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A Multifaceted Computational Approach to Understanding the MERS-CoV Main Protease and Brown Algae Compounds' Interaction.

Hattan S GattanMaha Mahmoud AlawiLeena Hussein BajraiThamir A AlandijanyIsra M AlsaadyMai M El-DalyVivek Dhar DwivediEsam Ibraheem Azhar
Published in: Marine drugs (2023)
Middle East Respiratory Syndrome (MERS) is a viral respiratory disease caused b a special type of coronavirus called MERS-CoV. In the search for effective substances against the MERS-CoV main protease, we looked into compounds from brown algae, known for their medicinal benefits. From a set of 1212 such compounds, our computer-based screening highlighted four-CMNPD27819, CMNPD1843, CMNPD4184, and CMNPD3156. These showed good potential in how they might attach to the MERS-CoV protease, comparable to a known inhibitor. We confirmed these results with multiple computer tests. Studies on the dynamics and steadiness of these compounds with the MERS-CoV protease were performed using molecular dynamics (MD) simulations. Metrics like RMSD and RMSF showed their stability. We also studied how these compounds and the protease interact in detail. An analysis technique, PCA, showed changes in atomic positions over time. Overall, our computer studies suggest brown algae compounds could be valuable in fighting MERS. However, experimental validation is needed to prove their real-world effectiveness.
Keyphrases
  • respiratory syndrome coronavirus
  • sars cov
  • molecular dynamics
  • coronavirus disease
  • deep learning
  • systematic review
  • density functional theory
  • risk assessment
  • machine learning