Speaker
Description
Artificial Intelligence (AI) is now considered a mature technology for the de novo design of protein binders, offering a potentially more reliable option when working with poorly immunogenic or highly conserved target antigens, or when a specific conformational epitope must be targeted. Current benchmarks indicate that more than 10 designs typically must be characterized to yield binders with low nanomolar affinity and sufficient stability. However, most published successes have focused on biomarkers (e.g., PD-L1) for which structural data and targeting interfaces were already established via classical approaches. In contrast, there are very few documented cases where novel biomarkers and functionally relevant epitopes have been successfully targeted.
CDKL5 is an essential kinase whose dysfunction correlates with neurological conditions, including CDKL5 Deficiency Disorder. The sequence of its N-terminal kinase domain is highly conserved among CDKL enzymes, whereas its C-terminus is disordered and contains several evolutionary conserved sequences likely involved in functional regulation. These structural characteristics explain the historical difficulty in recovering highly specific antibodies for CDKL5 epitopes using classical discovery techniques. Consequently, the ability to quantify and precisely localize the protein in cells and tissues has been severely impaired.
Our project devised a customized approach to score de novo redesigned protein mini-binders targeting a specific, dynamically stable conformational epitope unique to the catalytic domain of CDKL5. The protocol integrated AI-driven biophysical models and physics-based simulations to shortlist the candidate pool to 15. The preliminary results will be discussed. Furthermore, we will show how a similar approach can be used to target also specific functional linear epitopes.