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A new antiviral scaffold for human norovirus identified with computer-aided approaches on the viral polymerase.

ABSTRACT: Human norovirus is the leading cause of acute gastroenteritis worldwide, affecting every year 685 million people. In about one third of cases, this virus affects children under five years of age, causing each year up to 200,000 child deaths, mainly in the developing countries. Norovirus outbreaks are associated with very significant economic losses, with an estimated societal cost of 60 billion dollars per year. Despite the marked socio-economic consequences associated, no therapeutic options or vaccines are currently available to treat or prevent this infection. One promising target to identify new antiviral agents for norovirus is the viral polymerase, which has a pivotal role for the viral replication and lacks closely homologous structures in the host. Starting from the scaffold of a novel class of norovirus polymerase inhibitors recently discovered in our research group with a computer-aided method, different new chemical modifications were designed and carried out, with the aim to identify improved agents effective against norovirus replication in cell-based assays. While different new inhibitors of the viral polymerase were found, a further computer-aided ligand optimisation approach led to the identification of a new antiviral scaffold for norovirus, which inhibits human norovirus replication at low-micromolar concentrations.

SUBMITTER: Giancotti G

PROVIDER: S-EPMC6895199 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A new antiviral scaffold for human norovirus identified with computer-aided approaches on the viral polymerase.

Giancotti Gilda G Rigo Ilaria I Pasqualetto Gaia G Young Mark T MT Neyts Johan J Rocha-Pereira Joana J Brancale Andrea A Ferla Salvatore S Bassetto Marcella M

Scientific reports 20191205 1

Human norovirus is the leading cause of acute gastroenteritis worldwide, affecting every year 685 million people. In about one third of cases, this virus affects children under five years of age, causing each year up to 200,000 child deaths, mainly in the developing countries. Norovirus outbreaks are associated with very significant economic losses, with an estimated societal cost of 60 billion dollars per year. Despite the marked socio-economic consequences associated, no therapeutic options or ...[more]

PMID: 31804593

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Project description:Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.

Dataset Information

A new antiviral scaffold for human norovirus identified with computer-aided approaches on the viral polymerase.

Publications

A new antiviral scaffold for human norovirus identified with computer-aided approaches on the viral polymerase.

Similar Datasets

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