Virtual Screening of <i>Artemisia annua</i> Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a −7 kcal/mol binding energy score, the top-2 docked complex with a −6.9 kcal/mol binding energy score, and the top-3 docked complex with a −6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were −24.23 kcal/mol, −26.38 kcal/mol, and −25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as −17.23 kcal/mol (top-1 complex), −24.75 kcal/mol (top-2 complex), and −24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results

Computational Based Designing of a Multi-Epitopes Vaccine against Burkholderia mallei

The emergence of antibiotic resistance in bacterial species is a major threat to public health and has resulted in high mortality as well as high health care costs. Burkholderia mallei is one of the etiological agents of health care-associated infections. As no licensed vaccine is available against the pathogen herein, using reverse vaccinology, bioinformatics, and immunoinformatics approaches, a multi-epitope-based vaccine against B. mallei was designed. In completely sequenced proteomes of B. mallei, 18,405 core, 3671 non-redundant, and 14,734 redundant proteins were predicted. Among the 3671 non-redundant proteins, 3 proteins were predicted in the extracellular matrix, 11 were predicted as outer membrane proteins, and 11 proteins were predicted in the periplasmic membrane. Only two proteins, type VI secretion system tube protein (Hcp) and type IV pilus secretin proteins, were selected for epitope prediction. Six epitopes, EAMPERMPAA, RSSPPAAGA, DNRPISINL, RQRFDAHAR, AERERQRFDA, and HARAAQLEPL, were shortlisted for multi-epitopes vaccine design. The predicted epitopes were linked to each other via a specific GPGPG linker and the epitopes peptide was then linked to an adjuvant molecule through an EAAAK linker to make the designed vaccine more immunologically potent. The designed vaccine was also found to have favorable physicochemical properties with a low molecular weight and fewer transmembrane helices. Molecular docking studies revealed vaccine construct stable binding with MHC-I, MHC-II, and TLR-4 with energy scores of &minus;944.1 kcal/mol, &minus;975.5 kcal/mol, and &minus;1067.3 kcal/mol, respectively. Molecular dynamic simulation assay noticed stable dynamics of the docked vaccine-receptors complexes and no drastic changes were observed. Binding free energies estimation revealed a net value of &minus;283.74 kcal/mol for the vaccine-MHC-I complex, &minus;296.88 kcal/mol for the vaccine-MHC-II complex, and &minus;586.38 kcal/mol for the vaccine-TLR-4 complex. These findings validate that the designed vaccine construct showed promising ability in terms of binding to immune receptors and may be capable of eliciting strong immune responses once administered to the host. Further evidence from experimentations in mice models is required to validate real immune protection of the designed vaccine construct against B. mallei

Unveiling HuB genes and drug design against Helicobacter pylori infection by network biology and biophysics techniques

Helicobacter pylori (H. pylori) is mainly considered for causing chronic gastritis, which can lead to several secondary complications like peptic ulcer and pre-malignant lesions for example atrophic gastritis, intestinal dysplasia and metaplasia, with the etiological factor of developing gastric cancer. Recent research demonstrates that H.pylori colonizes the stomach mucosa of more than fifty populations around the globe. This research focuses on unveiling hub genes, and diagnostic and drug targets against said organism by utilizing various types of networking biology and biophysical approaches. In data retrieval, the GSE19826 dataset was obtained from the gene expression omnibus database and microarray data set from array express. Geo2r analysis predicted a total number of 7 DEGs and 10 hub genes, next functional protein association network analysis (STRING) unveiled that among 10 Hub genes only 3 genes were found more interactive with other genes and involved in pathogenesis, The shortlisted three genes were further analyzed for survival analysis using Gene Expression Profiling Interactive Analysis (GEPIA) and predicted the survival rate of targeted genes. Moreover, functional enchainment analysis was done using the ToppFun server, the server predicted that COL11A1 and COL10A1 were more involved in the pathogenesis of the H. pylori infection. Furthermore, the COL10A1 gene was subjected to protein structure prediction. In molecular docking analysis, the asinex antibacterial library was screened for potential inhibitors, and one compound was predicted as a strong inhibitor with the best binding at −10.23 kcal/mol. The docking results were further validated through molecular dynamic simulation analysis and the MD simulation analysis evaluated the dynamic movement of the docked complex in various nanoseconds, the MD simulation results predicted that the docked complexes are stable throughout the simulation and can be used as a potential inhibitor against the said pathogen, however experimental study is required to further validate the predicted results and design drug against targeted pathogen

Proteome-Wide Screening of Potential Vaccine Targets against Brucella melitensis

The ongoing antibiotic-resistance crisis is becoming a global problem affecting public health. Urgent efforts are required to design novel therapeutics against pathogenic bacterial species. Brucella melitensis is an etiological agent of brucellosis, which mostly affects sheep and goats but several cases have also been reported in cattle, water buffalo, yaks and dogs. Infected animals also represent the major source of infection for humans. Development of safer and effective vaccines for brucellosis remains a priority to support disease control and eradication in animals and to prevent infection to humans. In this research study, we designed an in-silico multi-epitopes vaccine for B. melitensis using computational approaches. The pathogen core proteome was screened for good vaccine candidates using subtractive proteomics, reverse vaccinology and immunoinformatic tools. In total, 10 proteins: catalase; siderophore ABC transporter substrate-binding protein; pyridoxamine 5′-phosphate oxidase; superoxide dismutase; peptidylprolyl isomerase; superoxide dismutase family protein; septation protein A; hypothetical protein; binding-protein-dependent transport systems inner membrane component; and 4-hydroxy-2-oxoheptanedioate aldolase were selected for epitopes prediction. To induce cellular and antibody base immune responses, the vaccine must comprise both B and T-cells epitopes. The epitopes were next screened for antigenicity, allergic nature and water solubility and the probable antigenic, non-allergic, water-soluble and non-toxic nine epitopes were shortlisted for multi-epitopes vaccine construction. The designed vaccine construct comprises 274 amino acid long sequences having a molecular weight of 28.14 kDa and instability index of 27.62. The vaccine construct was further assessed for binding efficacy with immune cell receptors. Docking results revealed that the designed vaccine had good binding potency with selected immune cell receptors. Furthermore, vaccine-MHC-I, vaccine-MHC-II and vaccine-TLR-4 complexes were opted based on a least-binding energy score of −5.48 kcal/mol, 0.64 kcal/mol and −2.69 kcal/mol. Those selected were then energy refined and subjected to simulation studies to understand dynamic movements of the docked complexes. The docking results were further validated through MMPBSA and MMGBSA analyses. The MMPBSA calculated −235.18 kcal/mol, −206.79 kcal/mol, and −215.73 kcal/mol net binding free energy, while MMGBSA estimated −259.48 kcal/mol, −206.79 kcal/mol and −215.73 kcal/mol for TLR-4, MHC-I and MHC-II complexes, respectively. These findings were validated by water-swap and entropy calculations. Overall, the designed vaccine construct can evoke proper immune responses and the construct could be helpful for experimental researchers in formulation of a protective vaccine against the targeted pathogen for both animal and human us