Computational drug repurposing study of antiviral drugs against main protease, RNA polymerase, and spike proteins of SARS-CoV-2 using molecular docking method

A novel multi-epitope-based peptide recombinant influenza A vaccine prototype utilizing neuraminidase and hemagglutinin surface proteins: From in silico to preliminary study

Neuraminidase and hemagglutinin serve as the crucial surface proteins of influenza viruses. Hemagglutinin, as a variable surface protein, is indispensable for vaccine development. Therefore, Neuraminidase must not be overlooked in the formulation of the recombinant vaccine prototype, which may serve as a candidate for designing a multi-epitope recombinant vaccine using immunoinformatics. Our study involves immunoinformatic screening and analysis to develop a recombinant multi-epitope vaccine prototype comprising immunodominant and conserved epitopes from influenza hemagglutinin and neuraminidase. Predicted B-cell and T-cell epitopes target a wide allele population. A 199-amino acid construct integrates MHCI1 and MHCII for both mouse and human hosts, connected by rigid and flexible linkers. Molecular docking findings suggest that this multi-epitope structure could activate TLR3,2 TLR7, and TLR8, thereby prompting protective immune responses. B-cell epitopes mediate adaptive immune responses by facilitating antigen recognition and memory formation Furthermore, the designed construct underwent in silico cloning of the vaccine prototype candidate in pET21a as a prokaryotic expression vector, followed by evaluation and exploration. It underwent characterization for physicochemical attributes, allergenicity, toxicity, and antigenicity. Validation through dynamic simulation confirms the stability of the construct. This pioneering immunoinformatic study proposes the potential of a recombinant protein vaccine prototype centered around neuraminidase and hemagglutinin immunodominant epitopes to elicit immune responses against a broad spectrum of viruses. Additionally, this vaccine prototype has been evaluated through both in silico and in vitro studies.

In silico design of a trivalent multi-epitope global-coverage vaccine-candidate protein against influenza viruses: evaluation by molecular dynamics and immune system simulation

Hemagglutinin (HA), a variable viral surface protein, is essential for influenza vaccine development. Annually, traditional trivalent vaccines containing influenza A/H1N1, A/H3N2 and B viruses are administered globally, which are not very effective for the mutations in HA protein. The aim of this study was to design a multi-epitope vaccine containing epitopes of the HA protein of H1N1, H3N2 and B viruses using immunoinformatics methods. The HA protein epitope prediction was performed using Immune Epitope Database. Toxicity, antigenicity and conservancy of the epitopes were evaluated using ToxinPred, VaxiJen and Epitope Conservancy Analysis tools, respectively. Then, nontoxic, antigenic and high conserved epitopes with high prediction scores were selected. Their binding affinity was evaluated against human and mouse MHC class I and II molecules using the HPEPDOCK tool. Physicochemical properties and post-translational modifications were evaluated using ProtParam, SOLpro and MusiteDeep tools, respectively. Top selected epitopes were joined using linkers to produce the best effective recombinant trivalent vaccine candidate to elicit cellular and humoral immune responses in mouse and human host models. These sequences were modeled and verified. By evaluating the results of various analyses of all models and the most similarity to the native HA protein, model 5 was selected as the best model. Finally, in silico cloning of this model as vaccine candidate was performed in pET21. This study was a computer-aided analysis for a multi-epitope trivalent recombinant vaccine candidate against influenza viruses. The efficiency of our best model of vaccine candidates should be validated using in vitro and in vivo studies. Communicated by Ramaswamy H. Sarma.

Computational peptide engineering approach for selection of the new C05 antibody-driven peptide with potency to blocking influenza a virus attachment; from in silico to in vivo

Antiviral drugs are currently used to prevent or treat viral infections like influenza A Virus (IAV). Nonetheless, annual genetic mutations of influenza viruses make them resistant to efficient treatment by current medications. Antiviral peptides have recently attracted researchers' attention and can potentially supplant the current medications. This study aimed to design peptides against IAV propagation. For this purpose, P2 and P3 peptides were computationally designed based on the HCDR3 region of the C05 antibody (a monoclonal antibody that neutralizes influenza HA protein and inhibits the virus attachment). The synthesized peptides were tested against the influenza A virus (A/Puerto Rico/8/34 (H1N1)) in vitro, and the most efficient peptide was selected for in vivo experiments. It was shown that the designed peptide shows much more prophylactic and therapeutic effects against the virus. These findings demonstrated that the designed peptide can control the virus infection without any cytotoxicity effect. Antiviral peptide design is acknowledged as a critical tactic to manage viral infections by preventing viral binding to the host cells.Communicated by Ramaswamy H. Sarma.

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery

BACKGROUND

The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

OBJECTIVE

Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity.

METHODS

We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space.

RESULTS

The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces.

CONCLUSION

The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.