PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

Naz, Kanwal; Naz, Anam; Ashraf, Shifa Tariq; Rizwan, Muhammad; Ahmad, Jamil; Baumbach, Jan; Ali, Amjad

doi:10.1186/s12859-019-2713-9

Cited by 90 publications

(60 citation statements)

References 67 publications

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“…Until now, very few studies characterizing vaccine candidates had taken pangenomic features into consideration. The majority of bioinformatics pipelines were developed to investigate single genomes, or a very limited number of strains, and so far a single pipeline has been published for pangenomic RV analysis (18).…”

Section: Discussionmentioning

confidence: 99%

“…While these analyses provided the complete decision-tree performed, none of them provided the in-house scripts used for integration of all tools employed in their approaches. To our knowledge, the only tool available for pangenomic analysis and RV prediction is PanRV (18), which employs routinely used membrane and subcellular localization predictions. It also combines additional filters to enrich possible vaccine candidates such as gene essentiality and/or virulence factor predictions.…”

Section: Introductionmentioning

confidence: 99%

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EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains

2020

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In reverse vaccinology approaches, complete proteomes of bacteria are submitted to multiple computational prediction steps in order to filter proteins that are possible vaccine candidates. Most available tools perform such analysis only in a single strain, or a very limited number of strains. But the vast amount of genomic data had shown that most bacteria contain pangenomes, i.e., their genomic information contains core, conserved genes, and random accessory genes specific to each strain. Therefore, in reverse vaccinology methods it is of the utmost importance to define core proteins and core epitopes. EpitoCore is a decision-tree pipeline developed to fulfill that need. It provides surfaceome prediction of proteins from related strains, defines core proteins within those, calculate their immunogenicity, predicts epitopes for a given set of MHC alleles defined by the user, and then reports if epitopes are located extracellularly and if they are conserved among the core homologs. Pipeline performance is illustrated by mining peptide vaccine candidates in Mycobacterium avium hominissuis strains. From a total proteome of ∼4,800 proteins per strain, EpitoCore predicted 103 highly immunogenic core homologs located at cell surface, many of those related to virulence and drug resistance. Conserved epitopes identified among these homologs allows the users to define sets of peptides with potential to immunize the largest coverage of tested HLA alleles using peptide-based vaccines. Therefore, EpitoCore is able to provide automated identification of conserved epitopes in bacterial pangenomic datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains

2020

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“…Experimentally screening large genomes to find out the antigens of vaccine interest would be highly time-consuming and costly. 22 In this regards, implementation of vaccinology 3.0 and its related immunoinformatics methodologies to in silico pre-screening of the pathogens genomes would guide the selection of those antigens with higher interest. 22 Such tools are also very valuable if the pathogen in question is isolated and grown with difficulty.…”

Section: Next Generation Vaccinology For Ntdsmentioning

confidence: 99%

“… 22 In this regards, implementation of vaccinology 3.0 and its related immunoinformatics methodologies to in silico pre-screening of the pathogens genomes would guide the selection of those antigens with higher interest. 22 Such tools are also very valuable if the pathogen in question is isolated and grown with difficulty. In addition, in silico-based approaches may permit seeking “pan-genome” protective antigens, exploring vaccine candidates that can immunize against the different pathogenic strains or genetic variants of the pathogen.…”

Section: Next Generation Vaccinology For Ntdsmentioning

confidence: 99%

An Overview of Current Uses and Future Opportunities for Computer-Assisted Design of Vaccines for Neglected Tropical Diseases

Robleda-Castillo¹,

Ros-Lucas²,

Martínez-Peinado³

et al. 2021

AABC

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Neglected tropical diseases are infectious diseases that impose high morbidity and mortality rates over 1.5 billion people worldwide. Originally restricted to tropical and subtropical regions, changing climate conditions have increased their potential to emerge elsewhere. Control of their impact suffers from shortages like poor epidemiological surveillance or irregular drug distribution, and some NTDs still lack of appropriate diagnostics and/or efficient therapeutics. For these, availability of vaccines to prevent new infections, or the worsening of those already established, would mean a major breakthrough. However, only dengue and rabies count with approved vaccines at present. Herein, we review the state-of-the-art of vaccination strategies for NTDs, setting the focus on third generation vaccines and the concept of reverse vaccinology. Its capability to address pathogens´ biological complexity, likely contributing to save developmental costs is discussed. The use of computational tools is a fundamental aid to analyze increasingly large datasets aimed at designing vaccine candidates with the highest, possibly, opportunities to succeed. Ultimately, we identify and analyze those studies that took an in silico approach to find vaccine candidates, and experimentally assessed their immunogenicity and/or protection capabilities.

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“…Over the past decade, artificial intelligence (AI)-based models have revolutionized drug discovery in general (Zhong et al, 2018;Duan et al, 2019;Lavecchia, 2019). AI has also led to the creation of many RV virtual frameworks, which are generally classified as rule-based filtering models (Naz et al, 2019;Ong et al, 2020a). Machine learning (ML) enables the creation of models that learn and generalize the patterns within the available data and can make inferences from previously unseen data.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

Arshadi

Webb

Salem

et al. 2020

Front. Artif. Intell.

176

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SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

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PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

Cited by 90 publications

References 67 publications

EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains

EpitoCore: Mining Conserved Epitope Vaccine Candidates in the Core Proteome of Multiple Bacteria Strains

An Overview of Current Uses and Future Opportunities for Computer-Assisted Design of Vaccines for Neglected Tropical Diseases

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

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