An Investigation of the Antiviral Potential of Phytocompounds against Avian Infectious Bronchitis Virus through Template-Based Molecular Docking and Molecular Dynamics Simulation Analysis.

Vaccination is widely used to control Infectious Bronchitis in poultry; however, the limited cross-protection and safety issues associated with these vaccines can lead to vaccination failures. Keeping these limitations in mind, the current study explored the antiviral potential of phytocompounds against the Infectious Bronchitis virus using in silico approaches. A total of 1300 phytocompounds derived from fourteen botanicals were screened for their potential ability to inhibit the main protease, papain-like protease or RNA-dependent RNA-polymerase of the virus. The study identified Methyl Rosmarinate, Cianidanol, Royleanone, and 6,7-Dehydroroyleanone as dual-target inhibitors against any two of the key proteins. At the same time, 7-alpha-Acetoxyroyleanone from Rosmarinus officinalis was found to be a multi-target protein inhibitor against all three proteins. The potential multi-target inhibitor was subjected to molecular dynamics simulations to assess the stability of the protein-ligand complexes along with the corresponding reference ligands. The findings specified stable interactions of 7-alpha-Acetoxyroyleanone with the protein targets. The results based on the in silico study indicate that the phytocompounds can potentially inhibit the essential proteins of the Infectious Bronchitis virus; however, in vitro and in vivo studies are required for validation. Nevertheless, this study is a significant step in exploring the use of botanicals in feed to control Infectious Bronchitis infections in poultry.

Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review.

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety-they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

Scavenger receptors in host defense: from functional aspects to mode of action.

Scavenger receptors belong to a superfamily of proteins that are structurally heterogeneous and encompass the miscellaneous group of transmembrane proteins and soluble secretory extracellular domain. They are functionally diverse as they are involved in various disorders and biological pathways and their major function in innate immunity and homeostasis. Numerous scavenger receptors have been discovered so far and are apportioned in various classes (A-L). Scavenger receptors are documented as pattern recognition receptors and known to act in coordination with other co-receptors such as Toll-like receptors in generating the immune responses against a repertoire of ligands such as microbial pathogens, non-self, intracellular and modified self-molecules through various diverse mechanisms like adhesion, endocytosis and phagocytosis etc. Unlike, most of the scavenger receptors discussed below have both membrane and soluble forms that participate in scavenging; the role of a potential scavenging receptor Angiotensin-Converting Enzyme-2 has also been discussed whereby only its soluble form might participate in preventing the pathogen entry and replication, unlike its membrane-bound form. This review majorly gives an insight on the functional aspect of scavenger receptors in host defence and describes their mode of action extensively in various immune pathways involved with each receptor type. Video abstract.

Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets.

An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.