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

ABSTRACT: 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.

SUBMITTER: Choi J

PROVIDER: S-EPMC8231271 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recently moderate-virulence classical swine fever virus (CSFV) strains have been proven capable of generating postnatal persistent infection (PI), defined by the maintenance of viremia and the inability to generate CSFV-specific immune responses in animals. These animals also showed a type I interferon blockade in the absence of clinical signs. In this study, we assessed the infection generated in 7-week-old CSFV PI wild boars after infection with the African swine fever virus (ASFV). The wild boars were divided in two groups and were infected with ASFV. Group A comprised boars who were CSFV PI in a subclinical form and Group B comprised pestivirus-free wild boars. Some relevant parameters related to CSFV replication and the immune response of CSFV PI animals were studied. Additionally, serum soluble factors such as IFN-α, TNF-α, IL-6, IL-10, IFN-γ and sCD163 were analysed before and after ASFV infection to assess their role in disease progression.After ASFV infection, only the CSFV PI wild boars showed progressive acute haemorrhagic disease; however, the survival rates following ASFV infection was similar in both experimental groups. Notwithstanding, the CSFV RNA load of CSFV PI animals remained unaltered over the study; likewise, the ASFV DNA load detected after infection was similar between groups. Interestingly, systemic type I FN-α and IL-10 levels in sera were almost undetectable in CSFV PI animals, yet detectable in Group B, while detectable levels of IFN-γ were found in both groups. Finally, the flow cytometry analysis showed an increase in myelomonocytic cells (CD172a+) and a decrease in CD4+ T cells in the PBMCs from CSFV PI animals after ASFV infection.Our results showed that the immune response plays a role in the progression of disease in CSFV subclinically infected wild boars after ASFV infection, and the immune response comprised the systemic type I interferon blockade. ASFV does not produce any interference with CSFV replication, or vice versa. ASFV infection could be a trigger factor for the disease progression in CSFV PI animals, as their survival after ASFV was similar to that of the pestivirus-free ASFV-infected group. This fact suggests a high resistance in CSFV PI animals even against a virus like ASFV; this may mean that there are relevant implications for CSF control in endemic countries. The diagnosis of ASFV and CSFV co-infection in endemic countries cannot be ruled out and need to be studied in greater depth.

Project description:The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model's ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of [Formula: see text] on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms.

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

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