Data integrated simulation of enzymes

Integration of molecular simulation of enzymes, kinetic modelling of biocatalytic reactions, and experimental data.

In the framework of the Cluster of Excellence SimTech,we develop and apply an integrated simulation approach to model biochemical and biophysical properties of enzymes in aqueous and non-aqueous solvents: solubility, stability, and enzymatic function.

In the project, scale bridging between the molecular model on microscopic scales and kinetic and thermodynamic models on macroscopic scales will be achieved by methods such as enhanced sampling, biased simulations, forward flux simulations, and kinetic modeling. In addition, data-driven methods such as Markov State Models to analyse trajectories will be implemented. Experimental data on the thermodynamics of solvent mixtures and on enzyme kinetics will be used to enrich the models, and models will be used to design new experiments and to generate experimental data, thus bridging between the data-rich regime of molecular and kinetic models and the data-poor regime of thermodynamic and biocatalytic experiments.


  1. Carvalho, H., Ferrario, V., & Pleiss, J. (2021). The molecular mechanism of methanol inhibition in CALB-catalyzed alcoholysis: analyzing molecular dynamics simulations by a Markov state model. J Chem Theory Comput, 17, 6570–6582.
  2. Orlando, M., Buchholz, P., Lotti, M., & Pleiss, J. (2021). The GH19 Engineering Database: sequence diversity, substrate scope, and evolution in glycoside hydrolase family 19. PLoS One, 16, e0256817.
  3. Pleiss, J. (2021). Standardized data, scalable documentation, sustainable storage –  EnzymeML as a basis for FAIR data management in biocatalysis. ChemCatChem, 13, 3909–3913.
  4. Bauer, T., Buchholz, P., & Pleiss, J. (2020). The modular structure of α/β-hydrolases. FEBS J, 287, 1035–1053.
  5. Stockinger, P., Roth, S., Müller, M., & Pleiss, J. (2020). Systematic evaluation of imine-reducing enzymes: Common principles in imine reductases, β-hydroxyacid dehydrogenases, and short-chain dehydrogenases/reductases. ChemBioChem, 21, 2689–2695.
  6. Gygli, G., & Pleiss, J. (2020). Simulation Foundry: automated and F.A.I.R. molecular modelling. J Chem Inf Model, 60, 1922–1927.
  7. Eisenkolb, I., Jensch, A., Eisenkolb, K., Kramer, A., Buchholz, P., Pleiss, J., Spiess, A., & Radde, N. (2020). Modeling of biocatalytic reactions: A workflow for model calibration, selection and validation using Bayesian statistics. AIChE J, 66, e16866.
  8. Gygli, G., Xu, X., & Pleiss, J. (2020). Meta-analysis of viscosity of aqueous deep eutectic solvents and their components. Sci Rep, 10, 21395–21395.
  9. Mangiagalli, M., Carvalho, H., Natalello, A., Ferrario, V., Pennati, M., Barbiroli, A., Lotti, M., Pleiss, J., & Brocca, S. (2020). Diverse effects of aqueous polar co-solvents on Candida antarctica lipase B. Int J Biol Macromol, 150, 930–940.
  10. Baz, J., Held, C., Pleiss, J., & Hansen, N. (2019). Thermophysical properties of glyceline-water mixtures investigated by molecular modelling. Phys Chem Chem Phys, 21, 6467–6476.
  11. Roddan, R., Gygli, G., Sula, A., Mendez-Sanchez, D., Pleiss, J., Ward, J., Keep, N., & Hailes, H. (2019). The acceptance and kinetic resolution of alpha-methyl substituted aldehydes by norcoclaurine synthases. ACS Catal, 9, 9640–9649.
  12. Xu, X., Range, J., Gygli, G., & Pleiss, J. (2019). Analysis of thermophysical properties of deep eutectic solvents by data integration. J Chem Eng Data, 65, 1172–1179.
  13. Ferrario, V., Hansen, N., & Pleiss, J. (2018). Interpretation of cytochrome P450 monooxygenase kinetics by modeling of thermodynamic activity. J Inorg Biochem, 183, 172–178.
  14. Lotti, M., Pleiss, J., Valero, F., & Ferrer, P. (2018). Enzymatic production of biodiesel: strategies to overcome methanol inactivation. Biotechnol J, 13, e1700155.

Project Members

This image shows Henrique Carvalho

Henrique Carvalho


To the top of the page