Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

Title: Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

Volume: 6 Issue: 2

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

Affiliation:

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

Abstract: 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.

Cite this article as:

A. Dobchev Dimitar, Mager Imre, Tulp Indrek, Karelson Gunnar, Tamm Tarmo, Tamm Kaido, Janes Jaak, Langel Ulo and Karelson Mati, Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks, Current Computer-Aided Drug Design 2010; 6 (2) . https://dx.doi.org/10.2174/157340910791202478