Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
Development and Planning Department, Inner Mongolia University, Hohhot, China.
Curr Drug Metab. 2019;20(3):217-223. doi: 10.2174/1389200219666181010114750.
Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification.
Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification.
We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs.
In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.
细胞穿透肽（CPPs）是一类重要的短肽，能够促进各种分子进入细胞。CPP 可以通过质膜将药物分子输送到不同的细胞细胞器。因此，CPP 的鉴定及其相关机制已被广泛研究。为了揭示大量 CPP 的穿透机制，有必要开发方便、快速的 CPP 鉴定方法。
生化实验可以为准确鉴定 CPP 提供精确的细节，但这些方法昂贵且费力。为了克服这些缺点，已经开发了几种计算方法来识别 CPP。我们对 CPP 鉴定中机器学习方法的发展进行了综述。本综述深入探讨了 CPP 的鉴定。
我们总结了基于机器学习的 CPP 鉴定方法，并比较了 11 种不同计算方法的构建策略。此外，我们还指出了预测 CPP 时的局限性和困难。
本综述报道了使用机器学习方法进行 CPP 鉴定的最新研究。我们还讨论了用计算方法识别 CPP 的未来发展方向。
