Xie Peilin, Guan Jiahui, He Xuxin, Zhao Zhihao, Guo Yilin, Sun Zhenglong, Yao Lantian, Lee Tzong-Yi, Chiang Ying-Chih
Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Blvd, Longgang District, 518172, Shenzhen, China.
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Blvd, Longgang District, 518172, Shenzhen, China.
Comput Struct Biotechnol J. 2025 Feb 27;27:804-812. doi: 10.1016/j.csbj.2025.02.029. eCollection 2025.
N7-methylguanosine (m7G) modifications play a pivotal role in RNA stability, mRNA export, and protein translation. They are closely associated with ribosome function and the regulation of gene expression. Dysregulation of m7G has been implicated in various diseases, including cancers and neurodegenerative disorders, where the loss of m7G can lead to genomic instability and uncontrolled cell proliferation. Accurate identification of m7G sites is thus essential for elucidating these mechanisms. Due to the high cost of experimentally validating m7G sites, several artificial intelligence models have been developed to predict these sites. However, the performance of these models is not yet optimal, and a user-friendly web server is still needed. To address these issues, we developed CAP-m7G, an innovative model that integrates Chaos Game Representation, Capsule Networks, and reconstruction layers. CAP-m7G achieved an accuracy of 96.63%, a specificity of 95.07%, and a Matthews correlation coefficient (MCC) of 0.933 on independent test data. Our results demonstrate that the integration of Chaos Game Representation with Capsule Network can effectively capture the crucial sequence information associated with m7G sites. The web server can be accessed at https://awi.cuhk.edu.cn/~biosequence/CAP-m7G/index.php.
N7-甲基鸟苷（m7G）修饰在RNA稳定性、mRNA输出和蛋白质翻译中起关键作用。它们与核糖体功能和基因表达调控密切相关。m7G失调与包括癌症和神经退行性疾病在内的多种疾病有关，其中m7G的缺失会导致基因组不稳定和细胞增殖失控。因此，准确识别m7G位点对于阐明这些机制至关重要。由于通过实验验证m7G位点的成本高昂，已开发了几种人工智能模型来预测这些位点。然而，这些模型的性能尚未达到最佳，仍然需要一个用户友好的网络服务器。为了解决这些问题，我们开发了CAP-m7G，这是一种集成了混沌博弈表示、胶囊网络和重建层的创新模型。CAP-m7G在独立测试数据上的准确率达到96.63%，特异性为95.07%，马修斯相关系数（MCC）为0.933。我们的结果表明，混沌博弈表示与胶囊网络的集成可以有效地捕获与m7G位点相关的关键序列信息。可通过https://awi.cuhk.edu.cn/~biosequence/CAP-m7G/index.php访问该网络服务器。
