Kim Doo Nam, Yin Tianzhixi, Zhang Tong, Im Alexandria K, Cort John R, Rozum Jordan C, Pollock David, Qian Wei-Jun, Feng Song
Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USA.
National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USA.
Bioengineering (Basel). 2024 Dec 31;12(1):26. doi: 10.3390/bioengineering12010026.
Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.
翻译后修饰（PTMs）是蛋白质合成后氨基酸发生的共价变化，包括侧链和肽主链上的共价修饰。许多翻译后修饰对细胞和分子功能及结构有深远影响，其意义还延伸到进化研究领域。鉴于这些影响，我们探讨了如何利用人工智能（AI）研究翻译后修饰。首先，讨论了采用人工智能的基本原理及其在理解翻译后修饰功能方面的优势。然后，比较了各种深度学习架构和程序，包括语言模型的最新应用，用于预测蛋白质上的翻译后修饰位点及其调控功能。最后，描述了我们的高通量翻译后修饰数据生成管道，该管道将数据格式化为适合人工智能训练和预测的形式。我们希望这篇综述能阐明未来翻译后修饰人工智能模型可改进的领域，从而为翻译后修饰生物工程领域做出贡献。
