Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

Author(s): Dimitar A. Dobchev, Imre Mager, Indrek Tulp, Gunnar Karelson, Tarmo Tamm, Kaido Tamm, Jaak Janes, Ulo Langel, Mati Karelson

Journal Name: Current Computer-Aided Drug Design

Volume 6 , Issue 2 , 2010

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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.

Keywords: Artificial neural networks (ANN), Cell-penetrating peptides (CPP), QSAR, PCA

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Article Details

Year: 2010
Page: [79 - 89]
Pages: 11
DOI: 10.2174/157340910791202478
Price: $65

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