Antibody-Antigen Docking and Design via Hierarchical Structure Refinement

Wengong Jin, Dr.Regina Barzilay, Tommi Jaakkola
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10217-10227, 2022.

Abstract

Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope). Thus, the key question of antibody design is how to predict the 3D paratope-epitope complex (i.e., docking) for paratope generation. In this paper, we propose a new model called Hierarchical Structure Refinement Network (HSRN) for paratope docking and design. During docking, HSRN employs a hierarchical message passing network to predict atomic forces and use them to refine a binding complex in an iterative, equivariant manner. During generation, its autoregressive decoder progressively docks generated paratopes and builds a geometric representation of the binding interface to guide the next residue choice. Our results show that HSRN significantly outperforms prior state-of-the-art on paratope docking and design benchmarks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-jin22a, title = {Antibody-Antigen Docking and Design via Hierarchical Structure Refinement}, author = {Jin, Wengong and Barzilay, Dr.Regina and Jaakkola, Tommi}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {10217--10227}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/jin22a/jin22a.pdf}, url = {https://proceedings.mlr.press/v162/jin22a.html}, abstract = {Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope). Thus, the key question of antibody design is how to predict the 3D paratope-epitope complex (i.e., docking) for paratope generation. In this paper, we propose a new model called Hierarchical Structure Refinement Network (HSRN) for paratope docking and design. During docking, HSRN employs a hierarchical message passing network to predict atomic forces and use them to refine a binding complex in an iterative, equivariant manner. During generation, its autoregressive decoder progressively docks generated paratopes and builds a geometric representation of the binding interface to guide the next residue choice. Our results show that HSRN significantly outperforms prior state-of-the-art on paratope docking and design benchmarks.} }
Endnote
%0 Conference Paper %T Antibody-Antigen Docking and Design via Hierarchical Structure Refinement %A Wengong Jin %A Dr.Regina Barzilay %A Tommi Jaakkola %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-jin22a %I PMLR %P 10217--10227 %U https://proceedings.mlr.press/v162/jin22a.html %V 162 %X Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope). Thus, the key question of antibody design is how to predict the 3D paratope-epitope complex (i.e., docking) for paratope generation. In this paper, we propose a new model called Hierarchical Structure Refinement Network (HSRN) for paratope docking and design. During docking, HSRN employs a hierarchical message passing network to predict atomic forces and use them to refine a binding complex in an iterative, equivariant manner. During generation, its autoregressive decoder progressively docks generated paratopes and builds a geometric representation of the binding interface to guide the next residue choice. Our results show that HSRN significantly outperforms prior state-of-the-art on paratope docking and design benchmarks.
APA
Jin, W., Barzilay, D. & Jaakkola, T.. (2022). Antibody-Antigen Docking and Design via Hierarchical Structure Refinement. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:10217-10227 Available from https://proceedings.mlr.press/v162/jin22a.html.

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