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Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning.

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  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Basel, Switzerland : MDPI, [2000-
    • الموضوع:
      African Swine Fever/*drug therapy ; African Swine Fever Virus/*drug effects ; Antiviral Agents/*pharmacology; African Swine Fever Virus/metabolism ; Animals ; Binding Sites/drug effects ; Machine Learning ; Swine ; Viral Proteins/metabolism ; Virus Replication/drug effects
    • نبذة مختصرة :
      African swine fever virus (ASFV) is a highly contagious virus that causes severe hemorrhagic viral disease resulting in high mortality in domestic and wild pigs, until few antiviral agents can inhibit ASFV infections. Thus, new anti-ASFV drugs need to be urgently identified. Recently, we identified pentagastrin as a potential antiviral drug against ASFVs using molecular docking and machine learning models. However, the scoring functions are easily influenced by properties of protein pockets, resulting in a scoring bias. Here, we employed the 5'-P binding pocket of Asfv PolX as a potential binding site to identify antiviral drugs and classified 13 Asfv PolX structures into three classes based on pocket parameters calculated by the SiteMap module. We then applied principal component analysis to eliminate this scoring bias, which was effective in making the SP Glide score more balanced between 13 Asfv PolX structures in the dataset. As a result, we identified cangrelor and fostamatinib as potential antiviral drugs against ASFVs. Furthermore, the classification of the pocket properties of Asfv PolX protein can provide an alternative approach to identify novel antiviral drugs by optimizing the scoring function of the docking programs. Here, we report a machine learning-based novel approach to generate high binding affinity compounds that are individually matched to the available classification of the pocket properties of Asfv PolX protein.
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    • Grant Information:
      NRF-2018R1D1A1B07050744 National Research Foundation of Korea; NRF-2019R1A2C1086914 National Research Foundation of Korea; grant no. 3200532 to D.T. Ministry of Agriculture, Food and Rural Affairs (MAFRA)
    • Contributed Indexing:
      Keywords: African swine fever virus; AsfvPolX; cangrelor; fostamatinib; machine learning; molecular docking
    • الرقم المعرف:
      0 (Antiviral Agents)
      0 (Viral Proteins)
    • الموضوع:
      Date Created: 20211224 Date Completed: 20220111 Latest Revision: 20220111
    • الموضوع:
      20221213
    • الرقم المعرف:
      PMC8703626
    • الرقم المعرف:
      10.3390/ijms222413414
    • الرقم المعرف:
      34948216