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

OBJECTIVES: The new Coronavirus (SARS-CoV-2) created a pandemic in the world in late 2019 and early 2020. Unfortunately, despite the increasing prevalence of the disease, there is no effective drug for the treatment. A computational drug repurposing study would be an appropriate and rapid way to provide an effective drug in the treatment of the coronavirus disease of 2019 (COVID-19) pandemic. In this study, the inhibitory potential of more than 50 antiviral drugs on three important proteins of SARS-CoV-2, was investigated using the molecular docking method. METHODS: By literature review, three important proteins, including main protease, RNA-dependent RNA polymerase (RdRp), and spike, were selected as the drug targets. The three-dimensional (3D) structure of protease, spike, and RdRp proteins was obtained from the Protein Data Bank. Proteins were energy minimized. More than 50 antiviral drugs were considered as candidates for protein inhibition, and their 3D structure was obtained from Drug Bank. Molecular docking settings were defined using Autodock 4.2 software and the algorithm was executed. RESULTS: Based on the estimated binding energy of docking and hydrogen bond analysis and the position of drug binding, five drugs including, indinavir, lopinavir, saquinavir, nelfinavir, and remdesivir, had the highest inhibitory potential for all three proteins. CONCLUSIONS: According to the results, among the mentioned drugs, saquinavir and lopinavir showed the highest inhibitory potential for all three proteins compared to the other drugs. This study suggests that saquinavir and lopinavir could be included in the laboratory phase studies as a two-drug treatment for SARS-CoV-2 inhibition.

Molecular docking study of flavonoid compounds for possible matrix metalloproteinase-13 inhibition.

OBJECTIVES: Matrix metalloproteinase-13 (MMP-13) has been reported to be involved in different biological processes such as degradation of extracellular matrix proteins, activating or degrading some significant regulatory proteins, wound healing, tissue remodeling, cartilage degradation, bone development, bone mineralization, ossification, cell migration, and tumor cell invasion. Further, MMP-13 participates in many oral diseases such as tooth decay, gingivitis, and degradation of enamel and tissue around the implant. In addition, inhibition of MMP-13 has shown therapeutic properties for Alzheimer's disease (AD). We performed molecular docking to assess the binding affinity of 29 flavonoid compounds with the MMP-13. Additionally, pharmacokinetic and toxicity characteristics of the top-ranked flavonoids were studied. The current study also intended to identify the most important amino acids involved in the inhibition of MMP-13 based on topological feature (degree) in the ligand-amino acid network for MMP-13. METHODS: Molecular docking and network analysis were studied using AutoDock and Cytoscape software, respectively. Pharmacokinetic and toxicity characteristics of compounds were predicted using bioinformatics web tools. RESULTS: The results revealed that nine of the studied flavonoids had considerable estimated free energy of binding and inhibition constant: Rutin, nicotiflorin, orientin, vitexin, apigenin-7-glucoside, quercitrin, isoquercitrin, quercitrin-3-rhamnoside, and vicenin-2. Proline-242 was found to be the most important amino acid inhibiting the enzyme. CONCLUSIONS: The results of the current study may be helpful in the prevention and therapeutic procedures of many disorders such as cancer, tooth caries, and AD. Nevertheless, validation tests are required in the future.