PT  - JOURNAL ARTICLE
AU  - Cai, Huiyu
AU  - Zhang, Zuobai
AU  - Wang, Mingkai
AU  - Zhong, Bozitao
AU  - Wu, Yanling
AU  - Ying, Tianlei
AU  - Tang, Jian
TI  - Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation
AID  - 10.1101/2023.08.10.552845
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.08.10.552845
4099  - http://biorxiv.org/content/early/2023/08/11/2023.08.10.552845.short
4100  - http://biorxiv.org/content/early/2023/08/11/2023.08.10.552845.full
AB  - In the realm of antibody therapeutics development, increasing the binding affinity of an antibody to its target antigen is a crucial task. This paper presents GearBind, a pretrainable deep neural network designed to be effective for in silico affinity maturation. Leveraging multi-level geometric message passing alongside contrastive pretraining on protein structural data, GearBind capably models the complex interplay of atom-level interactions within protein complexes, surpassing previous state-of-the-art approaches on SKEMPI v2 in terms of Pearson correlation, mean absolute error (MAE) and root mean square error (RMSE). In silico experiments elucidate that pretraining helps GearBind become sensitive to mutation-induced binding affinity changes and reflective of amino acid substitution tendency. Using an ensemble model based on pretrained GearBind, we successfully optimize the affinity of CR3022 to the spike (S) protein of the SARS-CoV-2 Omicron strain. Our strategy yields a high success rate with up to 17-fold affinity increase. GearBind proves to be an effective tool in narrowing the search space for in vitro antibody affinity maturation, underscoring the utility of geometric deep learning and adept pre-training in macromolecule interaction modeling.Competing Interest StatementThe authors have declared no competing interest.