MOTIVATION Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem. RESULTS This article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units' models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction. AVAILABILITY AND IMPLEMENTATION The method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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DNN-Dom：通过深度神经网络仅从序列中预测蛋白质结构域边界。

动机准确描绘蛋白质结构域边界在蛋白质工程和结构预测中起着重要作用。尽管机器学习方法已广泛用于预测域边界，但这些方法通常会忽略残基之间的远程相互作用，这已被证明可以改善预测性能。然而，如何同时对局部和全局相互作用建模以进一步改善域边界预测仍然是一个具有挑战性的问题。结果本文采用了一种混合深度学习方法，该方法结合了卷积神经网络和门递归单元模型进行领域边界预测。它不仅捕获本地和非本地交互，而且融合了这些功能以进行预测。此外，我们采用平衡的随机森林进行分类，以处理样本的高度不平衡和深度特征的高维度。实验结果表明，我们提出的方法（DNN-Dom）优于现有的基于机器学习的边界预测方法。我们希望DNN-Dom可用于协助蛋白质结构和功能预测。可用性和实现该方法可从http://isyslab.info/DNN-Dom/上的DNN-Dom Server获得。补充信息补充数据可从Bioinformatics在线获得。可用性和实施​​该方法可从http://isyslab.info/DNN-Dom/上的DNN-Dom Server获得。补充信息补充数据可从Bioinformatics在线获得。可用性和实现该方法可从http://isyslab.info/DNN-Dom/上的DNN-Dom Server获得。补充信息补充数据可从Bioinformatics在线获得。