Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus

2021 ◽  
Author(s):  
Victor O. Gawriljuk ◽  
Daniel H. Foil ◽  
Ana C. Puhl ◽  
Kimberley M. Zorn ◽  
Thomas R. Lane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Yellow Fever ◽  
Yellow Fever Virus ◽  
Fever Virus ◽  
Learning Models ◽  
Antiviral Compound ◽  
Machine Learning Models

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

Molecules ◽  
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.

scholarly journals Towards a broad-spectrum antiviral, the myristoyltransferase inhibitor IMP-1088 suppresses viral replication – the Yellow fever NS5 is myristoylated

2021 ◽  
Author(s):  
Melissa Immerheiser ◽  
Melissa Zimniak ◽  
Helen Hilpert ◽  
Nina Geiger ◽  
Eva-Maria König ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Vaccinia Virus ◽  
Yellow Fever ◽  
Yellow Fever Virus ◽  
Human Herpesvirus ◽  
Fever Virus ◽  
Neglected Diseases ◽  
Subcellular Localisation ◽  
Antiviral Compound ◽  
Hiv 1

AbstractAlthough a potent Yellow fever vaccine is available since 1937, up to 200.000 severe cases are reported per year, which indicates that virus vaccines require additional support by antiviral therapies. Direct-acting antiviral drugs against severe and widespread diseases, such as DENV and Yellow fever infections with more than millions of diagnosed diseases per year, are still not available. Since antivirals’ development against neglected diseases is uneconomical, a broadspectrum antiviral compound would be of public benefit. Here, we show that IMP-1088, a recently published myristoyltransferase-1/2 inhibitor suppressing Rhino- and Polioviruses, inhibits replication of HIV-1, Yellow fever virus, Dengue virus, Vaccinia virus, CMV, and human Herpesvirus 8 in the low nanomolar range, indicating that IMP-1088 has broad-range activity against different pathogenic virus families. The inhibition relies on virally encoded myristoylation signals since Zika, Chikungunya, and Enterovirus 71 are not affected by IMP-1088. Furthermore, we show that the Yellow fever NS5 protein is myristoylated and IMP-1088 treatment of Dengue and Yellow fever infected cells leads to a re-localisation of the viral NS5 proteins.Author SummaryTreatment of viral diseases requires the development of tailored drugs specific to inhibit certain virus families. This specificity results in missing treatment options for important human pathogens such as Yellow fever and Dengue virus infection since the development is laborious and costly. Substances acting on various virus families could solve this problem. Here, we describe that IMP-1088, an inhibitor of the cellular myristoyltransferase, inhibits HIV-1, Dengue virus, Yellow fever viruses, Vaccinia virus, and Herpesviruses at low concentrations, which do not affect cell proliferation. Viruses without predicated myristoylation sites, such as Zika viruses, were not inhibited by IMP-1088. Since no experimental evidence was provided that Yellow fever virus proteins are myristoylated, we analysed the post-translational modification of Yellow fever NS5 protein. We determined the subcellular localisation to understand the mechanism of the IMP-1088 mediated suppression and could show that both the Dengue and the Yellow fever NS5 proteins are re-localised by IMP-1088 treatment.

scholarly journals Machine learning models exploring characteristic single-nucleotide signatures in Yellow Fever Virus

2021 ◽  
Author(s):  
Álvaro Salgado ◽  
Raquel Minardi ◽  
Marta Giovanetti ◽  
Adriano Veloso ◽  
Francielly Morais-Rodrigues ◽  
...  
Keyword(s):  
Machine Learning ◽  
Yellow Fever ◽  
Protein Function ◽  
Urban Areas ◽  
Yellow Fever Virus ◽  
Fever Virus ◽  
Single Nucleotide ◽  
Host Interactions ◽  
Human Infections ◽  
The Tropics

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression). This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in the PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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