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Yangtian Zhang

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Publications

DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Yangtian Zhang
Zuobai Zhang
Bozitao Zhong
Sanchit Misra
Jian Tang
Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Ac… (see more)curately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang
Huiyu Cai
Chence Shi
Yangtian Zhang
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 selfdocking, 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.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
Minghao Xu
Zuobai Zhang
Jiarui Lu
Zhaocheng Zhu
Yangtian Zhang
Chang Ma
Runcheng Liu
Jian Tang
We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lac… (see more)k of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_Benchmark
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)
doi.org
openreview.net