Alpha-helical proteins ( $\alpha$ TMPs) are essential in various biological processes. Despite their tertiary structures are crucial for revealing complex functions, experimental structure determination remains challenging and costly. In the past decades, various sequence-based topology prediction methods have been developed to bridge the gap between the sequences and structures by characterizing the structural features, but significant improvements are still required. Deep learning brings a great opportunity for its powerful representation learning capability from limited original data. In this work, we improved our $\alpha$ TMP topology prediction method DMCTOP using deep learning, which composed of two deep convolutional blocks to simultaneously extract local and global contextual features. Consequently, the inputs were simplified to reflect the original features of the sequence, including 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 $\alpha$ TMP sequence. To validate the effectiveness of our method, we benchmarked DMCTOP against 13 peer methods according to the whole sequence, the transmembrane segment and the traditional criterion in testing experiments. All the results reveal that our method achieved the highest prediction accuracy and outperformed all the previous methods. The method is available at https://icdtools.nenu.edu.cn/dmctop .

中文翻译：

---

基于深度多尺度卷积神经网络的α-螺旋跨膜蛋白拓扑预测改进

α-螺旋蛋白 ( $\阿尔法$ TMPs）在各种生物过程中是必不可少的。尽管它们的三级结构对于揭示复杂功能至关重要，但实验结构确定仍然具有挑战性且成本高昂。在过去的几十年中，已经开发了各种基于序列的拓扑预测方法，通过表征结构特征来弥合序列和结构之间的差距，但仍然需要进行重大改进。深度学习为其从有限的原始数据中强大的表示学习能力带来了巨大的机会。在这项工作中，我们改进了我们的$\阿尔法$ TMP拓扑预测方法DMCTOP使用深度学习，由两个深度卷积块组成，同时提取局部和全局上下文特征。因此，输入被简化以反映序列的原始特征，包括蛋白质序列特征和进化保守特征。DMCTOP 可以高效准确地识别所有拓扑类型和 N 端方向$\阿尔法$ TMP 序列。为了验证我们方法的有效性，我们在测试实验中根据整个序列、跨膜片段和传统标准将 DMCTOP 与 13 种对等方法进行了基准测试。所有结果表明，我们的方法实现了最高的预测精度，并且优于所有以前的方法。该方法可在https://icdtools.nenu.edu.cn/dmctop .