Identification of discontinuous antigenic determinants on proteins based on shape complementarities

Rapberger, Ronald; Lukas, Arno; Mayer, Bernd

doi:10.1002/jmr.819

Cited by 30 publications

(25 citation statements)

References 44 publications

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“…Although hydrophobic amino acids except for alanine, the smallest one, are under-represented in epitopes, those that are present may "provide the glue" in the final stabilization of the antigen-antibody complex by hydrophobic interaction. All non-covalent antigenantibody interactions are thought to be driven by shape complementarities in the complex formation (57). Thus, paratopes may interact preferentially with flexible regions of an antigen rather than with highly structured regions (72).…”

Section: Discussionmentioning

confidence: 99%

“…B-cell epitopes have historically been recognized as hydrophilic (10,11), flexible (12), mobile (high B factor; 73, 74), surface-exposed or solvent-accessible (3,5,13,57,75), enriched with ␤-turns (14) or coils/loops (3-4, 7), and highly sequencepolymorphic (3)(4)(5). Recent three-dimensionally based studies (3-7) also show that epitopes compared with non-epitope regions are (i) enriched for polar and charged amino acids and depleted of hydrophobic amino acids, (ii) more surface-exposed than the remaining protein, (iii) more sequence polymorphic, and (iv) enriched with unorganized secondary structure elements and depleted of strands and helices (3-7).…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…Through a combination of statistics, spatial context, DiscoTope could successfully predict the location of epitopes on the previously mentioned dataset. In 2007, Rapberger et al proposed a new kind of conformational B-cell epitopes prediction framework [38]. They took advantage of the complementary geometric shape of antigen epitopes and antibody paratope, as well as the measure of binding energy of antigen and antibody.…”

Section: Structure-based Prediction Methodsmentioning

confidence: 99%

Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction

Sun

Liu

et al. 2013

Computational and Mathematical Methods in Medicine

103

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Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed.

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“…To our knowledge, the first epitope prediction method taking into account antibody structure was suggested in 2007 by Rapberger et al [32]. Appreciating that the antigen epitope should geometrically and electrostatically match the antibody structure, they have generated a virtual library of 11,951 antibody models (based on computational mutations of known antibody structures).…”

Section: Antibody-specific B-cell Epitope Predictionmentioning

confidence: 99%

Antibody specific epitope prediction—emergence of a new paradigm

Sela-Culang

Ofran

Peters

2015

Current Opinion in Virology

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The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data.

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Identification of discontinuous antigenic determinants on proteins based on shape complementarities

Cited by 30 publications

References 44 publications

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

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

Bioinformatics Resources and Tools for Conformational B-Cell Epitope Prediction

Antibody specific epitope prediction—emergence of a new paradigm

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