Abstract
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 AsfvPolX as a potential binding site to identify antiviral drugs and classified 13 AsfvPolX 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 AsfvPolX 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 AsfvPolX 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 AsfvPolX protein.
Highlights
African swine fever virus (ASFV) is an enveloped, double-stranded, 170–193 kbp DNA virus belonging to the Asfarviridae family, genus Asfivirus, which replicates predominantly in the cytoplasm of macrophages [1]
We identified pentagastrin as a potential antiviral drug against ASFVs using principal component analysis and k-means clustering based on molecular docking from previous studies
After analyzing the differences in pocket properties of the AsfvPolX protein structures, we found that large contact areas, large enclosures, and highly hydrophilic pockets could be a significant cause of the score bias for molecular docking
Summary
African swine fever virus (ASFV) is an enveloped, double-stranded, 170–193 kbp DNA virus belonging to the Asfarviridae family, genus Asfivirus, which replicates predominantly in the cytoplasm of macrophages [1]. The scoring functions are influenced by the properties of protein pockets, resulting in scoring bias for proteins with certain properties To overcome these limitations of virtual screening, we applied principal component analysis based on 46 molecular descriptors calculated by QikProp to eliminate this scoring bias, which was effective in making the SP Glide score more balanced between 13 AsfvPolX structures in the molecular docking of the dataset. The classification of the pocket properties of AsfvPolX protein causing scoring bias against certain structures found in this study can provide an alternative approach to identify novel antiviral drugs by optimizing the scoring function of the docking programs These approaches could help improve the accuracy rate of virtual screening for various protein–ligand complexes
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