Affiliation:
1. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology , 291 Daehak-ro, Yuseong-gu , Daejeon 34141, Republic of Korea
Abstract
Abstract
Motivation
Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.
Results
We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody–antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model.
Availability and implementation
The source code and implementation are available at https://github.com/wsjeon92/AbFlex.
Funder
National Research Foundation of Korea
Korean Government
Publisher
Oxford University Press (OUP)