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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Farmaceutiska vetenskaper (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Pharmaceutical Sciences (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

Artificial neural networks (ANN)

Cell-penetrating peptides (CPP)

QSAR

PCA

Publication and Content Type

ref (subject category)

art (subject category)