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Incorporating Flexibility in Antibody-Antigen Interaction Prediction

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If you have a question about this talk, please contact Pietro Lio .

Antibodies are vital to our immune system, recognizing specific antigens with high precision. This makes them promising for therapies against infectious diseases. While potent monoclonal antibodies can be extracted, studying their binding remains costly and time-consuming. Integrating in-vitro and in-silico approaches accelerates development, but many computational methods overlook the crucial role of flexibility in binding.

In this talk, we will explore why flexibility is essential for predicting antibody-antigen interactions and how it can be modeled using the predicted Local Distance Difference Test (pLDDT) as a proxy within a fingerprint-based approach. We will conclude with a discussion on future directions and challenges in the field.

Google Meet: https://meet.google.com/bij-bnwo-ije

This talk is part of the Foundation AI series.

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Cavendish, Edwards room Lecture Theatre, Medical Research Council (MRC) Cognition and Brain Sciences Dr Constance Tipper Lecture Theatre, Department of Engineering

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