APSPM 2026

SMBE Australasian Protein Structural Phylogenetics Meeting

An SMBE Regional Meeting in Australasia

112Days
13Hours
17Minutes
46Seconds

Did you know? Schrödinger's What is Life? (1944) predicted genes were 'aperiodic crystals' storing life's code-script, inspiring DNA's discovery.

Organising Committee

Meet the team behind the APSPM 2026 conference.

Organiser Photo

Caroline Puente-Lelievre

University of Auckland, NZ


Caroline is a research fellow at the School of Biological Sciences and the Centre for Conputational Evolution, University of Auckland, New Zealand. Her research blends protein structure, molecular evolution, and phylogenomics to investigate evolutionary relationships across the tree of life.

Organiser Photo

Jordan Douglas

Australian National University, AU


Jordan completed his studies at the University of Auckland and is now based at the Australian National University. His interests lie in phylogenetic methods devlopment, virus evolution, and protein sequence and structure evolution; particularly the aminoacyl-tRNA synthetases.

Organiser Photo

Ashar Malik

University of Queensland, AU


Ashar is a Senior Postdoctoral Fellow at the Baker Heart and Diabetes Institute and UQ, having previously worked at A*STAR, Singapore. Part of his current research focuses on developing new methods in molecular evolution, notably 'Structome', his framework of structure-based phylogenetic methods.


Conference Helpers

Stephanie Portelli Photo

Stephanie Portelli

University of Queensland, AU


Stephanie is a research fellow at the School of Chemistry and Molecular Biosciences, University of Queensland, Australia. Her research focuses on understanding and predicting the effects of genomic variation leading to drug resistance and diseases like cancer.

Akshita Kumar Photo

Akshita Kumar

University of Queensland, AU


Akshita is a research fellow at the School of Chemistry and Molecular Biosciences, University of Queensland, Australia. Her research focuses on developing advanced machine learning and deep learning models to predict mutation effects on protein stability and function.