Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China.
Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China.
Brief Bioinform. 2023 Sep 22;24(6). doi: 10.1093/bib/bbad365.
For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.
对于蛋白质结构的精修和设计，拥有一个高效的蛋白质折叠和对接框架至关重要，该框架可以根据给定的约束生成蛋白质的 3D 结构。在本研究中，我们引入了基于梯度的全原子蛋白质折叠和对接框架 OPUS-Fold3，它可以根据指定的约束（例如，只要它可以表示为重原子位置的函数的势能）准确生成符合要求的 3D 蛋白质结构。我们的测试表明，例如，OPUS-Fold3 在骨架折叠方面的性能可与 pyRosetta 相媲美，在侧链建模方面的性能显著提高。OPUS-Fold3 是使用 Python 和 TensorFlow 2.4 开发的，对于任何源代码级别的修改都非常友好，并且可以与其他深度学习模型无缝结合，从而促进生物学和人工智能社区之间的合作。OPUS-Fold3 的源代码可以从 http://github.com/OPUS-MaLab/opus_fold3 下载。它可免费用于学术用途。
