Motivation

Thanks to the recent advances in structural biology, nowadays 3D structures of various proteins are solved on a routine basis. A large portion of these structures contain structural repetitions or internal symmetries. To understand the evolution mechanisms of these proteins and how structural repetitions affect the protein function, we need to be able to detect such proteins very robustly. As deep learning is particularly suited to deal with spatially organized data, we applied it to the detection of proteins with structural repetitions.

Results

We present DeepSymmetry, a versatile method based on 3D convolutional networks that detects structural repetitions in proteins and their density maps. Our method is designed to identify tandem repeat proteins, proteins with internal symmetries, symmetries in the raw density maps, their symmetry order and also the corresponding symmetry axes. Detection of symmetry axes is based on learning 6D Veronese mappings of 3D vectors, and the median angular error of axis determination is less than one degree. We demonstrate the capabilities of our method on benchmarks with tandem-repeated proteins and also with symmetrical assemblies. For example, we have discovered about 7800 putative tandem repeat proteins in the PDB.

Availability and implementation

The method is available at https://team.inria.fr/nano-d/software/deepsymmetry. It consists of a C++ executable that transforms molecular structures into volumetric density maps, and a Python code based on the TensorFlow framework for applying the DeepSymmetry model to these maps.

Supplementary information

Supplementary dataSupplementary data are available at Bioinformatics online.

中文翻译：

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DeepSymmetry：使用3D卷积网络识别蛋白质结构中的串联重复序列和内部对称性

动机

由于结构生物学的最新进展，如今，各种蛋白质的3D结构都可以常规解决。这些结构的大部分包含结构重复或内部对称性。要了解这些蛋白质的进化机制以及结构重复如何影响蛋白质功能，我们需要能够非常可靠地检测到此类蛋白质。由于深度学习特别适合处理空间组织的数据，因此我们将其应用于具有结构重复的蛋白质检测。

结果

我们提出了DeepSymmetry，这是一种基于3D卷积网络的通用方法，可以检测蛋白质中的结构重复及其密度图。我们的方法旨在识别串联重复蛋白，具有内部对称性的蛋白，原始密度图中的对称性，它们的对称顺序以及相应的对称轴。对称轴的检测基于学习3D向量的6D Veronese映射，并且轴确定的中值角误差小于1度。我们在串联重复蛋白和对称装配的基准测试中证明了我们方法的功能。例如，我们在PDB中发现了大约7800个假定的串联重复蛋白。

可用性和实施

该方法可从https://team.inria.fr/nano-d/software/deepsymmetry获得。它由一个C ++可执行文件（将分子结构转换为体积密度图）和一个基于TensorFlow框架的Python代码（将DeepSymmetry模型应用于这些图）组成。

补充资料

补充数据补充数据可从Bioinformatics在线获得。