AttABseq: an attention-based deep learning prediction method for antigen–antibody binding affinity changes based on protein sequences

Author:

Jin Ruofan12,Ye Qing1ORCID,Wang Jike1,Cao Zheng3,Jiang Dejun1,Wang Tianyue1,Kang Yu1ORCID,Xu Wanting1,Hsieh Chang-Yu1,Hou Tingjun1ORCID

Affiliation:

1. College of Pharmaceutical Science, Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Zhejiang University , Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China

2. College of Life Science, Zhejiang University , Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China

3. College of Computer Science and Technology, Zhejiang University , Yuhangtang Road 866, Hangzhou 310058, Zhejiang, China

Abstract

Abstract The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen–antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen–antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody–antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Oxford University Press (OUP)

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