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

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...

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Autores principales: Choi, Jiwon, Tark, Dongseob, Lim, Yun-Sook, Hwang, Soon B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703626/
https://www.ncbi.nlm.nih.gov/pubmed/34948216
http://dx.doi.org/10.3390/ijms222413414
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author Choi, Jiwon
Tark, Dongseob
Lim, Yun-Sook
Hwang, Soon B.
author_facet Choi, Jiwon
Tark, Dongseob
Lim, Yun-Sook
Hwang, Soon B.
author_sort Choi, Jiwon
collection PubMed
description 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.
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spelling pubmed-87036262021-12-25 Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning Choi, Jiwon Tark, Dongseob Lim, Yun-Sook Hwang, Soon B. Int J Mol Sci Article 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. MDPI 2021-12-14 /pmc/articles/PMC8703626/ /pubmed/34948216 http://dx.doi.org/10.3390/ijms222413414 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Choi, Jiwon
Tark, Dongseob
Lim, Yun-Sook
Hwang, Soon B.
Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning
title Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning
title_full Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning
title_fullStr Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning
title_full_unstemmed Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning
title_short Identification of African Swine Fever Virus Inhibitors through High Performance Virtual Screening Using Machine Learning
title_sort identification of african swine fever virus inhibitors through high performance virtual screening using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703626/
https://www.ncbi.nlm.nih.gov/pubmed/34948216
http://dx.doi.org/10.3390/ijms222413414
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