| Literature DB >> 33343349 |
Concetta Di Natale1,2,3, Sara La Manna1, Ilaria De Benedictis1, Paola Brandi4, Daniela Marasco1.
Abstract
At the end of December 2019, an epidemic form of respiratory tract infection now named COVID-19 emerged in Wuhan, China. It is caused by a newly identified viral pathogen, the severe acute respiratory syndrome coronavirus (SARS-CoV-2), which can cause severe pneumonia and acute respiratory distress syndrome. On January 30, 2020, due to the rapid spread of infection, COVID-19 was declared as a global health emergency by the World Health Organization. Coronaviruses are enveloped RNA viruses belonging to the family of Coronaviridae, which are able to infect birds, humans and other mammals. The majority of human coronavirus infections are mild although already in 2003 and in 2012, the epidemics of SARS-CoV and Middle East Respiratory Syndrome coronavirus (MERS-CoV), respectively, were characterized by a high mortality rate. In this regard, many efforts have been made to develop therapeutic strategies against human CoV infections but, unfortunately, drug candidates have shown efficacy only into in vitro studies, limiting their use against COVID-19 infection. Actually, no treatment has been approved in humans against SARS-CoV-2, and therefore there is an urgent need of a suitable vaccine to tackle this health issue. However, the puzzled scenario of biological features of the virus and its interaction with human immune response, represent a challenge for vaccine development. As expected, in hundreds of research laboratories there is a running out of breath to explore different strategies to obtain a safe and quickly spreadable vaccine; and among others, the peptide-based approach represents a turning point as peptides have demonstrated unique features of selectivity and specificity toward specific targets. Peptide-based vaccines imply the identification of different epitopes both on human cells and virus capsid and the design of peptide/peptidomimetics able to counteract the primary host-pathogen interaction, in order to induce a specific host immune response. SARS-CoV-2 immunogenic regions are mainly distributed, as well as for other coronaviruses, across structural areas such as spike, envelope, membrane or nucleocapsid proteins. Herein, we aim to highlight the molecular basis of the infection and recent peptide-based vaccines strategies to fight the COVID-19 pandemic including their delivery systems.Entities:
Keywords: COVID-19; SARS-Cov-2; clinical trials; peptide on market; peptide-based vaccine
Year: 2020 PMID: 33343349 PMCID: PMC7744882 DOI: 10.3389/fphar.2020.578382
Source DB: PubMed Journal: Front Pharmacol ISSN: 1663-9812 Impact factor: 5.810
FIGURE 1(A) Schematic representation of modular structure of SARS-CoV S-protein and its furin cleavage sites indicated by arrows. SP = signal peptide. (B) S1 cleavage furin site on human coronaviruses, the novel sites in SARS-CoV-2 is underlined in red while the furin cleavage on MERS is italicized. (C) Typical furin cleavage site.
Canonical alpha and beta HCoVs furin cleavage motif, ↓ indicates cut site of the protease.
| Alpha HCoVs | Beta HCoVs | S1/S2, site 1 | S1/S2, site 1 | S2′ |
|---|---|---|---|---|
| HCoV-229E | — | IAVQPR↓NVSYD | — | SRVAGR↓SA |
| HCoVNL63 | — | IPVRPR↓NSSDN | — | SRIAGR↓SA |
| — | HCoV-HKU1 | SRRKRR↓SISA | — | CGSSSR↓SF |
| — | HCoV-OC43 | KNRRSR↓GAITT | — | SKASSR↓SA |
| — | MERS-CoV | TPRSCR↓SVPG | — | GSRSAR↓SA |
| — | SARS-CoV | TVSLLR↓STGQ | IAY↓TMS | LKPTKR↓SF |
| — | SARS-CoV-2 | SPRRAR↓SVAS | IAY↓TMS | SKPSKR↓SF |
FIGURE 2Schematic representation of main types of vaccine delivery systems. (A) VLP, (B) liposome-based particle, (C) polymer micro-/nano-particle, (D) MAP, different colors indicate peptide sequences that can be diverse; “OH” is a free/unblocked carboxyl group.
Worldwide clinical trials of peptide-based vaccines (source http://www.clinicaltrials.gov, website access 20/07/2020).
| Trial phase | Number of peptide-based vaccines | Clinical indications |
|---|---|---|
| I | 178 | Cancer, HIV infections, autoimmune diseases, arthritis, digestive system diseases, gonadal disorders, lung diseases, RNA virus infections, skin diseases, malaria, allergy, mycoses, influenza, hepatitis, hand, foot and mouth disease |
| II | 115 | Cancer, blood coagulation disorders, liver diseases, lung diseases, bone marrow diseases, endocrine system diseases, hepatitis, skin diseases |
| III | 7 | Cancer |
| IV | 0 | No peptide vaccine reached market yet |
FIGURE 3Schematic representation of main steps for the identification of B- and T- cell epitopes in silico.
Peptide vaccines in preclinical phase. Source: World Health Organization (WHO) website.
| Vaccine | Producer/s | Type of candidate vaccine |
|---|---|---|
| FlowVax COVID-19 | Flow pharma | Adjuvanted, microsphere peptide vaccine targeting SARS-CoV-2 N with a suite of 16 T-cell peptides |
| DPX-COVID-19 | IMV inc | Peptide antigens formulated in lipid nanoparticles (LNP) |
| Vaxil bio | Peptide | |
| VIDO-InterVac, university of Saskatchewan | Adjuvanted microsphere peptide | |
| OncoGen | Synthetic long peptide vaccine candidate for S and M proteins | |
| Ii-key peptide vaccine | Generex/EpiVax | T-cell epitopes + ii-key peptides |
| University of sao paulo | Vlp peptides | |
| Axon neuroscience | Peptides derived from spike protein | |
| Intravacc/Epivax | Outer membrane vesicle (OMV)-subunit | |
| VIDO-InterVac, university of Saskatchewan | Adjuvanted microsphere peptide | |
| Valo therapeutics ltd | Adenovirus-based + HLA-matched peptides | |
| FBRI SRC VB VECTOR, rospotrebnadzor, koltsovo | Peptide vaccine |
WHO, World Health Organization; LNP, lipid nanoparticles; OMV, outer membrane vesicle.
CD4+ T-cell epitopes, predicted by IEDB.
| Protein | Allele | Epitope |
|---|---|---|
| S (spike) | DRB1*1101 | GNYNYLYRLFRKSN |
| DRB1*1301 | IRAAEIRASANLAA | |
| DRB1*0301 | INLVRDLPQGFSAL | |
| M (membrane) | DRB1*1101 | SYFIASFRLFARTRS |
| DRB1*1301 | AVILRGHLRIAGHH | |
| DRB1*0301 | EITVATSRTLSYYK |
CD8+ T-cell epitopes, predicted by IEDB.
| Protein | Allele | Epitope | MHC IC50 (nM) | Overall score (IEDB) |
|---|---|---|---|---|
| S (spike) | A*0201 | YLQPRTFLL | 4.6 | 1.17 |
| KIADYNYKL | 15.9 | 0.99 | ||
| FQFCNDPFL | 8.9 | 0.99 | ||
| SIIAYTMSL | 15.3 | 0.82 | ||
| VLNDILSRL | 19.7 | 0.43 | ||
| A*0101 | LTDEMIAQY | 5.2 | 1.71 | |
| WTAGAAAYY | 40.1 | 0.88 | ||
| A*2402 | NYNYLYRLF | 23.4 | 1.01 | |
| QYIKWPWYI | 8.9 | 0.45 | ||
| B*3501 | IPFAMQMAY | 2.3 | 2.24 | |
| LPFNDGVYF | 3.5 | 1.90 | ||
| VASQSIIAY | 7.2 | 1.80 | ||
| FAMQMAYRF | 6.3 | 1.70 | ||
| LGAENSVAY | 11.5 | 1.66 | ||
| M (membrane) | A*0201 | GLMWLSYFI | 4.5 | 0.86 |
| FVLAAVYRI | 11 | 0.45 | ||
| KLLEQWNLV | 7.3 | 0.18 | ||
| A*0101 | ATSRTLSYY | 48.2 | 0.92 | |
| A*2402 | YFLASFRLF | 9.9 | 1.48 | |
| SYFLASFRL | 22.5 | 0.72 | ||
| B*3501 | YANRNRFLY | 6.8 | 1.66 | |
| VATSRTLSY | 23.9 | 1.27 | ||
| FAYANRNRF | 23.6 | 0.99 |
HLA allele frequencies in the Romanian population.
| HLA allele | Frequency (%) |
|---|---|
| HLA-A*02 | 29 |
| HLA-A*01 | 14.3 |
| HLA-A*24 | 11.2 |
| HLA-B*35 | 16 |
| HLA-B*18 | 11 |
| HLA-DRB1*11 | 18.5 |
| HLA-DRB1*03 | 11.3 |
| HLA-DRB1*13 | 10.5 |