Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models
Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models
复制标题
使用伪氨基酸组成和随机森林模型预测和分析细胞穿透肽
DOI：
10.1007/s00726-015-1974-5
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发表时间：
2015-07-01
期刊：
影响因子：
3.5
通讯作者：
Cai, Yu-Dong
中科院分区：
文献类型：
--
作者：
Chen, Lei;Chu, Chen;Cai, Yu-Dong
细胞穿透肽是一组短肽，可以穿过细胞膜进入细胞，从而促进各种分子货物的摄取。因此，它们有可能成为强大的药物输送系统。正确识别多肽是穿透细胞还是非穿透细胞将加速这一应用。在这项研究中，我们确定了哪些特征对多肽是穿透细胞或不穿透细胞很重要，并根据从分析中提取的关键特征建立了预测模型。所研究的多肽来自以前的研究，根据氨基酸的六个属性-氨基酸频率、密码子多样性、静电电荷、分子体积、极性和二级结构-用伪氨基酸合成法将每个多肽编码为数字载体。然后采用最小冗余度、最大相关性和增量特征选择的方法对这些特征进行分析，发现其中一些特征是细胞渗透的关键决定因素。同时，建立了最优随机森林预测模型。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。
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.