Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning.
Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning.
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作者：
Li G;Du X;Li X;Zou L;Zhang G;Wu Z
DNA结合蛋白在选择性剪接、RNA编辑和甲基化等生物学功能中发挥着重要作用。已经提出了许多传统的机器学习（ML）方法和深度学习（DL）方法来预测DBP。然而，这些方法要么依赖于手动特征提取，要么无法捕获DNA序列中的长期依赖性。在本文中，我们提出了一种方法，称为PDBP-Fusion，来识别DBPs的基础上融合的本地功能和长期的依赖关系，只有从主序列。我们利用卷积神经网络（CNN）来学习局部特征，并使用双向长短期记忆网络（Bi-LSTM）来捕获上下文中的关键长期依赖关系。此外，我们同时执行特征提取，模型训练和模型预测。PDBP-Fusion方法可以在PDB 14189基准数据集上以86.45%的灵敏度、79.13%的特异性、82.81%的准确性和0.661 MCC预测DBP。与其他先进的预测模型相比，我们提出的方法的MCC至少增加了9.1%。此外，PDBP-Fusion在PDB 2272独立数据集上也获得了上级性能和模型鲁棒性。它表明PDBP-Fusion可以用于准确有效地预测序列中的DBP;在线服务器位于http://119.45.144.26:8080/PDBP-Fusion/。
DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.