Promising phytochemicals of traditional Himalayan medicinal plants against putative replication and transmission targets of SARS-CoV-2 by computational investigation

Jagadish Natesh, Priya Mondal, Bhavjot Kaur, Abdul Ajees Abdul Salam, Srikaa Kasilingam, Syed Musthapa Meeran

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Identification and repurposing of therapeutic and preventive strategies against COVID-19 are rapidly undergoing. Several medicinal plants from the Himalayan region have been traditionally used to treat various human disorders. Thus, in our current study, we intended to explore the potential ability of Himalayan medicinal plant (HMP) bioactives against COVID-19 using computational investigations. Methods: Molecular docking was performed against six crucial targets involved in the replication and transmission of SARS-CoV-2. About forty-two HMP bioactives were analyzed against these targets for their binding energy, molecular interactions, inhibition constant, and biological pathway enrichment analysis. Pharmacological properties and potential biological functions of HMP bioactives were predicted using the ADMETlab and PASS webserver respectively. Results: Our current investigation has demonstrated that the bioactives of HMPs potentially act against COVID-19. Docking results showed that several HMP bioactives had a superior binding affinity with SARS-CoV-2 essential targets like 3CLpro, PLpro, RdRp, helicase, spike protein, and human ACE2. Based on the binding energies, several bioactives were selected and analyzed for pathway enrichment studies. We have found that selected HMP bioactives may have a role in regulating immune and apoptotic pathways. Furthermore, these selected HMP bioactives have shown lower toxicity with pleiotropic biological activities, including anti-viral activities in predicting activity spectra for substances. Conclusions: Current study results can explore the possibility of HMPs as therapeutic agents against COVID-19.

Original languageEnglish
Article number104383
JournalComputers in Biology and Medicine
Volume133
DOIs
Publication statusPublished - 06-2021

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

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