This is an old revision of the document!

Email: julian.zaugg@uqconnect.edu.au
LinkedIn: julianzaugg
Github: julianzaugg My CV

My thesis has focused on how experimental data produced from protein engineering studies could be complemented by computational and statistical methods to predict beneficial mutations and understand protein function. I have used sequence, structural and biochemical data for the epoxide hydrolase from the fungus Aspergillus niger (AnEH) as a model system. I have implemented contrasting matching learning methods, i.e. generative vs discriminative, to predict selectivity-enhancing mutations. I have also applied molecular modelling methods such as docking, molecular dynamics and free energy calculations to determine the effect of ligand binding and multiple reaction pathways on the enantioselectivity of AnEH.

In combination with my thesis research, I have collaborated with Prof. Burkhard Rost’s group (Technical University of Munich, protein structure and function prediction) and provided technical and analytical assistance to the groups of Prof. Elizabeth Gillam (University of Queensland, characterisation of cytochrome P450 enzymes) and Prof. Bostjan Kobe (University of Queensland, structural biology of infection and immunity).

Publications
Zaugg J., Gumulya Y., Gillam E. M. J. and Bodén M., Computational Tools for Directed Evolution: a Comparison of Prospective and Retrospective Strategies. Methods in Molecular Biology, 2014, 1179, 315-333.

Zaugg J., Gumulya Y., Mark A. E., Bodén M. and Malde A. K., Using Modelling to Complement Experiment: Understanding the Origin of Enantioselectivity of Epoxide Hydrolase, 2017 [Under revision]

Zaugg J., Gumulya Y., Malde A. K. and Bodén M., Learning Epistatic Interactions from Sequence-Activity Data to Predict Enantioselectivity, 2017 [Submitted]