MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics
Teng, D.; Pitman, M.; Jha, P. K.; Sood, A.; Rufa, D.; Ryczko, K.; Bortolato, A.; Tiwary, P.
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Binding kinetics are crucial for antibody function, shaping pharmacokinetics and in vivo efficacy beyond what equilibrium affinity captures. We present "Metadynamics-Anchored Hybrid Learning for Engineering off-Rates (MAHLER)", a fully open-source machine learning/physics hybrid method that predicts relative antibody-antigen residence times at scale. Incorporating inverse-folding models into molecular dynamics simulations, MAHLER shows first-in-class screening-grade accuracy in calculating relative antibody-antigen dissociation kinetics across a family of point mutants. After initial antigen-specific setup, each prediction takes only 4 minutes on a single NVIDIA A100 GPU, compared to days even with already enhanced molecular dynamics simulations. This provides practical kinetics-aware complement to current computational design approaches that focus primarily on binding affinity for antibody-antigen complexes.
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