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Journal Articles Frontiers in Chemistry Year : 2022

Discovery of putative inhibitors against main drivers of SARS-CoV-2 infection: Insight from quantum mechanical evaluation and molecular modeling

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Toheeb Balogun
  • Function : Author
Onyeka Chukwudozie
  • Function : Author
Uchechukwu Ogbodo
  • Function : Author
Idris Junaid
  • Function : Author
Olugbodi Sunday
  • Function : Author
Oluwasegun Ige
  • Function : Author
Abdullahi Aborode
  • Function : Author
Abiola Akintayo
  • Function : Author
Emmanuel Oluwarotimi
  • Function : Author
Isaac Oluwafemi
  • Function : Author
Oluwatosin Saibu
  • Function : Author
Prosper Chuckwuemaka
  • Function : Author
Damilola Omoboyowa
  • Function : Author
Abdullahi Alausa
  • Function : Author
Nkechi Atasie
  • Function : Author
Ayooluwa Ilesanmi
  • Function : Author
Gbenga Dairo
  • Function : Author
Zainab Tiamiyu
  • Function : Author
Gaber Batiha
  • Function : Author
Afrah Fahad Alkhuriji
  • Function : Author
Wafa Abdullah I. Al-Megrin
  • Function : Author
Michel de Waard
  • Function : Author
Jean-Marc Sabatier

Abstract

SARS-CoV-2 triggered a worldwide medical crisis, affecting the world’s social, emotional, physical, and economic equilibrium. However, treatment choices and targets for finding a solution to COVID-19’s threat are becoming limited. A viable approach to combating the threat of COVID-19 is by unraveling newer pharmacological and therapeutic targets pertinent in the viral survival and adaptive mechanisms within the host biological milieu which in turn provides the opportunity to discover promising inhibitors against COVID-19. Therefore, using high-throughput virtual screening, manually curated compounds library from some medicinal plants were screened against four main drivers of SARS-CoV-2 (spike glycoprotein, PLpro, 3CLpro, and RdRp). In addition, molecular docking, Prime MM/GBSA (molecular mechanics/generalized Born surface area) analysis, molecular dynamics (MD) simulation, and drug-likeness screening were performed to identify potential phytodrugs candidates for COVID-19 treatment. In support of these approaches, we used a series of computational modeling approaches to develop therapeutic agents against COVID-19. Out of the screened compounds against the selected SARS-CoV-2 therapeutic targets, only compounds with no violations of Lipinski’s rule of five and high binding affinity were considered as potential anti-COVID-19 drugs. However, lonchocarpol A, diplacol, and broussonol E (lead compounds) were recorded as the best compounds that satisfied this requirement, and they demonstrated their highest binding affinity against 3CLpro. Therefore, the 3CLpro target and the three lead compounds were selected for further analysis. Through protein–ligand mapping and interaction profiling, the three lead compounds formed essential interactions such as hydrogen bonds and hydrophobic interactions with amino acid residues at the binding pocket of 3CLpro. The key amino acid residues at the 3CLpro active site participating in the hydrophobic and polar inter/intra molecular interaction were TYR54, PRO52, CYS44, MET49, MET165, CYS145, HIS41, THR26, THR25, GLN189, and THR190. The compounds demonstrated stable protein–ligand complexes in the active site of the target (3CLpro) over a 100 ns simulation period with stable protein–ligand trajectories. Drug-likeness screening shows that the compounds are druggable molecules, and the toxicity descriptors established that the compounds demonstrated a good biosafety profile. Furthermore, the compounds were chemically reactive with promising molecular electron potential properties. Collectively, we propose that the discovered lead compounds may open the way for establishing phytodrugs to manage COVID-19 pandemics and new chemical libraries to prevent COVID-19 entry into the host based on the findings of this computational investigation.
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Dates and versions

hal-03811348 , version 1 (11-10-2022)

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Toheeb Balogun, Onyeka Chukwudozie, Uchechukwu Ogbodo, Idris Junaid, Olugbodi Sunday, et al.. Discovery of putative inhibitors against main drivers of SARS-CoV-2 infection: Insight from quantum mechanical evaluation and molecular modeling. Frontiers in Chemistry, 2022, 10, ⟨10.3389/fchem.2022.964446⟩. ⟨hal-03811348⟩
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