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<p>Ebola virus disease (EVD) is an acute life-threatening disease caused by highly pathogenic <i>Ebolavirus</i> (EBOV), with reported case fatality rates reaching 90%. There have been numerous EBOV outbreaks and epidemics since the first outbreak was reported in Africa in 1976. Despite the approval of three vaccines and two monoclonal antibody therapies by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of EVD the urgent need for alternative therapeutic strategies persists. In the present study, we screened a library of 235 phytocompounds derived from <i>Azadirachta indica</i> against the key EBOV viral protein 24 (VP24), VP30, VP35 and VP40 through a random forest-based machine learning model with an accuracy of 84.5%. Initially, 48 compounds were identified as active, and subsequent toxicity assessment refined the selection to a promising candidate, daucosterol. Molecular docking studies indicated that daucosterol exhibited significant binding affinity to all four viral proteins. Subsequent validation through molecular dynamics simulations confirmed the stability of daucosterol protein complexes. These results imply that daucosterol acts as a potential multitarget inhibitor against EBOV proteins and could serve as a promising lead compound for future therapeutic development against EVD.</p>

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In-silico evaluation of Azadirachta indica-derived Daucosterol against key viral proteins of Ebolavirus using ML and MD simulations approach

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Tushar Joshi,
  • Priyamvada Priyamvada,
  • Shalini Mathpal,
  • Suratha Sriram,
  • Shivani Madaan,
  • Sudha Ramaiah,
  • Anand Anbarasu

摘要

Ebola virus disease (EVD) is an acute life-threatening disease caused by highly pathogenic Ebolavirus (EBOV), with reported case fatality rates reaching 90%. There have been numerous EBOV outbreaks and epidemics since the first outbreak was reported in Africa in 1976. Despite the approval of three vaccines and two monoclonal antibody therapies by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of EVD the urgent need for alternative therapeutic strategies persists. In the present study, we screened a library of 235 phytocompounds derived from Azadirachta indica against the key EBOV viral protein 24 (VP24), VP30, VP35 and VP40 through a random forest-based machine learning model with an accuracy of 84.5%. Initially, 48 compounds were identified as active, and subsequent toxicity assessment refined the selection to a promising candidate, daucosterol. Molecular docking studies indicated that daucosterol exhibited significant binding affinity to all four viral proteins. Subsequent validation through molecular dynamics simulations confirmed the stability of daucosterol protein complexes. These results imply that daucosterol acts as a potential multitarget inhibitor against EBOV proteins and could serve as a promising lead compound for future therapeutic development against EVD.