AffinityFlow: Guided Flows for Antibody Affinity Maturation

Can Chen, Karla-Luise Herpoldt, Chenchao Zhao, Zichen Wang, Marcus D. Collins, Shang Shang, Ron Benson

TL;DR: We introduce AffinityFlow, which explores guided flows for antibody affinity maturation

Abstract: Antibodies are widely used as therapeutics, but their development requires costly affinity maturation, involving iterative mutations to enhance binding affinity. This paper explores a sequence-only scenario for affinity maturation, using solely antibody and antigen sequences. Recently AlphaFlow wraps AlphaFold within flow matching to generate diverse protein structures, enabling a sequence-conditioned generative model of structure. Building on this, we propose an \textit{alternating optimization} framework that (1) fixes the sequence to guide structure generation toward high binding affinity using a structure-based predictor, then (2) applies inverse folding to create sequence mutations, refined by a sequence-based predictor. A key challenge is the lack of labeled data for training both predictors. To address this, we develop a \textit{co-teaching} module that incorporates valuable information from noisy biophysical energies into predictor refinement. The sequence-based predictor selects consensus samples to teach the structure-based predictor, and vice versa. Our method, \textit{AffinityFlow}, achieves state-of-the-art performance in proof-of-concept affinity maturation experiments.

Lay Summary: Camera ready

Application-Driven Machine Learning: This submission is on Application-Driven Machine Learning.

Primary Area: Applications->Health / Medicine

Keywords: flow matching, co-teaching, protein design, antibody affinity maturation, protein property prediction

Submission Number: 10602