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

Kumar, Vinod and Agrawal, Piyush and Kumar, Rajesh and Bhalla, Sherry and Usmani, Salman Sadullah and Varshney, Grish C and Raghava, Gajendra P S (2018) Prediction of Cell-Penetrating Potential of Modified Peptides Containing Natural and Chemically Modified Residues. Frontiers in microbiology, 9. p. 725. ISSN 1664-302X

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Official URL: https://www.frontiersin.org/articles/10.3389/fmicb...

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 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/).

Item Type: Article
Uncontrolled Keywords: Random Forest; SVM; antimicrobial peptide; chemical descriptors; in silico method; machine learning; modified cell-penetrating peptides
Subjects: Q Science > QR Microbiology
Depositing User: Dr. K.P.S.Sengar
Date Deposited: 25 Mar 2019 15:07
Last Modified: 25 Mar 2019 15:07
URI: http://crdd.osdd.net/open/id/eprint/2189

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