Bioexcel webinar #85: Modelling antibodies in the post-Alphafold era: where are we now?

Antibodies are specialized proteins used by the immune system to eliminate unrecognized, potentially harmful molecules (antigens). Their ability to bind antigens with high specificity makes them an ideal molecule to work with in pharmaceutical research and drug development. However modelling antibodies offers endless challenges from a structural perspective, which have been only partially addressed by the recent developments in machine learning-based structure prediction (e.g. AlphaFold21, AlphaFold32). These algorithms tend to rely on coevolutionary information, which is missing in both the antibody’s Complementarity-Determining Regions (CDRs) and between the antibody and antigen sequences.

In this talk I will discuss when and how it is possible to obtain accurate structural predictions of these proteins and their complexes. I will demonstrate how integrating experimental data with AI-driven modeling within the BioExcel flagship software HADDOCK3-4 improves prediction accuracy5. Such models can then be used as a starting point for improving the binding properties of the complexes through antibody design.

I will showcase real-world examples of antibody structural prediction challenges, focusing on cases where pure machine learning-based prediction is unsuccessful.

Speaker:
Marco Giulini (he/him), Bijvoet Centre for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University