Evolution is an all-purpose problem solver, which researchers mimic in the laboratory to engineer tailor-made (bio)molecules that aid us in combating diseases and in realizing a sustainable economy. While effective, such directed evolution campaigns are not only laborious and time-consuming but also cover only a miniscule fraction of the unimaginably large sequence space available. As a result, means to guide evolutionary trajectories along a biomolecule’s fitness landscape are sought-after, as they could greatly accelerate evolutionary searches.
Within the framework of the recently funded ML-GUIDE project, we aim to make directed evolution guidable and, ultimately, predictable by machine learning. In your role, you will be leading next-generation and third-generation sequencing campaigns to expedite the design of high-affinity binders that engage with therapeutic targets or efficient (bio)catalysts for synthetic applications. Your contributions in data generation and valorization will be crucial to establish accelerated Design-Build-Test-Learn cycles to continuously improve models via active learning and guide evolutionary trajectories toward promising but otherwise inaccessible sequence spaces.
You will be embedded in the research groups of Prof. Clemens Mayer (https://mayerlab.nl/), who leads the Molecular Evolution Group at the University of Groningen. As part of the ML-GUIDE team, you will closely collaborate with the groups of Prof. Francesca Grisoni (https://molecularmachinelearning.com/) and Dr. Robert Pollice (https://pollicegroup.web.rug.nl/) to use the power of sequencing for common aspects of engineering biomolecules for diverse applications!
