Benchmarking B cell epitope prediction: Underperformance of existing methods

Blythe, Martin; Flower, Darren R.

doi:10.1110/ps.041059505

Cited by 281 publications

(204 citation statements)

References 11 publications

(19 reference statements)

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“…This result is consistent with the results of Blythe and Flower's study on a smaller dataset of 50 proteins [5]. Perhaps more interesting is the finding that none of the three Naive Bayes classifiers offer improvements over the propensity scale based methods.…”

Section: Resultssupporting

confidence: 90%

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Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information

EL-Manzalawy

Dobbs

Honavar

2008

2008 IEEE International Conference on Bioinformatics and Biomedicine

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Section: Resultssupporting

confidence: 90%

“…However, as shown by Blythe and Flower [5], the performance of such methods is only marginally better than that of random guessing. Hence, several methods based on machine learning and statistical approaches have been recently proposed for predicting linear B-cell epitopes [18,30,32,8,31,10,9].…”

Section: Introductionmentioning

confidence: 99%

Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information

EL-Manzalawy

Dobbs

Honavar

2008

2008 IEEE International Conference on Bioinformatics and Biomedicine

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“…Hence, any reduction in the need for discovery and confirmatory wet-lab research by epitope prediction algorithms is highly desirable. Among in silico predictive methods from primary sequence information, epitope prediction algorithms are distinguished for their lack of reliability (1). This underperformance prompted us to examine current approaches to B-cell epitope prediction by use of extensive data on epitopes and confirmed non-epitope regions of the Chlamydia spp.…”

mentioning

confidence: 99%

“…However, even the best combinations of aa propensity scales performed only marginally better than random sequence selection (1). With the availability of B-cell epitope databases, antigenicity scales (17,18) and machine learning approaches (19 -24) have been attempted, and improved prediction accuracy has been reported.…”

mentioning

confidence: 99%

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

Rahman

Chowdhury

Sachse

et al. 2016

Journal of Biological Chemistry

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X-ray crystallography has shown that an antibody paratope typically binds 15-22 amino acids (aa) of an epitope, of which 2-5 randomly distributed amino acids contribute most of the binding energy. In contrast, researchers typically choose for B-cell epitope mapping short peptide antigens in antibody binding assays. Furthermore, short 6 -11-aa epitopes, and in particular non-epitopes, are over-represented in published B-cell epitope datasets that are commonly used for development of B-cell epitope prediction approaches from protein antigen sequences. We hypothesized that such suboptimal length peptides result in weak antibody binding and cause false-negative results. We tested the influence of peptide antigen length on antibody binding by analyzing data on more than 900 peptides used for B-cell epitope mapping of immunodominant proteins of Chlamydia spp. We demonstrate that short 7-12-aa peptides of B-cell epitopes bind antibodies poorly; thus, epitope mapping with short peptide antigens falsely classifies many B-cell epitopes as non-epitopes. We also show in published datasets of confirmed epitopes and non-epitopes a direct correlation between length of peptide antigens and antibody binding. Elimination of short, <11-aa epitope/non-epitope sequences improved datasets for evaluation of in silico B-cell epitope prediction. Achieving up to 86% accuracy, protein disorder tendency is the best indicator of B-cell epitope regions for chlamydial and published datasets. For B-cell epitope prediction, the most effective approach is plotting disorder of protein sequences with the IUPred-L scale, followed by antibody reactivity testing of 16 -30-aa peptides from peak regions. This strategy overcomes the well known inaccuracy of in silico B-cell epitope prediction from primary protein sequences.Knowledge of B-cell epitopes of proteins is essential in many fields of applied biomedical research, such as antibody diagnostics and therapeutics, vaccines, as well basic research. Laboratory methods for identification of such epitopes are time-consuming and labor-intensive. Hence, any reduction in the need for discovery and confirmatory wet-lab research by epitope prediction algorithms is highly desirable. Among in silico predictive methods from primary sequence information, epitope prediction algorithms are distinguished for their lack of reliability (1). This underperformance prompted us to examine current approaches to B-cell epitope prediction by use of extensive data on epitopes and confirmed non-epitope regions of the Chlamydia spp. proteome, accumulated in research on chlamydial molecular serology (2).Recent three-dimensional antibody-antigen complex studies (3-7) show that about 15-22-aa 2 antigen peptide residues are structurally involved in binding of epitopes to ϳ17-aa residues in antibody complementarity-determining regions (CDRs; paratopes). Among these 15-22 structural epitope residues, about 2-5 aa, termed functional residues, contribute most of the total binding energy to antibodies (6). These functional residues lie...

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“…13,14 However, the results of these studies are inconsistent and very little progress in B-cell epitope prediction has been made. 15,16 Generally, the lack of success in B-cell epitope definition is due, mainly, to the difficulties associated with the task. The optimal amino acid (aa) length of a B-cell epitope is five aa, 17 but longer epitopes have been described.…”

mentioning

confidence: 99%

The self/nonself issue: A confrontation between proteomes

Kanduc¹

2010

Self/Nonself

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Antibodies are immunoglobulin proteins that interact with specific areas on the surface of antigen proteins. These areas are referred to as B-cell epitopes. Identifying B-cell epitopes in order to induce a specific antibody response constitutes the unresolved core of immunology. Practically, the identification of epitopes in proteins is the fundamental, preliminary step in designing effective immunotherapy for cancer and infectious diseases as well as autoimmune pathologies.1-3 As a logical consequence, recent decades have seen determined efforts aimed at identifying and defining antigen epitopes. To understand the molecular determinants characterizing epitopic structures, immunology has used the self/nonself concept. [4][5][6] For decades, immunologists have based their studies, reasoning, experiments and clinical treatments on the idea that the immune system works by distinguishing between self and nonself.4,5 However, theoretical and experimental considerations lead to the recognition that there are no known molecular mechanisms that can explain how peptides of self-origin can be discriminated qualitatively from peptides of nonself-origin. 6 So, for example, how can the hexapeptide VLDVGG, which occurs in two different human proteins, be discriminated from the hexapeptide VLDVGG, which occurs in 380 bacterial, viral, protozoan and other organism proteins? How can the heptapeptide PPPPPPP, which occurs in 625 human proteins, be catalogued as self or nonself and distinguished from the heptapeptide PPPPPPP, which occurs in 12,883 bacterial, viral, protozoan and other organism proteins?

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Benchmarking B cell epitope prediction: Underperformance of existing methods

Cited by 281 publications

References 11 publications

Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information

Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction

The self/nonself issue: A confrontation between proteomes

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