tetano
Editor, Senior Moderator
PLoS One
. 2022 Mar 24;17(3):e0264700.
doi: 10.1371/journal.pone.0264700. eCollection 2022.
Epitope-based peptide vaccine design and elucidation of novel compounds against 3C like protein of SARS-CoV-2
Muhammad Sajid[SUP] 1 [/SUP], Saigha Marriam[SUP] 2 [/SUP], Hamid Mukhtar[SUP] 3 [/SUP], Summar Sohail[SUP] 4 [/SUP], Muhammad Sajid[SUP] 1 [/SUP], Sheikh Arslan Sehgal[SUP] 5 [/SUP]
Affiliations
Abstract
Coronaviruses (CoVs) are positive-stranded RNA viruses with short clubs on their edges. CoVs are pathogenic viruses that infect several animals and plant organisms, as well as humans (lethal respiratory dysfunctions). A noval strain of CoV has been reported and named as SARS-CoV-2. Numerous COVID-19 cases were being reported all over the World. COVID-19 and has a high mortality rate. In the present study, immunoinformatics techniques were utilized to predict the antigenic epitopes against 3C like protein. B-cell epitopes and Cytotoxic T-lymphocyte (CTL) were designed computationally against SARS-CoV-2. Multiple Sequence Alignment (MSA) of seven complete strains (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2) was performed to elucidate the binding domain and interacting residues. MHC-I binding epitopes were evaluated by analyzing the binding affinity of the top-ranked peptides having HLA molecule. By utilizing the docked complexes of CTL epitopes with antigenic sites, the binding relationship and affinity of top-ranked predicted peptides with the MHC-I HLA protein were investigated. The molecular docking analyses were conducted on the ZINC database library and twelve compounds having least binding energy were scrutinized. In conclusion, twelve CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, SEDMLNPNY, LSQTGIAV, VLDMCASLK, LTQDHVDIL, TTLNDFNLV, CTSEDMLNP, TTITVNVLA, YNGSPSGVY, and SMQNCVLKL) were identified against SARS-CoV-2.
. 2022 Mar 24;17(3):e0264700.
doi: 10.1371/journal.pone.0264700. eCollection 2022.
Epitope-based peptide vaccine design and elucidation of novel compounds against 3C like protein of SARS-CoV-2
Muhammad Sajid[SUP] 1 [/SUP], Saigha Marriam[SUP] 2 [/SUP], Hamid Mukhtar[SUP] 3 [/SUP], Summar Sohail[SUP] 4 [/SUP], Muhammad Sajid[SUP] 1 [/SUP], Sheikh Arslan Sehgal[SUP] 5 [/SUP]
Affiliations
- PMID: 35324925
- DOI: 10.1371/journal.pone.0264700
Abstract
Coronaviruses (CoVs) are positive-stranded RNA viruses with short clubs on their edges. CoVs are pathogenic viruses that infect several animals and plant organisms, as well as humans (lethal respiratory dysfunctions). A noval strain of CoV has been reported and named as SARS-CoV-2. Numerous COVID-19 cases were being reported all over the World. COVID-19 and has a high mortality rate. In the present study, immunoinformatics techniques were utilized to predict the antigenic epitopes against 3C like protein. B-cell epitopes and Cytotoxic T-lymphocyte (CTL) were designed computationally against SARS-CoV-2. Multiple Sequence Alignment (MSA) of seven complete strains (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2) was performed to elucidate the binding domain and interacting residues. MHC-I binding epitopes were evaluated by analyzing the binding affinity of the top-ranked peptides having HLA molecule. By utilizing the docked complexes of CTL epitopes with antigenic sites, the binding relationship and affinity of top-ranked predicted peptides with the MHC-I HLA protein were investigated. The molecular docking analyses were conducted on the ZINC database library and twelve compounds having least binding energy were scrutinized. In conclusion, twelve CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, SEDMLNPNY, LSQTGIAV, VLDMCASLK, LTQDHVDIL, TTLNDFNLV, CTSEDMLNP, TTITVNVLA, YNGSPSGVY, and SMQNCVLKL) were identified against SARS-CoV-2.