Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

Dimitar A Dobchev; Imre Mager; Indrek Tulp; Gunnar Karelson; Tarmo Tamm; Kaido Tamm; Jaak Janes; Ulo Langel; Mati Karelson

doi:10.2174/157340910791202478

Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks

Curr Comput Aided Drug Des. 2010;6(2):79-89. doi: 10.2174/157340910791202478.

Authors

Dimitar A Dobchev¹, Imre Mager, Indrek Tulp, Gunnar Karelson, Tarmo Tamm, Kaido Tamm, Jaak Janes, Ulo Langel, Mati Karelson

Affiliation

¹ Department of Chemistry,Tallinn University of Technology, Akadeemia tee 15, Tallinn 19086,Estonia. dimitar@molcode.com.

PMID: 20402661
DOI: 10.2174/157340910791202478

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.

MeSH terms

Animals
Cell-Penetrating Peptides / pharmacokinetics*
Cells / metabolism*
Computer Simulation*
Humans
Neural Networks, Computer*
Principal Component Analysis

Substances

Cell-Penetrating Peptides