Benchmarking B-Cell Epitope Prediction with Quantitative Dose-Response Data on Antipeptide Antibodies: Towards Novel Pharmaceutical Product Development

Caoili, Salvador Eugenio C.

doi:10.1155/2014/867905

Cited by 7 publications

(6 citation statements)

References 55 publications

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“…For instance, there are likely numerous false positive epitopes for highly studied organisms and few identified epitopes for poorly studied organisms. Also, there is a lack of quantitative data reported for epitopes [58], such as the proportion of a given population that binds an epitope. To address this lack of information, we first used K-TOPE to analyze specimens for responses to common pathogens in a general population.…”

Section: Discussionmentioning

confidence: 99%

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes

et al. 2019

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Antibodies are essential to functional immunity, yet the epitopes targeted by antibody repertoires remain largely uncharacterized. To aid in characterization, we developed a generalizable strategy to predict antibody-binding epitopes within individual proteins and entire proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a random library and identified the peptides using next-generation sequencing. To predict antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We used the enrichment over background of these k-mers in the antibody-binding peptide dataset to predict antibody-binding epitopes. As a positive control, we used this approach, termed K-mer Tiling of Protein Epitopes (K-TOPE), to predict epitopes targeted by monoclonal and polyclonal antibodies of well-characterized specificity, accurately recovering their known epitopes. K-TOPE characterized a commonly targeted antigen from Rhinovirus A , predicting four epitopes recognized by antibodies present in 87% of sera (n = 250). An analysis of 2,908 proteins from 400 viral taxa that infect humans predicted seven enterovirus epitopes and five Epstein-Barr virus epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus proteomes similarly predicted 22 epitopes recognized by >30% of specimens. Twelve of these common viral and bacterial epitopes agreed with previously mapped epitopes with p-values < 0.05. Additionally, we predicted 30 HSV2-specific epitopes that were 100% specific against HSV1 in novel and previously reported antigens. Experimentally validating these candidate epitopes could help identify diagnostic biomarkers, vaccine components, and therapeutic targets. The K-TOPE approach thus provides a powerful new tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires.

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Section: Discussionmentioning

confidence: 99%

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes

et al. 2019

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“…For instance, there are likely numerous false positive epitopes for highly studied organisms and few identified epitopes for poorly studied organisms. Also, there is a lack of quantitative data reported for epitopes [53], such as the proportion of a given population that binds an epitope. To address this lack of information, we first used K-TOPE to analyze specimens for responses to common pathogens in a general population.…”

Section: Discussionmentioning

confidence: 99%

A general approach for identifying protein epitopes targeted by antibody repertoires using whole proteomes

Paull

Johnston

Ibsen

et al. 2019

Preprint

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28Antibodies are essential to functional immunity, yet the epitopes targeted by antibody 29 repertoires remain largely uncharacterized. To aid in characterization, we developed a 30 generalizable strategy to identify antibody-binding epitopes within individual proteins and entire 31 proteomes. Specifically, we selected antibody-binding peptides for 273 distinct sera out of a 32 random library and identified the peptides using next-generation sequencing. To identify 33 antibody-binding epitopes and the antigens from which these epitopes were derived, we tiled the 34 sequences of candidate antigens into short overlapping subsequences of length k (k-mers). We 35 used the enrichment over background of these k-mers in the antibody-binding peptide dataset to 36 identify antibody-binding epitopes. As a positive control, we used this approach, termed K-mer 37 Tiling of Protein Epitopes (K-TOPE), to identify epitopes targeted by monoclonal and polyclonal 38 antibodies of well-characterized specificity, accurately recovering their known epitopes. K-39 TOPE characterized a commonly targeted antigen from Rhinovirus A, identifying three epitopes 40 recognized by antibodies present in 83% of sera (n = 250). An analysis of 2,908 proteins from 41 400 viral taxa that infect humans revealed seven enterovirus epitopes and five Epstein-Barr virus 42 epitopes recognized by >30% of specimens. Analysis of Staphylococcus and Streptococcus 43 proteomes similarly revealed six epitopes recognized by >40% of specimens. These common 44 viral and bacterial epitopes exhibited excellent agreement with previously mapped epitopes.45 Additionally, we identified 30 HSV2-specific epitopes that were 100% specific against HSV1 in 46 novel and previously reported antigens. The K-TOPE approach thus provides a powerful new 47 tool to elucidate the organisms, antigens, and epitopes targeted by human antibody repertoires. 48 49 50 Introduction 51 52 Immunological memory allows for rapid antibody responses towards diverse antigens 53 long after initial exposure. For example, the adaptive immune response to many vaccinations is 54 often sustained throughout an individual's lifetime [1]. This immunological information is 55 archived within the genes encoding B-cell and T-cell receptors along with the corresponding 56 receptor structures, but has proven difficult to characterize in a comprehensive manner. The 57 ability to more fully interrogate immunological memory could reveal exposures to pathogens, 58 commensal organisms, and allergens. Such information has proven useful for correlating 59 antibody responses with disease outcomes to design more effective vaccines [2]. A detailed 60 record of immune exposures can also facilitate the identification of biomarkers to diagnose 61 infectious [3], autoimmune [4], and allergic conditions [5]. Furthermore, the capability to 62 broadly characterize antibody repertoires at the epitope level could be used to identify conserved 63 pathogen epitopes [6] and tumor specific antigen epitopes [7] to aid in therapeutic disco...

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“…Due to these difficulties to realize systematic experimental validations, we believe PEPOP, and others similar tools, have to be seen as "test tubes" which will gradually be validated as studies will be developed, until a consensus satisfactory validation process is developed. Although some studies begin to explore this problem [47] the proposed benchmark is not applicable for all epitope prediction tools neither for all studies. Anyway, PEPOP has already been successfully used in several studies of different goals [27,[32][33][34][35]48,49].…”

Section: Discussionmentioning

confidence: 99%

Benchmarking the PEPOP methods for mimicking discontinuous epitopes

Demolombe

Brevern

Molina

et al. 2018

Preprint

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Computational methods provide approaches to identify epitopes in protein antigens to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein antigen in its interaction with an antibody. In the present work, we describe these new methods and the benchmarking of their performances.Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 antigen-antibody complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method.The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, "optimized" methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.

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Benchmarking B-Cell Epitope Prediction with Quantitative Dose-Response Data on Antipeptide Antibodies: Towards Novel Pharmaceutical Product Development

Cited by 7 publications

References 55 publications

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes

A general approach for predicting protein epitopes targeted by antibody repertoires using whole proteomes

A general approach for identifying protein epitopes targeted by antibody repertoires using whole proteomes

Benchmarking the PEPOP methods for mimicking discontinuous epitopes

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