<i>In silico</i> approach to design a multi-epitopic vaccine candidate targeting the non-mutational immunogenic regions in envelope protein and surface glycoprotein of SARS-CoV-2

Ibrahim

Journal of Infection and Public Health

et al. 2022

Objectives To clarify the work done by using AI for identifying the genomic sequences, development of drugs and vaccines for COVID-19 and to recognize the advantages and challenges of using such technology. Methods A non-systematic review was done. All articles published on Pub-Med, Medline, Google, and Google Scholar on AI or digital health regarding genomic sequencing, drug development, and vaccines of COVID-19 were scrutinized and summarized. Results The sequence of SARS- CoV-2 was identified with the help of AI. It can help also in the prompt identification of variants of concern (VOC) as delta strains and Omicron. Furthermore, there are many drugs applied with the help of AI. These drugs included Atazanavir, Remdesivir, Efavirenz, Ritonavir, and Dolutegravir, PARP1 inhibitors (Olaparib and CVL218 which is Mefuparib hydrochloride), Abacavir, Roflumilast, Almitrine, and Mesylate. Many vaccines were developed utilizing the new technology of bioinformatics, databases, immune-informatics, machine learning, and reverse vaccinology to the whole SARS-CoV-2 proteomes or the structural proteins. Examples of these vaccines are the messenger RNA and viral vector vaccines. AI provides cost-saving and agility. However, the challenges of its usage are the difficulty of collecting data, the internal and external validation, ethical consideration, therapeutic effect, and the time needed for clinical trials after drug approval. Moreover, there is a common problem in the deep learning (DL) model which is the shortage of interpretability. Conclusion The growth of AI techniques in health care opened a broad gate for discovering the genomic sequences of the COVID-19 virus and the VOC. AI helps also in the development of vaccines and drugs (including drug repurposing) to obtain potential preventive and therapeutic agents) for controlling the COVID-19 pandemic.

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery

Ibrahim

Journal of Infection and Public Health

et al. 2022

Veterinary Medicine & Sci

“…In this study, the MW of the vaccine construct and the theoretical pI signifies the basic nature of the vaccine construct. The aliphatic index and low GRAVY score indicate the thermostable and hydrophilic nature of the vaccine candidate (Abdi et al., 2022 ; Susithra Priyadarshni et al., 2022 ; Validi et al., 2018 ). Moreover, the GRAVY value 0.008 of the vaccine suggesting that the vaccine may interact with water (Abdi et al., 2022 ), and this vaccine is ideal for its usage in endemic areas (Bachmann & Jennings, 2010 ; Omoniyi et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Development of membrane protein‐based vaccine against lumpy skin disease virus (LSDV) using immunoinformatic tools

Salauddin,

Kayesh,

Ahammed

et al. 2024

IntroductionLumpy skin disease, an economically significant bovine illness, is now found in previously unheard‐of geographic regions. Vaccination is one of the most important ways to stop its further spread.AimTherefore, in this study, we applied advanced immunoinformatics approaches to design and develop an effective lumpy skin disease virus (LSDV) vaccine.MethodsThe membrane glycoprotein was selected for prediction of the different B‐ and T‐cell epitopes by using the immune epitope database. The selected B‐ and T‐cell epitopes were combined with the appropriate linkers and adjuvant resulted in a vaccine chimera construct. Bioinformatics tools were used to predict, refine and validate the 2D, 3D structures and for molecular docking with toll‐like receptor 4 using different servers. The constructed vaccine candidate was further processed on the basis of antigenicity, allergenicity, solubility, different physiochemical properties and molecular docking scores.ResultsThe in silico immune simulation induced significant response for immune cells. In silico cloning and codon optimization were performed to express the vaccine candidate in Escherichia coli. This study highlights a good signal for the design of a peptide‐based LSDV vaccine.ConclusionThus, the present findings may indicate that the engineered multi‐epitope vaccine is structurally stable and can induce a strong immune response, which should help in developing an effective vaccine towards controlling LSDV infection.

“…There have been several reports employing immunoinformatics to identify T-cell epitopes and formulate a vaccine for SARS-CoV-2 infection. PubMed search using keywords 'peptide-based vaccine', 'SARS-CoV-2', and 'immunoinformatics' conducted on 14 October 2021 resulted in 15 research articles [94][95][96][97][98][99][100][101][102][103][104][105][106][107][108]. In this study, we made a prediction based on the HLA alleles that are present with at least 5% frequency in the Indonesian population.…”

Section: Discussionmentioning

confidence: 99%

Immunoinformatics Analysis of SARS-CoV-2 ORF1ab Polyproteins to Identify Promiscuous and Highly Conserved T-Cell Epitopes to Formulate Vaccine for Indonesia and the World Population

et al. 2021

SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon’s entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.