DMCTOP: Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network

Alpha-helical transmembrane proteins ( $\alpha \text{TMPs}$ ) belong to an important category of integral membranes. Their structures are highly valuable in relevant research, but costly to solve experimentally. Sequence-based topology prediction provides a practical computational approach to characterize the structure features. Although much progress had been made in the past decade, there is significant room for improvement in predicting the topology structure. Deep learning brings a great opportunity for its capability of mining new features from data. In this work, we propose a novel $\alpha \text{TMP}$ topology prediction method DMCTOP using a Deep Multi-Scale Convolutional Neural Network (DMCNN), which composes of two deep convolutional blocks to extract local and global contextual features. Consequently, the inputs of DMCTOP is simplified to a protein sequence feature and an evolutionary conservation feature. DMCTOP can efficiently and accurately identify all topological types and the N-terminal orientation for an $\alpha \text{TMP}$ sequence. In the testing experiments, the prediction accuracy was calculated according to the whole sequence, the transmembrane segments and the traditional criterion. Our state-of-the-art method achieved the highest prediction accuracy compared to all the previous methods. The standalone tool is available at https://github.com/NENUBioCompute/DMCTOP.