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,Jian Tang


引用 0|浏览42
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. We then derive a powerful ensemble model based on GearBind and use it to optimize the affinity of two antibodies with distinct formats and target antigens. The affinity of CR3022 against the spike (S) protein of the SARS-CoV-2 Omicron strain is increased by up to 17-fold, and the affinity of a fully human single-domain antibody (UdAb) against 5T4 by up to 5.6-fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks. ### Competing Interest Statement The authors have declared no competing interest.
AI 理解论文
Chat Paper