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Harnessing the potential of HSV glycoproteins as cell penetrating peptides through in-silico methods

  • Rakesh Rahangdale
  • , Mukesh Pasupuleti
  • , Fayaz Shaik Mohammad
  • , H. Raghu Chandrashekar*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Cell-penetrating peptides (CPPs) are short amino acid sequences that can cross cell membranes. This is novel emerging approach for cell imaging, gene editing, and drug delivery. In-silico tools help identify and optimize CPPs efficiently, reducing time and cost. Virus-derived efficient CPPs such as TAT, Pep-1, and C105Y (HIV-1), MPG (SV40), pepR and pepM (DENV), and VP22 (HSV-1) suggest potential for discovering additional novel CPPs. This study is the first to employ an in-silico approach to systematically identify CPPs from all eleven surface glycoproteins (gC, gB, gD, gH, gL, gK, gJ, gM, gG, gE, and gI) of HSV-1 and HSV-2. Glycoprotein sequences were analyzed with CellPPD to assess their potential as CPP. Predicted CPPs were assessed using C2Pred for individual peptide probability, while uptake efficiency was evaluated through MLCPP. The physicochemical properties of CPPs were determined using CellPPD, Heliquest, ProtParam, and PepCalc. The potential for membrane binding and localization was evaluated using APD3 and DeepTMHMM. Immunogenicity, allergenicity, toxicity, and hemolytic properties were predicted utilizing IEDB, AllerTop, ToxinPred, and HAPPENN servers. Tertiary structure and helical projection were predicted using PEP-FOLD4 and Heliquest servers. Novel CPPs were identified, including tumor-penetrating CPPs (peptides 5, 6, and 10), nuclear localization signal (NLS)-CPP (peptide 1), and heparan sulfate (HS)-binding CPPs (peptides 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, and 16). The identified CPPs exhibit diverse functional properties, indicating their potential applications in gene editing and targeted drug delivery. These findings lay the groundwork for further experimental validation and therapeutic exploration.

Original languageEnglish
Article number52
JournalNetwork Modeling Analysis in Health Informatics and Bioinformatics
Volume14
Issue number1
DOIs
Publication statusPublished - 12-2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computer Science Applications
  • Computer Networks and Communications
  • Urology
  • Computational Mathematics

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