The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the protein fold recognition task. Our neural network model combines 1D-convolutional layers with gated recurrent unit (GRU) layers. The GRU cells, as recurrent layers, cope with the processing issues associated to the highly variable protein sequence lengths and so extract a fold-related embedding of fixed size for each protein domain. These embeddings are then used to perform the pairwise fold recognition task, which is based on transferring the fold type of the most similar template structure. We compare our model with several template-based and deep learning-based methods from the state-of-the-art. The evaluation results over the well-known LINDAHL and SCOP_TEST sets, along with a proposed LINDAHL test set updated to SCOP 1.75, show that our embeddings perform significantly better than these methods, specially at the fold level. Supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2020.3012732 , source code and trained models are available at http://sigmat.ugr.es/~amelia/CNN-GRU-RF+/ .

中文翻译：

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使用卷积和递归神经网络从序列中识别蛋白质折叠

从其氨基酸序列中识别蛋白质折叠类型提供了有关蛋白质 3D 结构的重要见解。在本文中，我们提出了一种深度学习架构，可以处理蛋白质残基级特征来解决蛋白质折叠识别任务。我们的神经网络模型将一维卷积层与门控循环单元 (GRU) 层相结合。GRU 细胞作为循环层，处理与高度可变的蛋白质序列长度相关的处理问题，因此为每个蛋白质结构域提取固定大小的折叠相关嵌入。然后使用这些嵌入来执行成对折叠识别任务，该任务基于转移最相似模板结构的折叠类型。我们将我们的模型与最先进的几种基于模板和基于深度学习的方法进行比较。对著名的 LINDAHL 和 SCOP_TEST 集的评估结果，以及更新到 SCOP 1.75 的提议的 LINDAHL 测试集，表明我们的嵌入性能明显优于这些方法，特别是在折叠级别。补充材料，可在计算机协会数字图书馆中找到，网址为http://doi.ieeecomputersociety.org/10.1109/TCBB.2020.3012732 ，源代码和训练模型可在 http://sigmat.ugr.es/~amelia/CNN-GRU-RF+/ .