Background: 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 automatic learning representations. It is worthwhile to explore the applications of deep learning techniques to the protein remote homology detection.
Results: In this study, we employ the Bidirectional Long Short-Term Memory (BLSTM) to learn effective features from protein sequences, 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.
Conclusion: 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.
The supplementary files consists of the main python programs and the used datasets. To use these programs or datasets, please download following files:
ProtDet-CCH depends on the following toolkits:
Please cite the following papers when using the programs or datasets at this website:
Li S, Chen J, Liu B. Protein remote homology detection based on bidirectional long short-term memory, BMC Bioinformatics 2017;18:443.