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Front Microbiol. 2018 Apr 12;9:725. doi: 10.3389/fmicb.2018.00725. eCollection 2018.

Prediction of Cell-Penetrating Potential of Modified Peptides Containing Natural and Chemically Modified Residues.

Author information

1
Center for Computational Biology, Indraprastha Institute of Information Technology, Okhla, India.
2
Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector-39A, Chandigarh, India.
3
Cell Biology and Immunology, CSIR-Institute of Microbial Technology, Sector-39A, Chandigarh, India.

Abstract

Designing drug delivery vehicles using cell-penetrating peptides is a hot area of research in the field of medicine. In the past, number of in silico methods have been developed for predicting cell-penetrating property of peptides containing natural residues. In this study, first time attempt has been made to predict cell-penetrating property of peptides containing natural and modified residues. The dataset used to develop prediction models, include structure and sequence of 732 chemically modified cell-penetrating peptides and an equal number of non-cell penetrating peptides. We analyzed the structure of both class of peptides and observed that positive charge groups, atoms, and residues are preferred in cell-penetrating peptides. In this study, models were developed to predict cell-penetrating peptides from its tertiary structure using a wide range of descriptors (2D, 3D descriptors, and fingerprints). Random Forest model developed by using PaDEL descriptors (combination of 2D, 3D, and fingerprints) achieved maximum accuracy of 95.10%, MCC of 0.90 and AUROC of 0.99 on the main dataset. The performance of model was also evaluated on validation/independent dataset which achieved AUROC of 0.98. In order to assist the scientific community, we have developed a web server "CellPPDMod" for predicting the cell-penetrating property of modified peptides (http://webs.iiitd.edu.in/raghava/cellppdmod/).

KEYWORDS:

Random Forest; SVM; antimicrobial peptide; chemical descriptors; in silico method; machine learning; modified cell-penetrating peptides

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