DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910333100097536 |
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| author | Zhang, Yangtian Zhang, Zuobai Zhong, Bozitao Misra, Sanchit Tang, Jian |
| author_facet | Zhang, Yangtian Zhang, Zuobai Zhong, Bozitao Misra, Sanchit Tang, Jian |
| contents | 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 ($60\times$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_01794 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing Zhang, Yangtian Zhang, Zuobai Zhong, Bozitao Misra, Sanchit Tang, Jian Quantitative Methods Machine Learning 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 ($60\times$ 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. |
| title | DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2306.01794 |