Linear B-cell epitope prediction for <i>in silico</i> vaccine design: a performance review of methods available via command-line interface

Galanis, Kosmas A.; Nastou, Katerina C.; Papandreou, Nikos C.; Petichakis, Georgios N.; Pigis, Diomidis G.; Iconomidou, Vassiliki A.

doi:10.1101/833418

Cited by 20 publications

(23 citation statements)

References 65 publications

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“…Admittedly, bioinformatics prediction of B cell epitopes is still challenging ( Galanis et al, 2019 ). However, the Bepipred 2.0 software that we used is among the best such software.…”

Section: Discussionmentioning

confidence: 99%

The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS-CoV-2

Zhu

Kim

Xiao

et al. 2020

Preprint

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The outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths. A systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking. In this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes. We created an online database to broadly share the predictions as a resource for the research community. Using this resource, we showed that genetic variations in SARS-CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus. Importantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research.

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“…Admittedly, bioinformatics prediction of B cell epitopes is still challenging ( Galanis et al, 2019 ). However, the Bepipred 2.0 software that we used is among the best such software.…”

Section: Discussionmentioning

confidence: 99%

The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS-CoV-2

Zhu

Kim

Xiao

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Epitope data obtained from crystal structures are considered as a higher quality that significantly enhanced the predictive power. This method performs better than other available methods for sequence-based epitope prediction both on epitope data derived from threedimensional structures and on a wide set of linear epitopes downloaded from the IEDB database, making it a reliable and effective tool for predicting linear B-cell epitopes (Sanchez-Trincado et al, 2017;Galanis et al, 2019, Galanis et al, 2021. The best performance B-cell epitope predictor is BepiPred as it achieved the highest Matthews correlation coefficient value that measures the overall accuracy of the prediction (Galanis et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…This method performs better than other available methods for sequence-based epitope prediction both on epitope data derived from threedimensional structures and on a wide set of linear epitopes downloaded from the IEDB database, making it a reliable and effective tool for predicting linear B-cell epitopes (Sanchez-Trincado et al, 2017;Galanis et al, 2019, Galanis et al, 2021. The best performance B-cell epitope predictor is BepiPred as it achieved the highest Matthews correlation coefficient value that measures the overall accuracy of the prediction (Galanis et al, 2019). A highly antigenic B-cell epitope proven to be serologically reactive during natural infection was discovered within the PvAMA1 vaccine candidate using a BepiPred server (Bueno et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics characterization of Plasmodium knowlesi apical membrane antigen 1 (PkAMA1) for multi-epitope vaccine design

et al. 2021

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Malaria caused by Plasmodium knowlesi species has become a public health concern, especially in Malaysia. Plasmodium knowlesi parasite which originates from the macaque species, infects human through the bite of the Anopheles mosquitoes. Research on malaria vaccine has been a continuous effort to eradicate the malaria infection, yet there is no vaccine against P. knowlesi malaria to date. Apical membrane antigen 1 (AMA1) is a unique surface protein of all apicomplexan parasites that plays a crucial role in parasite-host cell invasion and thus has been a long-standing malaria vaccine candidate. The selection of protective epitopes in silico has led to significant advances in the design of the vaccine. The present study aimed to employ bioinformatics tools to predict the potential immunogenic B-and T-cell epitopes in designing malaria vaccine targeting P. knowlesi AMA1 (PkAMA1). B-cell epitopes were predicted using four bioinformatics tools, i.e., BepiPred, ABCpred, BcePred, and IEDB servers whereas T-cell epitopes were predicted using two bioinformatics servers, i.e., NetMHCpan-4.1 and NetMHCIIpan-4.0 targeting human major histocompatibility complex (MHC) class I and class II molecules, respectively. The antigenicity of the selected epitopes computed by both B-and T-cell predictors were further analyzed using the VaxiJen server. The results demonstrated that PkAMA1 protein encompasses multi antigenic regions that have the potential for the development of multi-epitope vaccine. Two B-and T-cell epitopes consensus regions, i.e., NSGIRIDLGEDAEVGNSKYRIPAGKCP (codons 28-54) and KTHAASFVIAEDQNTSY RHPAVYDEKNKT (codons 122-150) at domain I (DI) of PkAMA1 were reported. Advancement of bioinformatics in characterization of the target protein may facilitate vaccine development especially in vaccine design which is costly and cumbersome process. Thus, comprehensive B-cell and T-cell epitope prediction of PkAMA1 offers a promising pipeline for the development and design of multi-epitope vaccine against P. knowlesi.

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“…Although protein exposure on the bacterial surface is a requirement, it is not sufficient for ascertaining antigenicity. Prediction of B-cell epitopes, especially with well-defined limits, is extremely challenging (56). Nevertheless, exposed regions showing particular physicochemical properties are indicative of antibody recognition.…”

Section: Antigenicity Of the Exposed Proteomementioning

confidence: 99%

Designing Multi-Antigen Vaccines Against Acinetobacter baumannii Using Systemic Approaches

McConnell

Martín-Galiano

2021

Front. Immunol.

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Vaccines and monoclonal antibodies are promising approaches for preventing and treating infections caused by multidrug resistant Acinetobacter baumannii. However, only partial protection has been achieved with many previously tested protein antigens, which suggests that vaccines incorporating multiple antigens may be necessary in order to obtain high levels of protection. Several aspects that use the wealth of omic data available for A. baumannii have not been fully exploited for antigen identification. In this study, the use of fractionated proteomic and computational data from ~4,200 genomes increased the number of proteins potentially accessible to the humoral response to 8,824 non-redundant proteins in the A. baumannii panproteome. Among them, 59% carried predicted B-cell epitopes and T-cell epitopes recognized by two or more alleles of the HLA class II DP supertype. Potential cross-reactivity with human proteins was detected for 8.9% of antigens at the protein level and 2.7% at the B-cell epitope level. Individual antigens were associated with different infection types by genomic, transcriptomic or functional analyses. High intra-clonal genome density permitted the identification of international clone II as a “vaccitype”, in which 20% of identified antigens were specific to this clone. Network-based centrality measurements were used to identify multiple immunologic nodes. Data were formatted, unified and stored in a data warehouse database, which was subsequently used to identify synergistic antigen combinations for different vaccination strategies. This study supports the idea that integration of multi-omic data and fundamental knowledge of the pathobiology of drug-resistant bacteria can facilitate the development of effective multi-antigen vaccines against these challenging infections.

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Linear B-cell epitope prediction for in silico vaccine design: a performance review of methods available via command-line interface

Cited by 20 publications

References 65 publications

The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS-CoV-2

The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS-CoV-2

Bioinformatics characterization of Plasmodium knowlesi apical membrane antigen 1 (PkAMA1) for multi-epitope vaccine design

Designing Multi-Antigen Vaccines Against Acinetobacter baumannii Using Systemic Approaches

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