Pretrainable geometric graph neural network for antibody affinity maturation

Huiyu Cai, Zuobai Zhang, Mingkai Wang, Bozitao Zhong, Quanxiao Li, Yuxuan Zhong, Yanling Wu (), Tianlei Ying () and Jian Tang ()
Additional contact information
Huiyu Cai: BioGeometry
Zuobai Zhang: Mila-Québec AI Institute
Mingkai Wang: Fudan University
Bozitao Zhong: Mila-Québec AI Institute
Quanxiao Li: Fudan University
Yuxuan Zhong: Fudan University
Yanling Wu: Fudan University
Tianlei Ying: Fudan University
Jian Tang: BioGeometry

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.

Date: 2024
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DOI: 10.1038/s41467-024-51563-8

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