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 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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Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models

Molecules ◽

10.3390/molecules26123592 ◽

2021 ◽

Vol 26 (12) ◽

pp. 3592

Author(s):

Jiwon Choi ◽

Jun Seop Yun ◽

Hyeeun Song ◽

Yong-Keol Shin ◽

Young-Hoon Kang ◽

...

Keyword(s):

Machine Learning ◽

Molecular Docking ◽

African Swine Fever Virus ◽

Antiviral Agents ◽

Antiviral Drug ◽

African Swine Fever ◽

Antiviral Drugs ◽

Fever Virus ◽

Learning Models ◽

Machine Learning Models

African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs.

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The African Swine Fever Virus Transcriptome

Journal of Virology ◽

10.1128/jvi.00119-20 ◽

2020 ◽

Vol 94 (9) ◽

Cited By ~ 9

Author(s):

Gwenny Cackett ◽

Dorota Matelska ◽

Michal Sýkora ◽

Raquel Portugal ◽

Michal Malecki ◽

...

Keyword(s):

African Swine Fever Virus ◽

Transcription Termination ◽

African Swine Fever ◽

Hemorrhagic Fever ◽

Antiviral Drugs ◽

Fever Virus ◽

Transcription Start ◽

Control Of Gene Expression ◽

Domestic Pigs ◽

Transcription Start Sites

ABSTRACT African swine fever virus (ASFV) causes hemorrhagic fever in domestic pigs, presenting the biggest global threat to animal farming in recorded history. Despite the importance of ASFV, little is known about the mechanisms and regulation of ASFV transcription. Using RNA sequencing methods, we have determined total RNA abundance, transcription start sites, and transcription termination sites at single-nucleotide resolution. This allowed us to characterize DNA consensus motifs of early and late ASFV core promoters, as well as a polythymidylate sequence determinant for transcription termination. Our results demonstrate that ASFV utilizes alternative transcription start sites between early and late stages of infection and that ASFV RNA polymerase (RNAP) undergoes promoter-proximal transcript slippage at 5′ ends of transcription units, adding quasitemplated AU- and AUAU-5′ extensions to mRNAs. Here, we present the first much-needed genome-wide transcriptome study that provides unique insight into ASFV transcription and serves as a resource to aid future functional analyses of ASFV genes which are essential to combat this devastating disease. IMPORTANCE African swine fever virus (ASFV) causes incurable and often lethal hemorrhagic fever in domestic pigs. In 2020, ASF presents an acute and global animal health emergency that has the potential to devastate entire national economies as effective vaccines or antiviral drugs are not currently available (according to the Food and Agriculture Organization of the United Nations). With major outbreaks ongoing in Eastern Europe and Asia, urgent action is needed to advance our knowledge about the fundamental biology of ASFV, including the mechanisms and temporal control of gene expression. A thorough understanding of RNAP and transcription factor function, and of the sequence context of their promoter motifs, as well as accurate knowledge of which genes are expressed when and the amino acid sequence of the encoded proteins, is direly needed for the development of antiviral drugs and vaccines.

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The g5R (D250) Gene of African Swine Fever Virus Encodes a Nudix Hydrolase That Preferentially Degrades Diphosphoinositol Polyphosphates

Journal of Virology ◽

10.1128/jvi.76.3.1415-1421.2002 ◽

2002 ◽

Vol 76 (3) ◽

pp. 1415-1421 ◽

Cited By ~ 22

Author(s):

Jared L. Cartwright ◽

Stephen T. Safrany ◽

Linda K. Dixon ◽

Edward Darzynkiewicz ◽

Janusz Stepinski ◽

...

Keyword(s):

Membrane Trafficking ◽

African Swine Fever Virus ◽

African Swine Fever ◽

Vero Cells ◽

Fever Virus ◽

Nudix Hydrolase ◽

Ca 50 ◽

Diphosphoinositol Polyphosphate ◽

Kinetics Of

ABSTRACT The African swine fever virus (ASFV) g5R gene encodes a protein containing a Nudix hydrolase motif which in terms of sequence appears most closely related to the mammalian diadenosine tetraphosphate (Ap4A) hydrolases. However, purified recombinant g5R protein (g5Rp) showed a much wider range of nucleotide substrate specificity compared to eukaryotic Ap4A hydrolases, having highest activity with GTP, followed by adenosine 5′-pentaphosphate (p5A) and dGTP. Diadenosine and diguanosine nucleotides were substrates, but the enzyme showed no activity with cap analogues such as 7mGp3A. In common with eukaryotic diadenosine hexaphosphate (Ap6A) hydrolases, which prefer higher-order polyphosphates as substrates, g5Rp also hydrolyzes the diphosphoinositol polyphosphates PP-InsP5 and [PP]2-InsP4. A comparison of the kinetics of substrate utilization showed that the k cat/K m ratio for PP-InsP5 is 60-fold higher than that for GTP, which allows classification of g5R as a novel diphosphoinositol polyphosphate phosphohydrolase (DIPP). Unlike mammalian DIPP, g5Rp appeared to preferentially remove the 5-β-phosphate from both PP-InsP5 and [PP]2-InsP4. ASFV infection led to a reduction in the levels of PP-InsP5, ATP and GTP by ca. 50% at late times postinfection. The measured intracellular concentrations of these compounds were comparable to the respective K m values of g5Rp, suggesting that one or all of these may be substrates for g5Rp during ASFV infection. Transfection of ASFV-infected Vero cells with a plasmid encoding epitope-tagged g5Rp suggested localization of this protein in the rough endoplasmic reticulum. These results suggest a possible role for g5Rp in regulating a stage of viral morphogenesis involving diphosphoinositol polyphosphate-mediated membrane trafficking.

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Comprehensive Analysis of G-Quadruplexes in African Swine Fever Virus Genome Reveals Potential Antiviral Targets by G-Quadruplex Stabilizers

Frontiers in Microbiology ◽

10.3389/fmicb.2021.798431 ◽

2021 ◽

Vol 12 ◽

Author(s):

Elishiba Muturi ◽

Fei Meng ◽

Huan Liu ◽

Mengwei Jiang ◽

Hongping Wei ◽

...

Keyword(s):

Viral Genome ◽

Biochemical Characterization ◽

Fluorescent Protein ◽

African Swine Fever Virus ◽

Antiviral Drug ◽

African Swine Fever ◽

Fever Virus ◽

Infection Model ◽

Entire Genome ◽

G Quadruplex

African Swine Fever Virus (ASFV), a lethal hemorrhagic fever of the swine, poses a major threat to the world’s swine population and has so far resulted in devastating socio-economic consequences. The situation is further compounded by the lack of an approved vaccine or antiviral drug. Herein, we investigated a novel anti-ASFV approach by targeting G-Quadruplexes (G4s) in the viral genome. Bioinformatics analysis of putative G-quadruplex-forming sequences (PQSs) in the genome of ASFV BA71V strain revealed 317 PQSs on the forward strand and 322 PQSs on the reverse strand of the viral genome, translating to a density of 3.82 PQSs/kb covering 9.52% of the entire genome, which means that 85% of genes in the ASFV genome have at least 1 PQS on either strand. Biochemical characterization showed that 8 out of 13 conserved PQSs could form stable G4s in the presence of K+, and 4 of them could be stabilized by G4 ligands, N-Methyl Mesoporphyrin (NMM), and pyridostatin (PDS) in vitro. An enhanced green fluorescent protein (EGFP)-based reporter system revealed that the expression of two G4-containing genes, i.e., P1192R and D117L, could be significantly suppressed by NMM and PDS in 293T cells. In addition, a virus infection model showed that NMM could inhibit the replication of ASFV in Porcine Alveolar Macrophages (PAM) cells with an EC50 value of 1.16 μM. Altogether, the present study showed that functional PQSs existent in the promoters, CDS, 3′ and 5′ UTRs of the ASFV genome could be stabilized by G4 ligands, such as NMM and PDS, and could serve as potential targets for antivirals.

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Prediction of antiviral drugs against African Swine Fever Viruses based on protein-protein interaction analysis

10.1101/599043 ◽

2019 ◽

Author(s):

Zhaozhong Zhu ◽

Yunshi Fan ◽

Zena Cai ◽

Zheng Zhang ◽

Congyu Lu ◽

...

Keyword(s):

Broad Spectrum ◽

Interaction Analysis ◽

African Swine Fever Virus ◽

Antiviral Drug ◽

African Swine Fever ◽

High Specificity ◽

Antiviral Drugs ◽

Multiple Sources ◽

Swine Industry ◽

Spectrum Effect

AbstractThe African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is no effective antiviral drug or vaccine against the virus until now. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein-protein interactions (PPIs) between ASFV and swine were curated by integration of PPIs from multiple sources. Thirty-two swine proteins were observed to interact with ASFVs and were defined as AIPs. They were found to play a central role in the swine PPI network, with significant larger degree, betweenness and smaller shortest path length than other swine proteins. Some of AIPs also interacted with several other viruses and could be taken as potential targets of drugs for broad-spectrum effect, such as HSP90AB1. Finally, the antiviral drugs which targeted AIPs and ASFV proteins were predicted. Several drugs with either broad-spectrum effect or high specificity on AIPs were identified, such as Polaprezinc. This work could not only deepen our understanding towards the ASFV-swine interactions, but also help for the development of effective antiviral drugs against the ASFVs.

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Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.13 ◽

2017 ◽

Vol 7 (8) ◽

pp. 30

Author(s):

Hyeuk Kim

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Cluster Analysis ◽

Unsupervised Learning ◽

Principal Component ◽

Component Analysis ◽

Baseball Players ◽

Partitioning Around Medoids ◽

Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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