DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
arXiv (Cornell University)(2023)
摘要
Proteins play a critical role in carrying out biological functions, and their
3D structures are essential in determining their functions. Accurately
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 χ_1 to χ_4
and training diffusion models for each torsional angle. We evaluate the method
on several benchmarks for protein side-chain packing and show that our method
achieves improvements of 11.9% and 13.5% in angle accuracy on CASP13 and
CASP14, respectively, with a significantly smaller model size (60× fewer
parameters). Additionally, we show the effectiveness of our method in enhancing
side-chain predictions in the AlphaFold2 model. Code is available at
https://github.com/DeepGraphLearning/DiffPack.
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关键词
torsional diffusion model,autoregressive protein
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