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Prediction of Cell-Penetrating Peptides using Artificial Neural Networks
Stockholm University, Faculty of Science, Department of Neurochemistry. University of Tartu, Estonia.
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2010 (English)In: Current Computer-Aided Drug Design, ISSN 1573-4099, Vol. 6, no 2, 79-89 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2010. Vol. 6, no 2, 79-89 p.
Keyword [en]
Artificial neural networks (ANN), Cell-penetrating peptides (CPP), QSAR, PCA
National Category
Pharmaceutical Sciences Computer and Information Science
URN: urn:nbn:se:su:diva-43384DOI: 10.2174/157340910791202478ISI: 000277889300001OAI: diva2:356168
Available from: 2010-10-11 Created: 2010-10-11 Last updated: 2015-04-22Bibliographically approved

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Langel, Ülo
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