Designing of a Novel Candidate Multi-epitope Vaccine to boost Immune
Responses against SARS‐COV‐2 using Immunoinformatics and Machine
Learning based Approach
Authors: Mohammadi Shiva , Pour Soudabe Kavusi , Jalili Sajad , Barazesh Mahdi 
Published: September 2022

Background:
The fast development of an effective vaccine is the major demand for protection
against the SARS-COV-2 virus outbreak. With the vast volume of data and the requirement for automatic
abstract property learning, machine learning (ML) as a branch of artificial intelligence (AI) has a significant
contribution in areas of vaccine discovery. The rise of ML has greatly accelerated the often lengthy
vaccine approval process. ML models for COVID-19 vaccine development focus on the prediction of
potential epitopes by using a variety of techniques, such as artificial neural networks, gradient boosting
decision trees and deep neural networks.

:
In this regard, immuno-informatics tools are time-saving and cost-effective methods to hasten the design
and establishment of a proficient multi-peptide candidate vaccine. The utilization of multi-epitope-based
vaccines has been demonstrated to be a promising immunization approach against viruses due to the induction
of long-term protective immunity.

Methods:
In the present study, a comprehensive computational and machine learning based approach was
conducted to design a multi-epitope-based potential candidate vaccine composed of cytotoxic T lymphocyte
(CTL) and helper T lymphocyte (HTL) epitopes of conserved regions of Spike and Nucleocapsid
proteins. The potential viral peptides as the candidate vaccine were screened regarding convenient features
like hydrophilicity, flexibility, antigenicity, and charging properties. In the next step, the vaccine
efficacy needs to be improved by an immune adjuvant. For this purpose, the C-terminal domain of the
heat shock protein gp96 (CT-GP96) was applied as a potent adjuvant for enhancing immunity. The five
assembled constructs with different peptide orders were generated and fused with the assistance of suitable
linkers. All five assembled candidate vaccine constructs were modeled and their 3D structures were
assessed in terms of strong immune responses stimulation and their structural stability and immune processing
for response induction. Finally, the best refined model was docked to toll-like receptor 4 (TLR4).
Furthermore, Molecular Dynamics (MD) simulation of the vaccine-receptor complex was done to assess
the stability and related physical movements of the vaccine-TLR4 docking complex. The final candidate
vaccine was computationally cloned in E. coli expression host to guarantee its high level of production.

Results:
Following a comprehensive immune-informatics and machine learning-based approach, the best
conserved CTL and HTL immune stimulant epitopes were selected and assembled in different orders to
build five different constructs. The final validated candidate vaccine construct was selected according to
its efficacy, stability, and exposure ability, molecular docking analysis with TLR4. The molecular simulations
by iMODS software also confirmed the stability of the binding interface. Additionally, the computational
cloning of the final assembled candidate vaccine with pET28a plasmid showed the possibility of
high level production of the vaccine construct post transformation in an E. coli host.

Conclusion:
The computational analysis indicated that this construct can be proposed as a potent prophylactic
and therapeutic candidate multi-epitope vaccine against SARS-COV-2 once its effectiveness is
verified by experimental and animal studies.