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Chence Shi

chence.shi@mila.quebec
PhD - Université de Montréal
Supervisor
Jian Tang
Website
Google Scholar

Publications

E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang
Huiyu Cai
Chence Shi
Bozitao Zhong
Jian Tang
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… (see more)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.
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Protein Sequence and Structure Co-Design with Equivariant Translation
Chence Shi
Chuanrui Wang
Jiarui Lu
Bozitao Zhong
Jian Tang
Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and … (see more)desired functions has been a long-standing challenge in the field of bioengineering. Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models, both of which suffer from high inference costs. In this paper, we propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state from random initialization, based on context features given a priori. Our model consists of a trigonometry-aware encoder that reasons geometrical constraints and interactions from context features, and a roto-translation equivariant decoder that translates protein sequence and structure interdependently. Notably, all protein amino acids are updated in one shot in each translation step, which significantly accelerates the inference process. Experimental results across multiple tasks show that our model outperforms previous state-of-the-art baselines by a large margin, and is able to design proteins of high fidelity as regards both sequence and structure, with running time orders of magnitude less than sampling-based methods.
2023-02-01
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net