Li Shumin, Chen Junjie, Liu Bin
School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, 518055, China.
BMC Bioinformatics. 2017 Oct 10;18(1):443. doi: 10.1186/s12859-017-1842-2.
Protein remote homology detection plays a vital role in studies of protein structures and functions. Almost all of the traditional machine leaning methods require fixed length features to represent the protein sequences. However, it is never an easy task to extract the discriminative features with limited knowledge of proteins. On the other hand, deep learning technique has demonstrated its advantage in automatically learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection.
In this study, we employ the Bidirectional Long Short-Term Memory (BLSTM) to learn effective features from pseudo proteins, also propose a predictor called ProDec-BLSTM: it includes input layer, bidirectional LSTM, time distributed dense layer and output layer. This neural network can automatically extract the discriminative features by using bidirectional LSTM and the time distributed dense layer.
Experimental results on a widely-used benchmark dataset show that ProDec-BLSTM outperforms other related methods in terms of both the mean ROC and mean ROC50 scores. This promising result shows that ProDec-BLSTM is a useful tool for protein remote homology detection. Furthermore, the hidden patterns learnt by ProDec-BLSTM can be interpreted and visualized, and therefore, additional useful information can be obtained.
蛋白质远程同源性检测在蛋白质结构与功能研究中起着至关重要的作用。几乎所有传统机器学习方法都需要固定长度的特征来表示蛋白质序列。然而，在对蛋白质了解有限的情况下提取具有判别力的特征绝非易事。另一方面，深度学习技术已在自动学习表示方面展现出优势。探索深度学习技术在蛋白质远程同源性检测中的应用是值得的。
在本研究中，我们采用双向长短期记忆网络（BLSTM）从伪蛋白质中学习有效特征，还提出了一种名为ProDec - BLSTM的预测器：它包括输入层、双向LSTM、时间分布密集层和输出层。该神经网络可通过双向LSTM和时间分布密集层自动提取具有判别力的特征。
在一个广泛使用的基准数据集上的实验结果表明，ProDec - BLSTM在平均ROC和平均ROC50分数方面均优于其他相关方法。这一有前景的结果表明ProDec - BLSTM是蛋白质远程同源性检测的有用工具。此外，ProDec - BLSTM学习到的隐藏模式可以被解释和可视化，因此可以获得额外的有用信息。
