翻译后修饰
计算机科学
管道(软件)
计算生物学
人工智能
认知科学
化学
生物
生物化学
心理学
程序设计语言
酶
作者
Doo Nam Kim,Tianzhixi Yin,Tong Zhang,Alexandria Im,John Cort,Jordan C. Rozum,David D. Pollock,Weijun Qian,Song Feng
标识
DOI:10.3390/bioengineering12010026
摘要
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.
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