Zhou Yuxuan, Wu Jingcheng, Yao Shihao, Xu Yulian, Zhao Wenbin, Tong Yunguang, Zhou Zhan
Innovation Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang University Innovation Institute for Artificial Intelligence in Medicine - Aoming (Hangzhou) Biomedical Co., Ltd. Joint Laboratory, Hangzhou, 310018, China.
Innovation Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Comput Biol Med. 2023 Sep;164:107288. doi: 10.1016/j.compbiomed.2023.107288. Epub 2023 Aug 1.
Circular RNAs (circRNAs) have been found to have the ability to encode proteins through internal ribosome entry sites (IRESs), which are essential RNA regulatory elements for cap-independent translation. Identification of IRES elements in circRNA is crucial for understanding its function. Previous studies have presented IRES predictors based on machine learning techniques, but they were mainly designed for linear RNA IRES. In this study, we proposed DeepCIP (Deep learning method for CircRNA IRES Prediction), a multimodal deep learning approach that employs both sequence and structural information for circRNA IRES prediction. Our results demonstrate the effectiveness of the sequence and structure models used by DeepCIP in feature extraction and suggest that integrating sequence and structural information efficiently improves the accuracy of prediction. The comparison studies indicate that DeepCIP outperforms other comparative methods on the test set and real circRNA IRES dataset. Furthermore, through the integration of an interpretable analysis mechanism, we elucidate the sequence patterns learned by our model, which align with the previous discovery of motifs that facilitate circRNA translation. Thus, DeepCIP has the potential to enhance the study of the coding potential of circRNAs and contribute to the design of circRNA-based drugs. DeepCIP as a standalone program is freely available at https://github.org/zjupgx/DeepCIP.
环状RNA（circRNAs）已被发现能够通过内部核糖体进入位点（IRESs）编码蛋白质，IRESs是不依赖帽结构的翻译所必需的RNA调控元件。鉴定circRNA中的IRES元件对于理解其功能至关重要。先前的研究提出了基于机器学习技术的IRES预测器，但它们主要是为线性RNA IRES设计的。在本研究中，我们提出了DeepCIP（环状RNA IRES预测的深度学习方法），这是一种多模态深度学习方法，它利用序列和结构信息进行circRNA IRES预测。我们的结果证明了DeepCIP使用的序列和结构模型在特征提取方面的有效性，并表明有效整合序列和结构信息可提高预测准确性。比较研究表明，DeepCIP在测试集和真实circRNA IRES数据集上优于其他比较方法。此外，通过整合可解释的分析机制，我们阐明了我们模型学习到的序列模式，这与先前发现的促进circRNA翻译的基序一致。因此，DeepCIP有潜力加强对circRNAs编码潜力的研究，并有助于基于circRNA的药物设计。DeepCIP作为一个独立程序可在https://github.org/zjupgx/DeepCIP上免费获取。
