Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks.
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.
Dobchev
Dimitar A
DA
Department of Chemistry,Tallinn University of Technology, Akadeemia tee 15, Tallinn 19086,Estonia. dimitar@molcode.com.
Mager
Imre
I
Tulp
Indrek
I
Karelson
Gunnar
G
Tamm
Tarmo
T
Tamm
Kaido
K
Janes
Jaak
J
Langel
Ulo
U
Karelson
Mati
M
D000818:Animals; D057846:Cell-Penetrating Peptides / Q000493:pharmacokinetics*; D002477:Cells / Q000378:metabolism*; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D025341:Principal Component Analysis
D057846:Cell-Penetrating Peptides