Dr Antonia Mey
I pursue an interdisciplinary line of research at the interface of chemistry, physics, biology and computing to address challenges in biomolecular modelling. I use a combination of different modelling techniques and machine learning to tackle open questions in antimicrobial resistance. In particular I am interested in the role of metalloenzymes in antibiotic resistance and how we can develop new computational tools for predictive modelling of dynamics, interactions, and catalysis of these metalloenzymes.
Both, addressing methodological problems to improve machine learning techniques for e.g. learning from molecular simulation data, as well as applying these new tools to build reliable and robust computational models in a pharmaceutically relevant context from part of my core research interest.
- Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A, J. Juárez-Jiménez, A. Gupta, G. Karunanithy, A.S.J.S. Mey, et al. Chem. Sci, accepted (2020)
- BioSimSpace: An interoperable Python framework for biomolecular simulation, L.O. Hedges, A.S.J.S. Mey, et al., JOSS, 4, 1831 (2019)
- Allosteric effects in a catalytically impaired variant of the enzyme Cyclophilin A may be explained by changes in nano-microsecond time scale motions, P. Wapeesittipan, A.S.J.S. Mey, M. Walkinshaw, J. Michel, Comms. Chem. 2, 41 (2019)
- Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations, A.S.J.S. Mey, J. Juárez-Jiménez, J. Michel, J. Comput. Aided. Mol. Des. 32, 199 (2018)