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Email: julian.zaugg@uqconnect.edu.au
LinkedIn : julianzaugg

My thesis has primarily 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. Sequence, structural and biochemical data for the epoxide hydrolase from the fungus {\it Aspergillus niger} has been used as a model system. The ability of contrasting machine learning methods, i.e. {\it generative} vs {\it discriminative}, to predict selectivity-enhancing mutations was evaluated. Molecular modelling methods such as docking, molecular dynamics and free energy calculations were used to understand the origin of enantioselectivity.

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]