Improved protein structure refinement guided by deep learning based accuracy estimation.
Improved protein structure refinement guided by deep learning based accuracy estimation.
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DOI：
10.1038/s41467-021-21511-x
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发表时间：
2021-02-26
影响因子：
16.6
通讯作者：
Baker D
中科院分区：
文献类型：
--
作者：
Hiranuma N;Park H;Baek M;Anishchenko I;Dauparas J;Baker D
我们开发了一个深度学习框架（DeepAccNet），用于估计蛋白质模型中每个残基的准确性和残基-残基距离的有符号误差，并使用这些预测来指导Rosetta蛋白质结构的优化。该网络使用3D卷积来评估局部原子环境，然后使用2D卷积来提供其全局上下文，并优于其他类似预测蛋白质结构模型准确性的方法。PDB中X射线和cryoEM结构的总体准确度预测与其分辨率相关，该网络应广泛用于评估预测结构模型和实验确定结构的准确度，并识别可能出错的特定区域。在Rosetta改进协议的多个阶段中引入准确性预测大大提高了所得蛋白质结构模型的准确性，说明了深度学习如何改善对生物分子全局能量最小值的搜索。在这里，作者介绍了DeepAccNet，这是一个深度学习框架，可以估计蛋白质模型中每个残基的准确度和残基-残基距离的有符号误差，用于指导Rosetta蛋白质结构的优化。基准测试表明，与其他相关的最先进的方法相比，准确性预测和细化的改进。
We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules. Here the authors present DeepAccNet, a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, which are used to guide Rosetta protein structure refinement. Benchmarking suggests an improvement of accuracy prediction and refinement compared to other related state of the art methods.
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影响因子：
2.9
作者：
Haas J;Barbato A;Behringer D;Studer G;Roth S;Bertoni M;Mostaguir K;Gumienny R;Schwede T
通讯作者：
Schwede T
影响因子：
3
作者：
Ray A;Lindahl E;Wallner B
通讯作者：
Wallner B
影响因子：
2.9
作者：
Park, Hahnbeom;Lee, Gyu Rie;Baker, David
通讯作者：
Baker, David
影响因子：
2.9
作者：
Read, Randy J.;Sammito, Massimo D.;Croll, Tristan I.
通讯作者：
Croll, Tristan I.
DOI：
10.1073/pnas.1719115115
发表时间：
2018-03-20
影响因子：
11.1
作者：
Park, Hahnbeom;Ovchinnikov, Sergey;Baker, David
通讯作者：
Baker, David