An introduction to epitope prediction methods and software

Yang, Xing-Dong; Yu, Xiaohong

doi:10.1002/rmv.602

Cited by 172 publications

(126 citation statements)

References 134 publications

(60 reference statements)

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“…47,48 In the past years new prediction tools have been developed, some of them more accurate regarding, for example, variant epitope length than others. [49][50][51][52] For the individual researcher, however, it is difficult to choose the most suitable method.…”

Section: Discussionmentioning

confidence: 99%

Comparison of experimental fine‐mapping to in silico prediction results of HIV‐1 epitopes reveals ongoing need for mapping experiments

et al. 2014

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SummaryMethods for identifying physiologically relevant CD8 T-cell epitopes are critically important not only for the development of T-cell-based vaccines but also for understanding host-pathogen interactions. As experimentally mapping an optimal CD8 T-cell epitope is a tedious procedure, many bioinformatic tools have been developed that predict which peptides bind to a given MHC molecule. We assessed the ability of the CD8 T-cell epitope prediction tools SYFPEITHI, CTLPRED and IEDB to foretell nine experimentally mapped optimal HIV-specific epitopes. Randomly -for any of the subjects' HLA type and with any matching score -the optimal epitope was predicted in seven of nine epitopes using SYFPEITHI, in three of nine epitopes using CTLPRED and in all nine of nine epitopes using IEDB. The optimal epitope within the three highest ranks was given in four of nine epitopes applying SYFPEITHI, in two of nine epitopes applying CTLPRED and in seven of nine epitopes applying IEDB when screening for all of the subjects' HLA types. Knowing the HLA restriction of the peptide of interest improved the ranking of the optimal epitope within the predicted results. Epitopes restricted by common HLA alleles were more likely to be predicted than those restricted by uncommon HLA alleles. Epitopes with aberrant lengths compared with the usual HLA-class I nonamers were most likely not predicted. Application of epitope prediction tools together with literature searches for already described optimal epitopes narrows down the possibilities of optimal epitopes within a screening peptide of interest. However, in our opinion, the actual fine-mapping of a CD8 T-cell epitope cannot yet be replaced.

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Section: Discussionmentioning

confidence: 99%

Comparison of experimental fine‐mapping to in silico prediction results of HIV‐1 epitopes reveals ongoing need for mapping experiments

et al. 2014

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“…Known as antigenic determinants, it is important to identify the epitopes to notarize the type and intensity of the immune response, and to serve for vaccine design, disease prevention, diagnosis and treatments [14][15][16]. Thus, a large number of rapid, fairly accurate and cost effective in silico algorithms are being developed with an enormous expansion in protein structures [17].…”

Section: Introductionmentioning

confidence: 99%

In Silico Identification and in Vitro Analysis of B and T-Cell Epitopes of the Black Turtle Bean (Phaseolus Vulgaris L.) Lectin

Zhao

Elfalleh

et al. 2018

Cell Physiol Biochem

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Background/Aims: The incidence of lectin allergic disease is increasing in recent decades, and definitive treatment is still lacking. Identification of B and T-cell epitopes of allergen will be useful in understanding the allergen antibody responses as well as aiding in the development of new diagnostics and therapy regimens for lectin poisoning. In the current study, we mainly addressed these questions. Methods: Three-dimensional structure of the lectin from black turtle bean (Phaseolus vulgaris L.) was modeled using the structural template of Phytohemagglutinin from P. vulgaris (PHA-E, PDB ID: 3wcs.1.A) with high identity. The B and T-cell epitopes were screened and identified by immunoinformatics and subsequently validated by ELISA, lymphocyte proliferation and cytokine profile analyses. Results: Seven potential B-cell epitopes (B1 to B7) were identified by sequence and structure based methods, while three T-cell epitopes (T1 to T3) were identified by the predictions of binding score and inhibitory concentration. The epitope peptides were synthesized. Significant IgE binding capability was found in B-cell epitopes (B2, B5, B6 and B7) and T2 (a cryptic B-cell epitope). T1 and T2 induced significant lymphoproliferation, and the release of IL-4 and IL-5 cytokine confirmed the validity of T-cell epitope prediction. Abundant hydrophobic amino acids were found in B-cell epitope and T-cell epitope regions by amino acid analysis. Positively charged amino acids, such as His residue, might be more favored for B-cell epitope. Conclusion: The present approach can be applied for the identification of epitopes in novel allergen proteins and thus for designing diagnostics and therapies in lectin allergy.

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“…Based on the information (data) utilized for performing prediction, the methodologies can be grouped as sequence-based or structure-based approaches. Many sequence-based linear epitope prediction methods for B cells have been developed and used since long time and majority of them are propensity scale and machine learning-based methods [109,110]. Some of the major tools/servers that deal with the prediction of linear Bcell epitopes are listed in Table 3.…”

Section: Computational Prediction Of Allergen Epitopesmentioning

confidence: 99%

Databases and Algorithms in Allergen Informatics

Kadam¹,

Sawant²,

Jayaraman³

et al. 2016

Bioinformatics - Updated Features and Applications

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Allergic diseases are considered as one of the major health problems worldwide due to their increasing prevalence. Advancements in genomic, proteomic, and analytical techniques have resulted in considerable progress in the field of allergology, which has led to accumulation of huge amount of data. Allergen bioinformatics comprises allergenrelated data resources and computational methods/tools, which deal with an efficient archival, management, and analysis of allergological data. Significant work has been done in the area of allergen bioinformatics that has proven pivotal for the development and progress of this field. In this chapter, we describe the current status of databases and algorithms, encompassing the field of allergen bioinformatics by examining work carried out thus far with respect to features such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment. This chapter illustrates concepts and algorithms in allergen bioinformatics, as well as it outlines the key areas for potential development in allergology field.

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An introduction to epitope prediction methods and software

Cited by 172 publications

References 134 publications

Comparison of experimental fine‐mapping to in silico prediction results of HIV‐1 epitopes reveals ongoing need for mapping experiments

Comparison of experimental fine‐mapping to in silico prediction results of HIV‐1 epitopes reveals ongoing need for mapping experiments

In Silico Identification and in Vitro Analysis of B and T-Cell Epitopes of the Black Turtle Bean (Phaseolus Vulgaris L.) Lectin

Databases and Algorithms in Allergen Informatics

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