protein Structures Using Molecular Dynamics

2025

Journal Article

Protein structural phylogenetics

Puente-Lelievre, Caroline, Malik, Ashar and Douglas, Jordan (2025). Protein structural phylogenetics. Genome Biology and Evolution, 17 (8) evaf139, 1-20. doi: 10.1093/gbe/evaf139

Protein structural phylogenetics

2023

Journal Article

The Singapore National Precision Medicine Strategy

Wong, Eleanor, Bertin, Nicolas, Hebrard, Maxime, Tirado-Magallanes, Roberto, Bellis, Claire, Lim, Weng Khong, Chua, Chee Yong, Tong, Philomena Mei Lin, Chua, Raymond, Mak, Kenneth, Lim, Tit Meng, Cheong, Wei Yang, Thien, Kwee Eng, Goh, Khean Teik, Chai, Jin-Fang, Lee, Jimmy, Sung, Joseph Jao-Yiu, Wong, Tien Yin, Chin, Calvin Woon Loong, Gluckman, Peter D., Goh, Liuh Ling, Ban, Kenneth Hon Kim, Tan, Tin Wee, SG10K_Health Consortium, Van Dam, Rob M., Teo, Yik Ying, Loh, Marie, Eillot, Paul, Lee, Eng Sing ... Tan, Patrick (2023). The Singapore National Precision Medicine Strategy. Nature Genetics, 55 (2), 178-186. doi: 10.1038/s41588-022-01274-x

The Singapore National Precision Medicine Strategy

2023

Journal Article

Structome: a tool for the rapid assembly of datasets for structural phylogenetics

Malik, Ashar J., Langer, Desiree, Verma, Chandra S., Poole, Anthony M. and Allison, Jane R. (2023). Structome: a tool for the rapid assembly of datasets for structural phylogenetics. Bioinformatics Advances, 3 (1) vbad134, vbad134. doi: 10.1093/bioadv/vbad134

Structome: a tool for the rapid assembly of datasets for structural phylogenetics

2020

Journal Article

Erratum: DStabilize: A Web Resource to Generate Mirror Images of Biomolecules (Structure (2020) 28(12) (1358–1360.e2), (S0969212620302471), (10.1016/j.str.2020.07.014))

Malik, Ashar J., Aronica, Pietro G.A. and Verma, Chandra S. (2020). Erratum: DStabilize: A Web Resource to Generate Mirror Images of Biomolecules (Structure (2020) 28(12) (1358–1360.e2), (S0969212620302471), (10.1016/j.str.2020.07.014)). Structure, 28 (12), 1376-1378. doi: 10.1016/j.str.2020.11.010

Erratum: DStabilize: A Web Resource to Generate Mirror Images of Biomolecules (Structure (2020) 28(12) (1358–1360.e2), (S0969212620302471), (10.1016/j.str.2020.07.014))

2020

Journal Article

DStabilize: a web resource to generate mirror images of biomolecules

Malik, Ashar J., Aronica, Pietro G.A. and Verma, Chandra S. (2020). DStabilize: a web resource to generate mirror images of biomolecules. Structure, 28 (12), 1358-1360.e2. doi: 10.1016/j.str.2020.07.014

DStabilize: a web resource to generate mirror images of biomolecules

2020

Journal Article

Structural Phylogenetics with Confidence

Malik, Ashar J, Poole, Anthony M and Allison, Jane R (2020). Structural Phylogenetics with Confidence. Molecular Biology and Evolution, 37 (9), 2711-2726. doi: 10.1093/molbev/msaa100

Structural Phylogenetics with Confidence

Availability

Mr Ashar Malik is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Protein Structural Phylogenetics

    Protein structures change shape much more slowly than their underlying sequences, which means structure can preserve evolutionary signal long after sequence similarity has faded into noise. This makes structure a powerful lens for tracing relationships between proteins across deep evolutionary timescales, relationships that sequence-based methods alone often can't resolve. My own work centres on distance-based structural phylogenetics, though I've also dabbled on the character-based side — introducing structural alphabets to phylogenetics, which has since grown into its own field with dedicated international meetings. Projects in this space can reveal how ancient protein families are related, how function has diversified over time, and how structure-based methods compare with and complement traditional sequence approaches. Projects are flexible and can be shaped around your interests — get in touch to chat about what's possible.

  • Enzyme Engineering

    AI has transformed how we think about proteins, from predicting structures with remarkable accuracy to designing entirely new sequences from scratch. At the heart of this is the relationship between a protein's structure and its function: shape determines what an enzyme can bind, catalyse, or recognise. One of the most exciting applications is using targeted amino acid substitutions to deliberately tune how an enzyme behaves, improving stability, shifting specificity, or boosting activity. But this is far easier said than done, and there's a lot we still don't understand about how small structural tweaks ripple through to functional outcomes. This opens up many interesting directions where AI can help decode the link between structural change and function. Get in touch to explore potential projects in this space.

  • Emergence and Complexity in Biological Systems

    Many of the most interesting behaviours in biology aren't properties of individual molecules, but emerge from the way many components interact across scales, a phenomenon often described through the lens of emergent behaviour in higher-dimensional systems. Understanding how this complexity arises, and how it can be modelled, simulated, or measured, opens up new ways of thinking about biological systems as a whole rather than as a sum of parts. There's plenty of scope to explore these ideas using computational toy abstractions of biological systems. Get in touch to find out more about potential directions.

Enquiries

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