A Putative Prophylactic Solution for COVID-19: Development of Novel Multiepitope Vaccine Candidate against SARS-COV-2 by Comprehensive Immunoinformatic and Molecular Modelling Approach.

Hafiz Muzzammel Rehman,Huda Ahmed Alghamdi,Roquyya Gul,Matheus Froeyen,Muhammad Shahbaz Aslam,Muhammad Usman Mirza,Muhammad Sajjad,Mian Azhar Ahmad,Mahjabeen Saleem,Sarfraz Ahmad,Munir Ahmad Bhinder

doi:10.3390/biology9090296

Abstract

The outbreak of 2019-novel coronavirus (SARS-CoV-2) that causes severe respiratory infection (COVID-19) has spread in China, and the World Health Organization has declared it a pandemic. However, no approved drug or vaccines are available, and treatment is mainly supportive and through a few repurposed drugs. The urgency of the situation requires the development of SARS-CoV-2-based vaccines. Immunoinformatic and molecular modelling are time-efficient methods that are generally used to accelerate the discovery and design of the candidate peptides for vaccine development. In recent years, the use of multiepitope vaccines has proved to be a promising immunization strategy against viruses and pathogens, thus inducing more comprehensive protective immunity. The current study demonstrated a comprehensive in silico strategy to design stable multiepitope vaccine construct (MVC) from B-cell and T-cell epitopes of essential SARS-CoV-2 proteins with the help of adjuvants and linkers. The integrated molecular dynamics simulations analysis revealed the stability of MVC and its interaction with human Toll-like receptors (TLRs), which trigger an innate and adaptive immune response. Later, the in silico cloning in a known pET28a vector system also estimated the possibility of MVC expression in Escherichia coli. Despite that this study lacks validation of this vaccine construct in terms of its efficacy, the current integrated strategy encompasses the initial multiple epitope vaccine design concepts. After validation, this MVC can be present as a better prophylactic solution against COVID-19.

Highlights

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an enveloped, non-segmented positive-sense RNA virus, causes severe respiratory infection [1]
Docking and immuno-informatics method are helpful for the prediction of the binding interaction between Toll-like receptors (TLRs) and ligand complexes, and analysis was done as these are proven useful tools in identifying novel multiepitope vaccines [23,32]
Out of 932 amino acids, 37 antigenic peptides were predicted in SARS-CoV-2 Nsp12 polymerase

Summary

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an enveloped, non-segmented positive-sense RNA virus, causes severe respiratory infection [1]. SARS-CoV-2 is spherical, having a diameter of about 125 nm, and its genome (~30 kb) contains at least six open reading frames, which encode 16 non-structural proteins and 4 major structural proteins, namely, a spike protein (S), a form of glycoprotein; a membrane protein (M), which consists of the membrane; an envelope protein (E); and a nucleocapsid (N) protein, encoded by the ORFs near the 30 end of the genome Among these structural proteins, the spike (S) glycoprotein binds to the cellular receptor angiotensin-converting enzyme 2 (ACE2), and is responsible for causing the viral infection [9]. Most of the non-structural proteins play an essential role in viral replication, mainly SARS-CoV-2 main protease (Mpro), known as chymotrypsin-like protease (3CLpro) [15,16], Nsp helicase [17], and the Nsp RNA-dependent RNA polymerase [18]

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Conclusion