The Computational Protein Engineering group aims to design proteins more effectively using state-of-the-art research approaches, while contributing to the development of novel tools for in silico prediction of protein function.
We are an interdisciplinary group of scientists working at the interface of data science, machine learning, bioinformatics, protein modelling, directed evolution, biochemistry, enzymology, and metabolic engineering. The main goal is to develop innovative and sustainable solutions to the most pressing issues facing our societies in terms of bioproduction and bioremediation.
The main areas of focus are:
- Directed evolution of enzymes for cell factory development of natural products and biochemicals.
- Implementation of machine learning for improving protein function: activity, expression, selectivity, stability, etc.
- Understanding and harnessing non-additive epistatic effects and allosteric networks for improving protein function.
- Multiparametric optimization of enzyme properties for cost-effective development of biocatalytic processes.
- Gain insights for engineering protein dynamics using molecular dynamics simulations and other approaches.
The Computational Protein Engineering group is located at The Novo Nordisk Foundation Center for Biosustainability, DTU Biosustain and it is headed by Senior Researcher Carlos Acevedo-Rocha.