Developing an Effective Peptide-Based Vaccine for COVID-19: Preliminary Studies in Mice Models

Yang, Haiqiang; Cao, Jessica; Lin, Xiaoyang; Yue, Jingwen; Zieneldien, Tarek; Kim, Janice; Wang, Lianchun; Fang, Jianming; Huang, Ruo‐Pan; Bai, Yun; Sneed, Kevin B.; Cao, Chuanhai

doi:10.3390/v14030449

Cited by 11 publications

(6 citation statements)

References 87 publications

(90 reference statements)

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“…While several types of vaccines have been developed to address the disease, the PBVs have not received as much attention. However, these vaccines have several potential benefits, including increased safety, immunogenicity, and cost-effectiveness ( Yang et al, 2022 ). Computational methods can be used to identify potential vaccine candidates more quickly and cost-effectively than traditional laboratory-based approaches ( Oluwagbemi et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

Bukhari,

Elshiekh,

Abbas

2024

PeerJ Computer Science

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Majority of the existing SARS-CoV-2 vaccines work by presenting the whole pathogen in the attenuated form to immune system to invoke an immune response. On the other hand, the concept of a peptide based vaccine (PBV) is based on the identification and chemical synthesis of only immunodominant peptides known as T-cell epitopes (TCEs) to induce a specific immune response against a particular pathogen. However PBVs have received less attention despite holding huge untapped potential for boosting vaccine safety and immunogenicity. To identify these TCEs for designing PBV, wet-lab experiments are difficult, expensive, and time-consuming. Machine learning (ML) techniques can accurately predict TCEs, saving time and cost for speedy vaccine development. This work proposes novel hybrid ML techniques based on the physicochemical properties of peptides to predict SARS-CoV-2 TCEs. The proposed hybrid ML technique was evaluated using various ML model evaluation metrics and demonstrated promising results. The hybrid technique of decision tree classifier with chi-squared feature weighting technique and forward search optimal feature searching algorithm has been identified as the best model with an accuracy of 98.19%. Furthermore, K-fold cross-validation (KFCV) was performed to ensure that the model is reliable and the results indicate that the hybrid random forest model performs consistently well in terms of accuracy with respect to other hybrid approaches. The predicted TCEs are highly likely to serve as promising vaccine targets, subject to evaluations both in-vivo and in-vitro. This development could potentially save countless lives globally, prevent future epidemic-scale outbreaks, and reduce the risk of mutation escape.

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Section: Discussionmentioning

confidence: 99%

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

Bukhari,

Elshiekh,

Abbas

2024

PeerJ Computer Science

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“…Hence, subunit/peptide-based vaccines are developed in which a small portion of pathogen protein is utilized in order to generate the vaccine candidates against [24,[51][52][53][54][55][56]. While designing a subunit-vaccines it is necessary to check the peptide/antigens have the capacity to effectively stimulate the immune system and generate specific type of cytokines to send signal to the immune system to perform inflammatory responses against the pathogens and cancerous In the current study, we have used experimentally validated IFN-γ inducing and non-inducing peptides from IEDB resource.…”

Section: Discussionmentioning

confidence: 99%

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

Dhall

Patiyal

Raghava

2023

Preprint

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A host-specific technique has been developed for annotating interferon-gamma (IFN-γ) inducing peptides, it is an updated version of IFNepitope. In this study, dataset used for developing prediction method contain experimentally validated 25492 and 7983 IFN-γ inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. In order to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST and achieved AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-γ inducing peptides, thus server/software have been developed (https://webs.iiitd.edu.in/raghava/ifnepitope2/).

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“…All examined mAbs were shown to bind to peptide #37 spanning the amino acids 444-458 (Figure 4B), located within the RBM [85]. It has been shown that this region includes human T cell epitopes with a capacity to induce T cell response in mice [207]. Following the identification of the immundominant epitope (mimotope #37, KVGGNYNYLYRLFRK), a vaccine compound consisting of the synthesized peptide conjugated to CRM197 (P#37-CRM197) was used in a prime-boost immunization experiment.…”

Section: B Cell Peptide/mimotope-based Vaccinementioning

confidence: 99%

Why Should Vaccines against Respiratory Diseases Go Mucosal? B Cell Epitope-Based Vaccines against SARS-CoV-2

Tobias,

Steinberger,

Wilkinson

et al. 2024

Preprint

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Immunity against respiratory pathogens is often short-term, and consequently there is an unmet need for effective prevention of such infections. One such infectious disease is COVID-19, which is caused by the novel Beta coronavirus SARS-CoV-2 that emerged around the end of 2019. The World Health Organization declared the illness a pandemic on March 11, 2020, and since then it has killed or sickened millions of people globally. The development of COVID-19 systemic vaccines, which impressively led to a significant reduction in disease severity, hospitalization, and mortality, con-tained the pandemic's expansion. However, these vaccines have not been able to stop the virus from spreading because of the restricted development of mucosal immunity. As a result, breakthrough infections have frequently occurred and new strains of the virus have been emerging. Furthermore, SARS-CoV-2 will likely continue to circulate and, like the influenza virus, co-exist with humans. The upper respiratory tract and nasal cavity are the primary sites of SARS-CoV-2 infection and thus, a mucosal/nasal vaccination to induce a mucosal response and stop the virus transmission is warrant-ed. In this review, we present the status of the systemic vaccines, of the approved mucosal vaccines and those under evaluation in clinical trials. Furthermore, we present our approach of a B-cell pep-tide-based vaccination applied by prime-boost schedule for eliciting both systemic and mucosal immunity.

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Developing an Effective Peptide-Based Vaccine for COVID-19: Preliminary Studies in Mice Models

Cited by 11 publications

References 87 publications

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

Physicochemical properties-based hybrid machine learning technique for the prediction of SARS-CoV-2 T-cell epitopes as vaccine targets

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

Why Should Vaccines against Respiratory Diseases Go Mucosal? B Cell Epitope-Based Vaccines against SARS-CoV-2

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