Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models
文献类型：期刊论文
| 作者 | Chen, L; Chu, C; Huang, T; Kong, XY; Cai, YD |
| 刊名 | AMINO ACIDS
 |
| 出版日期 | 2015 |
| 卷号 | 47期号:7页码:1485-1493 |
| 关键词 | Cell-penetrating peptide Pseudo-amino acid composition Minimum redundancy maximum relevance Incremental feature selection Random forest |
| 通讯作者 | Chen, L (reprint author), Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China.,chen_lei1@163.com ; chuchen@sibcb.ac.cn ; tohuangtao@126.com ; xykong@sibs.ac.cn ; cai_yud@126.com |
| 英文摘要 | 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. |
| 学科主题 | Biochemistry & Molecular Biology |
| 类目[WOS] | Biochemistry & Molecular Biology |
| 关键词[WOS] | MODIFIED MAHALANOBIS DISCRIMINANT ; PROTEIN-STRUCTURE PREDICTION ; SUPPORT VECTOR MACHINE ; GENE-EXPRESSION LEVEL ; CODON USAGE BIAS ; SECONDARY STRUCTURE ; DELIVERY ; CLASSIFICATION ; INFORMATION ; MECHANISMS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000355745900018 |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.143/handle/331003/79]  |
| 专题 | 上海生化细胞研究所_上海生科院生化细胞研究所 |
| 推荐引用方式 GB/T 7714 | Chen, L,Chu, C,Huang, T,et al. Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models[J]. AMINO ACIDS,2015,47(7):1485-1493. |
| APA | Chen, L,Chu, C,Huang, T,Kong, XY,&Cai, YD.(2015).Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models.AMINO ACIDS,47(7),1485-1493. |
| MLA | Chen, L,et al."Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models".AMINO ACIDS 47.7(2015):1485-1493. |
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