Ongoing COVID-19 outbreak has raised a drastic challenge to global public health security. Most of the patients with COVID-19 suffer from mild flu-like illnesses such as cold and fever; however, few percentages of the patients progress from severe illness to death, mostly in an immunocompromised individual. The causative agent of COVID-19 is an RNA virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite these debilitating conditions, no medication to stop the disease progression or vaccination is available till now. Therefore, we aimed to formulate a multi-epitope vaccine against SARS-CoV-2 by utilizing an immunoinformatics approach. For this purpose, we used the SARS-CoV-2 spike glycoprotein to determine the immunodominant T-and B-cell epitopes. After rigorous assessment, we designed a vaccine construct using four potential epitopes from each of the three epitope classes such as cytotoxic T-lymphocytes, helper T-lymphocyte, and linear B-lymphocyte epitopes. The designed vaccine was antigenic, immunogenic, and non-allergenic with suitable physicochemical properties and has higher solubility. More importantly, the predicted vaccine structure was similar to the native protein. Further investigations indicated a strong and stable binding interaction between the vaccine and the toll-like receptor (TLR4). Strong binding stability and structural compactness were also evident in molecular dynamics simulation. Furthermore, the computer-generated immune simulation showed that the vaccine could trigger real-lifelike immune responses upon administration into humans. Finally, codon optimization based on Escherichia coli K12 resulted in optimal GC content and higher CAI value followed by incorporating it into the cloning vector pET28þ(a). Overall, these results suggest that the designed peptide vaccine can serve as an excellent prophylactic candidate against SARS-CoV-2.
Angiotensin-converting enzyme 2 (ACE2), also known as peptidyl-dipeptidase A, belongs to the dipeptidyl carboxydipeptidases family has emerged as a potential antiviral drug target against SARS-CoV-2. Most of the ACE2 inhibitors discovered till now are chemical synthesis; suffer from many limitations related to stability and adverse side effects. However, natural, and selective ACE2 inhibitors that possess strong stability and low side effects can be replaced instead of those chemicals' inhibitors. To envisage structurally diverse natural entities as an ACE2 inhibitor with better efficacy, a 3D structure-based-pharmacophore model (SBPM) has been developed and validated by 20 known selective inhibitors with their correspondence 1166 decoy compounds. The validated SBPM has excellent goodness of hit score and good predictive ability, which has been appointed as a query model for further screening of 11,295 natural compounds. The resultant 23 hits compounds with pharmacophore fit score 75.31 to 78.81 were optimized using in-silico ADMET and molecular docking analysis. Four potential natural inhibitory molecules namely D-DOPA (Amb17613565), L-Saccharopine (Amb6600091), D-Phenylalanine (Amb3940754), and L-Mimosine (Amb21855906) have been selected based on their binding affinity (−7.5, −7.1, −7.1, and −7.0 kcal/mol), respectively. Moreover, 250 ns molecular dynamics (MD) simulations confirmed the structural stability of the ligands within the protein. Additionally, MM/GBSA approach also used to support the stability of molecules to the binding site of the protein that also confirm the stability of the selected four natural compounds. The virtual screening strategy used in this study demonstrated four natural compounds that can be utilized for designing a future class of potential natural ACE2 inhibitor that will block the spike (S) protein dependent entry of SARS-CoV-2 into the host cell.
Protease (Mpro) of SARS-CoV-2 has been identified as being able to hinder the replication process of the virus. Using GC-MS analytical methods, phytochemicals were identified from different medicinal plants that resulted in inhibitory activity of the molecules against Mpro.
Merkel cell carcinoma (MCC) is a rare neuroendocrine skin malignancy caused by human Merkel cell polyomavirus (MCV), leading to the most aggressive skin cancer in humans. MCV has been identified in approximately 43%–100% of MCC cases, contributing to the highly aggressive nature of primary cutaneous carcinoma and leading to a notable mortality rate. Currently, no existing vaccines or drug candidates have shown efficacy in addressing the ailment caused by this specific pathogen. Therefore, this study aimed to design a novel multiepitope vaccine candidate against the virus using integrated immunoinformatics and vaccinomics approaches. Initially, the highest antigenic, immunogenic, and non-allergenic epitopes of cytotoxic T lymphocytes, helper T lymphocytes, and linear B lymphocytes corresponding to the virus whole protein sequences were identified and retrieved for vaccine construction. Subsequently, the selected epitopes were linked with appropriate linkers and added an adjuvant in front of the construct to enhance the immunogenicity of the vaccine candidates. Additionally, molecular docking and dynamics simulations identified strong and stable binding interactions between vaccine candidates and human Toll-like receptor 4. Furthermore, computer-aided immune simulation found the real-life-like immune response of vaccine candidates upon administration to the human body. Finally, codon optimization was conducted on the vaccine candidates to facilitate the in silico cloning of the vaccine into the pET28+(a) cloning vector. In conclusion, the vaccine candidate developed in this study is anticipated to augment the immune response in humans and effectively combat the virus. Nevertheless, it is imperative to conduct in vitro and in vivo assays to evaluate the efficacy of these vaccine candidates thoroughly. These evaluations will provide critical insights into the vaccine’s effectiveness and potential for further development.
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