Optimizing broadly neutralizing antibodies via all-atom interaction modeling and pre-trained language models

Antibody optimization is a fundamental challenge, and the identification of antibody-antigen interactions is crucial in the optimization process. However, current methods cannot accurately predict antibody-antigen interactions due to the lack of all-atom modeling, thus being unable to improve the time-consuming and costly traditional optimization techniques. We present InterAb, a novel model developed for predicting antibody-antigen interactions and optimizing antibodies through all-atom modeling and antibody language models. Leveraging the proposed all-atom modeling approach, AtomInter, and pre-trained antibody language models, InterAb outperforms existing methods in predicting antibody specificity and antibody-antigen binding affinity. In the antibody library we constructed, InterAb successfully identified antibodies capable of binding to influenza A virus. An antibody optimization framework, InterAb-Opt, was further developed for the optimization of broadly neutralizing antibodies. For R1-32 antibody, biolayer interferometry results reveal that 85%, 80%, 90%, and 67.5% of the 40 optimized antibodies exhibit enhanced binding affinities to wild-type SARS-CoV-2, Lambda, BQ.1.1, and EG.5.1, respectively, with a maximum improvement of up to 96-fold. For the newly emerging BA.2.86 and KP.3, 55% and 52.5% of the optimized antibodies notably transition from non-binding to binding. Neutralization assays demonstrated that the optimized antibody exhibited enhanced neutralization activity across multiple targets, highlighting the capability of InterAb-Opt in engineering broadly neutralizing antibodies. This technology enables precise analysis of antibody-antigen interactions and optimization of broadly neutralizing antibodies, holding promise for addressing challenges in immune evasion and vaccine design.