School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065, China.
Curr Med Chem. 2022;29(5):894-907. doi: 10.2174/0929867328666210915112030.
Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity, and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico has also attracted much attention for its accuracy, convenience, and speed. At present, many computational prediction models have been used to identify SUMO sites, but their contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We have briefly summarized the development of bioinformatics methods for sumoylation site prediction by mainly focusing on the benchmark dataset construction, feature extraction, machine learning method, published results, and online tools. We hope that this review will provide more help for wet-experimental scholars.
蛋白质的 SUMO 化修饰是一种重要的蛋白质可逆翻译后修饰，参与调节多种细胞过程。SUMO 化修饰的蛋白质可以改变其亚细胞定位、活性和稳定性。此外，它在转录调控和信号转导等多种细胞过程中也发挥着重要作用。异常的 SUMO 化修饰与许多疾病有关，包括神经退行性疾病和免疫相关疾病以及癌症的发生。因此，鉴定 SUMO 化修饰位点（SUMO 位点）对于理解其分子机制和调节作用至关重要。与劳动密集型且昂贵的实验方法相比，基于计算的 SUMO 化位点预测方法因其准确性、便利性和速度而受到广泛关注。目前，已经有许多计算预测模型被用于鉴定 SUMO 位点，但它们的内容尚未得到全面总结和综述。因此，本文对相关模型的研究进展进行了总结和讨论。我们主要关注基准数据集构建、特征提取、机器学习方法、已发表结果和在线工具，简要总结了 SUMO 化位点预测的生物信息学方法的发展。我们希望本综述能为湿实验学者提供更多帮助。
