Improved prediction of antibody and their complexes with clustered generative modelling ensembles

Gaining structural insights into antibody-antigen complexes is crucial for understanding antigen recognition mechanisms and advancing therapeutic antibody design. However, accurate prediction of the structure of highly variable complementarity-determining region 3 on the antibody heavy chain (CDR-H3 loop) remains a significant challenge due to its increased length and conformational variability. While AlphaFold2-multimer (AF2) has made substantial progress in protein structure prediction, its application on antibodies and antibody-antigen complexes is limited by the weak evolutionary signals in the CDR region and the lack of structural diversity in its output.

To address these limitations, we propose a workflow that combines AlphaFlow to generate ensembles of potential loop conformations with integrative modelling of antibody-antigen complexes with HADDOCK. Improving the structural diversity of the H3 loop increases the success rate of subsequent docking tasks. Our analysis shows that while AF2 generally predicts accurate antibody structures, it struggles with the H3 loop. In cases where AF2 mispredicts the loop, we leverage AlphaFlow to generate ensembles of loop conformations via score-based flow matching, followed by clustering to produce a structurally diverse set of models. We demonstrate that these ensembles significantly improve antibody-antigen docking performance compared to the standard AF2 ensembles.

The input datasets and codes involved in this research are available at https://github.com/haddocking/alphaflow-antibodies. All the resulting modelling data are available from Zenodo (https://zenodo.org/records/14906314).