Chen Lei, Chu Chen, Huang Tao, Kong Xiangyin, Cai Yu-Dong
College of Life Science, Shanghai University, Shanghai, 200444, People's Republic of China,
Amino Acids. 2015 Jul;47(7):1485-93. doi: 10.1007/s00726-015-1974-5. Epub 2015 Apr 18.
Cell-penetrating peptides, a group of short peptides, can traverse cell membranes to enter cells and thus facilitate the uptake of various molecular cargoes. Thus, they have the potential to become powerful drug delivery systems. The correct identification of peptides as cell-penetrating or non-cell-penetrating would accelerate this application. In this study, we determined which features were important for a peptide to be cell-penetrating or non-cell-penetrating and built a predictive model based on the key features extracted from this analysis. The investigated peptides were retrieved from a previous study, and each was encoded as a numeric vector according to six properties of amino acids-amino acid frequency, codon diversity, electrostatic charge, molecular volume, polarity, and secondary structure-by the pseudo-amino acid composition method. Methods of minimum redundancy maximum relevance and incremental feature selection were then employed to analyze these features, and some were found to be key determinants of cell penetration. In parallel, an optimal random forest prediction model was built. We hope that our findings will provide new resources for the study of cell-penetrating peptides.
细胞穿透肽是一类短肽，能够穿过细胞膜进入细胞，从而促进各种分子货物的摄取。因此，它们有潜力成为强大的药物递送系统。正确识别细胞穿透肽和非细胞穿透肽将加速这一应用。在本研究中，我们确定了肽成为细胞穿透肽或非细胞穿透肽的重要特征，并基于此分析提取的关键特征建立了预测模型。所研究的肽取自先前的一项研究，并通过伪氨基酸组成方法根据氨基酸的六个属性（氨基酸频率、密码子多样性、静电荷、分子体积、极性和二级结构）将每个肽编码为数字向量。然后采用最小冗余最大相关性和增量特征选择方法分析这些特征，发现其中一些是细胞穿透的关键决定因素。同时，建立了一个最优的随机森林预测模型。我们希望我们的发现将为细胞穿透肽的研究提供新的资源。
