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Huiyu Cai

Doctorat - Université de Montréal
Superviseur⋅e principal⋅e

Publications

Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation
Huiyu Cai
Zuobai Zhang
Mingkai Wang
Bozitao Zhong
Yanling Wu
Tianlei Ying
Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation
Huiyu Cai
Zuobai Zhang
Mingkai Wang
Bozitao Zhong
Yanling Wu
Tianlei Ying
In the realm of antibody therapeutics development, increasing the binding affinity of an antibody to its target antigen is a crucial task. T… (voir plus)his 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.
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yang Zhang
Huiyu Cai
Chence Shi
Bozitao Zhong
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work foc… (voir plus)uses on blind flexible self-docking, where we aim to predict the positions, orientations and conformations of docked molecules. Traditional physics-based methods usually suffer from inaccurate scoring functions and high inference cost. Recently, data-driven methods based on deep learning techniques are attracting growing interest thanks to their efficiency during inference and promising performance. These methods usually either adopt a two-stage approach by first predicting the distances between proteins and ligands and then generating the final coordinates based on the predicted distances, or directly predicting the global roto-translation of ligands. In this paper, we take a different route. Inspired by the resounding success of AlphaFold2 for protein structure prediction, we propose E3Bind, an end-to-end equivariant network that iteratively updates the ligand pose. E3Bind models the protein-ligand interaction through careful consideration of the geometric constraints in docking and the local context of the binding site. Experiments on standard benchmark datasets demonstrate the superior performance of our end-to-end trainable model compared to traditional and recently-proposed deep learning methods.