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My thesis 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. Using sequence, structural and biochemical data for the epoxide hydrolase from the fungus Aspergillus niger (AnEH) as a model system, I implemented contrasting machine learning methods, i.e. generative vs discriminative, to predict selectivity-enhancing mutations. I 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.

During my thesis research, I 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).

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., Malde A. K. and Bodén M., Learning Epistatic Interactions from Sequence-Activity Data to Predict Enantioselectivity, Journal of Computer–Aided Molecular Design, 2017 , 31, 1085–1096.

Zaugg J., Gumulya Y., Mark A. E., Bodén M. and Malde A. K., The Effect of Binding on the Enantioselectivity of an Epoxide Hydrolase, Journal of Chemical Information and Modeling, 2018, 58(3), 630-640.


Created by julian (Julian Zaugg) on 2017/09/11 18:23.

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  • Last modified: 2019/03/26 12:45
  • by julian