Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques

The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory activity, making them promising candidates for therapeutic applications. Identifying their binding mode is essential for their development. Experimental structural techniques are effective to get such information, but they are expensive and time-consuming. Here, we propose a computational approach, aiming to identify the epitope of a nanobody that acts as an agonist and a positive allosteric modulator at the rat metabotropic glutamate receptor 5. We employed multiple structure modeling tools, including various artificial intelligence algorithms for epitope mapping. The computationally identified epitope was experimentally validated, confirming the success of our approach. Additional dynamics studies provided further insights on the modulatory activity of the nanobody. The employed methodologies and approaches initiate a discussion on the efficacy of diverse techniques for epitope mapping and later nanobody engineering.