From Enzymes to Antibodies – the Surprising Insights of ML-driven Optimization
Time: 11:30 am
day: Day One
Details:
In this presentation, we will discuss unexpected findings from our work on ML-guided, lab-in-the-loop protein design targeting affinity, expression, and stability for antibodies. Notably, we demonstrate that it is possible to achieve improved binding affinity without altering the complementarity-determining regions (CDRs). On the other hand, we show that even substantial modifications to CDRs, including the highly variable CDR3, can be applied while retaining strong binding properties, provided the changes are guided by a custom-conditioned protein language model (PLM). This opens an exciting path for active-learning-driven antibody optimization starting from an immunization campaign or even a single lead.
