Pretrainable geometric graph neural network for antibody affinity maturation.

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 K_D

SUBMITTER: Cai H 

PROVIDER: S-EPMC11379722 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature