Yu Lezheng, Jing Runyu, Liu Fengjuan, Luo Jiesi, Li Yizhou
School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China.
College of Cybersecurity, Sichuan University, Chengdu 610065, China.
Mol Ther Nucleic Acids. 2020 Oct 10;22:862-870. doi: 10.1016/j.omtn.2020.10.005. eCollection 2020 Dec 4.
Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs.
癌症是对人类健康最危险的疾病之一。准确预测抗癌肽（ACP）对于新型抗癌药物的开发和设计具有重要价值。当前的深度神经网络模型在ACP分类任务中取得了最先进的预测准确率。然而，基于现有研究，尚不清楚哪种深度学习架构能实现最佳性能。因此，在本研究中，我们首先对三种重要的深度学习架构进行了系统探索：卷积网络、循环网络和卷积循环网络，用于区分ACP和非ACP。我们发现具有双向长短期记忆单元的循环神经网络优于其他架构。通过使用所提出的模型，我们实现了一种基于序列的深度学习工具（DeepACP），以准确预测肽表现出抗癌活性的可能性。结果表明，DeepACP优于几种现有方法，可作为预测抗癌肽的有效工具。此外，我们对深度学习模型进行了可视化和理解。我们希望我们的策略能够扩展到识别其他类型的肽，并可能为蛋白质组学和新药开发提供更多帮助。
