Dobchev Dimitar A, Mager Imre, Tulp Indrek, Karelson Gunnar, Tamm Tarmo, Tamm Kaido, Janes Jaak, Langel Ulo, Karelson Mati
Department of Chemistry,Tallinn University of Technology, Akadeemia tee 15, Tallinn 19086,Estonia.
Curr Comput Aided Drug Des. 2010;6(2):79-89. doi: 10.2174/157340910791202478.
An investigation of cell-penetrating peptides (CPPs) by using combination of Artificial Neural Networks (ANN) and Principle Component Analysis (PCA) revealed that the penetration capability (penetrating/non-penetrating) of 101 examined peptides can be predicted with accuracy of 80%-100%. The inputs of the ANN are the main characteristics classifying the penetration. These molecular characteristics (descriptors) were calculated for each peptide and they provide bio-chemical insights for the criteria of penetration. Deeper analysis of the PCA results also showed clear clusterization of the peptides according to their molecular features.
通过结合人工神经网络（ANN）和主成分分析（PCA）对细胞穿透肽（CPP）进行的一项研究表明，101种被检测肽的穿透能力（穿透/不穿透）能够以80%-100%的准确率进行预测。人工神经网络的输入是对穿透进行分类的主要特征。针对每种肽计算这些分子特征（描述符），它们为穿透标准提供了生化见解。对主成分分析结果的深入分析还表明，肽根据其分子特征呈现出明显的聚类。
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