scholarly journals B-Cell Epitope Discovery: The First Protein Flexibility-Based Algorithm – Zika Virus Conserved Epitope Demonstration

2020 ◽  
Author(s):  
Daniel W. Biner ◽  
Jason S. Grosch ◽  
Peter J. Ortoleva
Keyword(s):  
B Cell ◽  
Zika Virus ◽  
Clustering Algorithm ◽  
Cell Epitope ◽  
Protein Flexibility ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
B Cell Epitopes ◽  
Enveloped Virus ◽  
Epitope Discovery

<p></p><p>Antibody-antigen interaction – at antigenic local environments called B-cell epitopes – is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-eight enveloped virus-antibody structures) and 2) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of eleven Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.</p><p></p>

scholarly journals B-Cell Epitope Discovery: The First Protein Flexibility-Based Algorithm – Zika Virus Conserved Epitope Demonstration

2020 ◽  
Author(s):  
Daniel W. Biner ◽  
Jason S. Grosch ◽  
Peter J. Ortoleva
Keyword(s):  
B Cell ◽  
Zika Virus ◽  
Clustering Algorithm ◽  
Cell Epitope ◽  
Protein Flexibility ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
B Cell Epitopes ◽  
Enveloped Virus ◽  
Epitope Discovery

<p></p><p>Antibody-antigen interaction – at antigenic local environments called B-cell epitopes – is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-eight enveloped virus-antibody structures) and 2) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of eleven Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.</p><p></p>

B-Cell Epitope Discovery: The First Protein Flexibility-Based Algorithm – Zika Virus Conserved Epitope Demonstration

2020 ◽  
Author(s):  
Daniel W. Biner ◽  
Jason S. Grosch ◽  
Peter J. Ortoleva
Keyword(s):  
B Cell ◽  
Zika Virus ◽  
Clustering Algorithm ◽  
Cell Epitope ◽  
Protein Flexibility ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
B Cell Epitopes ◽  
Enveloped Virus ◽  
Epitope Discovery

<p></p><p>Antibody-antigen interaction – at antigenic local environments called B-cell epitopes – is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with clinically relevant enveloped viruses, simulated via molecular dynamics. The approach is validated against 1) seven structurally characterized enveloped virus epitopes which evolved the least (out of thirty-eight enveloped virus-antibody structures) and 2) eight preexisting epitope and peptide discovery algorithms. Rationale for a new benchmarking scheme is presented. A data-driven epitope clustering algorithm is introduced. The prediction of eleven Zika virus epitopes (for future exploration on recombinant vaccine technologies) is demonstrated. For the first time, protein flexibility is shown to outperform solvent accessible surface area as an epitope discovery metric.</p><p></p>

scholarly journals B-Cell Epitope Discovery: The First Protein Flexibility-Based Algorithm – Zika Virus Conserved Epitope Demonstration

2020 ◽  
Author(s):  
Daniel W. Biner ◽  
Jason S. Grosch ◽  
Peter J. Ortoleva
Keyword(s):  
B Cell ◽  
Zika Virus ◽  
Cell Epitope ◽  
Protein Flexibility ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
B Cell Epitopes ◽  
Local Environments ◽  
Epitope Discovery ◽  
Antigen Interaction

<p>Antibody-antigen interaction – at antigenic local environments called B-cell epitopes – is a prominent mechanism for neutralization of infection. Effective mimicry, and display, of B-cell epitopes is key to vaccine design. Here, a physical approach is evaluated for the discovery of epitopes which evolve slowly over closely related pathogens (conserved epitopes). The approach is 1) protein flexibility-based and 2) demonstrated with the Zika virus, simulated via molecular dynamics. The approach is validated against 1) the seven structurally characterized flavivirus epitopes which have evolved the least (out of thirty-eight flavivirus-antibody structures) and 2) eight preexisting epitope and peptide discovery algorithms. For the first time, protein flexibility outperforms solvent accessible surface area as an epitope discovery metric.</p>

scholarly journals Data curation to improve the pattern recognition performance of B-cell epitope prediction by support vector machine

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Cen Lim ◽  
Yee Ying Lim ◽  
Yee Siew Choong
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
B Cell ◽  
Recognition Performance ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Support Vector ◽  
B Cell Epitopes ◽  
Prediction Module ◽  
B Cell Epitope

Abstract B-cell epitope will be recognized and attached to the surface of receptors in B-lymphocytes to trigger immune response, thus are the vital elements in the field of epitope-based vaccine design, antibody production and therapeutic development. However, the experimental approaches in mapping epitopes are time consuming and costly. Computational prediction could offer an unbiased preliminary selection to reduce the number of epitopes for experimental validation. The deposited B-cell epitopes in the databases are those with experimentally determined positive/negative peptides and some are ambiguous resulted from different experimental methods. Prior to the development of B-cell epitope prediction module, the available dataset need to be handled with care. In this work, we first pre-processed the B-cell epitope dataset prior to B-cell epitopes prediction based on pattern recognition using support vector machine (SVM). By using only the absolute epitopes and non-epitopes, the datasets were classified into five categories of pathogen and worked on the 6-mers peptide sequences. The pre-processing of the datasets have improved the B-cell epitope prediction performance up to 99.1 % accuracy and showed significant improvement in cross validation results. It could be useful when incorporated with physicochemical propensity ranking in the future for the development of B-cell epitope prediction module.

Autoantibodies to a novel Rpp38 (Th/To) derived B-cell epitope are specific for systemic sclerosis and associate with a distinct clinical phenotype

Rheumatology ◽  
2019 ◽  
Author(s):  
Martial Koenig ◽  
Chelsea Bentow ◽  
Minoru Satoh ◽  
Marvin J Fritzler ◽  
Jean-Luc Senécal ◽  
...  
Keyword(s):  
B Cell ◽  
Antinuclear Antibodies ◽  
Cell Epitope ◽  
The Novel ◽  
Exact Probability ◽  
Chemiluminescence Immunoassay ◽  
B Cell Epitope ◽  
Diagnosis And Classification ◽  
Epitope Discovery

Abstract Objective Detection of antinuclear antibodies and specific autoantibodies is important in the diagnosis and classification of SSc. Several proteins of the Th/To complex, including Rpp25, Rpp38 and hPop1 are the target of autoantibodies in SSc patients. However, very little is known about the epitope distribution of this autoantigen. Consequently, we screened Rpp25, Rpp38 and hPop1 for B cell epitopes and evaluated their clinical relevance. Methods Serum pools with (n = 2) and without (n = 1) anti-Th/To autoantibodies were generated and used for epitope discovery. Identified biomarker candidate sequences were then utilized to synthesize synthetic, biotinylated, soluble peptides. The peptides were tested to determine reactivity with sera from SSc cohorts (n = 202) and controls (n = 159) using a chemiluminescence immunoassay. Additionally, samples were also tested for antibodies to full-length recombinant Rpp25 antibodies by chemiluminescence immunoassay. Results Several immunodominant regions were found on the three proteins. The strongest reactivity was observed with an Rpp38 peptide (aa 229–243). Autoantibodies to the Rpp38 peptide were detected in 8/149 (5.4%) limited cutaneous SSc patients, but not in any of 159 controls (P = 0.003 by two-sided Fisher's exact probability test). Although reactivity to the novel antigenic peptide was correlated with the binding to Rpp25 (rho = 0.44; P < 0.0001), subsets of patient sera either reacted strongly with Rpp25 or with the novel Rpp38-derived peptide. Conclusion A novel Rpp38 epitope holds promise to increase the sensitivity in the detection of anti-Th/To autoantibodies, thus enhancing the serological diagnosis of SSc.

scholarly journals The Antigenic Topology of Norovirus as Defined by B and T Cell Epitope Mapping: Implications for Universal Vaccines and Therapeutics

Viruses ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 432 ◽  
Author(s):  
Jessica M. van Loben Sels ◽  
Kim Y. Green
Keyword(s):  
T Cell ◽  
B Cell ◽  
Cell Epitope ◽  
T Cell Epitopes ◽  
B Cell Epitopes ◽  
P Domain ◽  
Capsid Protein Vp1 ◽  
Murine Noroviruses ◽  
Acute Nonbacterial Gastroenteritis ◽  
Insight Into

Human norovirus (HuNoV) is the leading cause of acute nonbacterial gastroenteritis. Vaccine design has been confounded by the antigenic diversity of these viruses and a limited understanding of protective immunity. We reviewed 77 articles published since 1988 describing the isolation, function, and mapping of 307 unique monoclonal antibodies directed against B cell epitopes of human and murine noroviruses representing diverse Genogroups (G). Of these antibodies, 91, 153, 21, and 42 were reported as GI-specific, GII-specific, MNV GV-specific, and G cross-reactive, respectively. Our goal was to reconstruct the antigenic topology of noroviruses in relationship to mapped epitopes with potential for therapeutic use or inclusion in universal vaccines. Furthermore, we reviewed seven published studies of norovirus T cell epitopes that identified 18 unique peptide sequences with CD4- or CD8-stimulating activity. Both the protruding (P) and shell (S) domains of the major capsid protein VP1 contained B and T cell epitopes, with the majority of neutralizing and HBGA-blocking B cell epitopes mapping in or proximal to the surface-exposed P2 region of the P domain. The majority of broadly reactive B and T cell epitopes mapped to the S and P1 arm of the P domain. Taken together, this atlas of mapped B and T cell epitopes offers insight into the promises and challenges of designing universal vaccines and immunotherapy for the noroviruses.

scholarly journals An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lenka Potocnakova ◽  
Mangesh Bhide ◽  
Lucia Borszekova Pulzova
Keyword(s):  
B Cell ◽  
In Silico ◽  
Epitope Mapping ◽  
Cell Epitope ◽  
Epitope Prediction ◽  
Diagnostic Tools ◽  
Accurate Identification ◽  
B Cell Epitopes ◽  
Epitope Identification ◽  
B Cell Epitope

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

scholarly journals Computer-Aided Design of an Epitope-Based Vaccine against Epstein-Barr Virus

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Julio Alonso-Padilla ◽  
Esther M. Lafuente ◽  
Pedro A. Reche
Keyword(s):  
T Cell ◽  
B Cell ◽  
Epstein Barr Virus ◽  
Cell Epitope ◽  
T Cell Epitopes ◽  
Cell Component ◽  
B Cell Epitopes ◽  
Barr Virus ◽  
Epstein Barr ◽  
Epitope Selection

Epstein-Barr virus is a very common human virus that infects 90% of human adults. EBV replicates in epithelial and B cells and causes infectious mononucleosis. EBV infection is also linked to various cancers, including Burkitt’s lymphoma and nasopharyngeal carcinomas, and autoimmune diseases such as multiple sclerosis. Currently, there are no effective drugs or vaccines to treat or prevent EBV infection. Herein, we applied a computer-aided strategy to design a prophylactic epitope vaccine ensemble from experimentally defined T and B cell epitopes. Such strategy relies on identifying conserved epitopes in conjunction with predictions of HLA presentation for T cell epitope selection and calculations of accessibility and flexibility for B cell epitope selection. The T cell component includes 14 CD8 T cell epitopes from early antigens and 4 CD4 T cell epitopes, targeted during the course of a natural infection and providing a population protection coverage of over 95% and 81.8%, respectively. The B cell component consists of 3 experimentally defined B cell epitopes from gp350 plus 4 predicted B cell epitopes from other EBV envelope glycoproteins, all mapping in flexible and solvent accessible regions. We discuss the rationale for the formulation and possible deployment of this epitope vaccine ensemble.

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

2018 ◽  
Vol 49 (4) ◽  
pp. 1600-1614 ◽  
Author(s):  
Shudong He ◽  
Jinlong Zhao ◽  
Walid Elfalleh ◽  
Mohamed Jemaà ◽  
Hanju  Sun ◽  
...  
Keyword(s):  
Amino Acids ◽  
T Cell ◽  
Phaseolus Vulgaris ◽  
B Cell ◽  
Cell Epitope ◽  
T Cell Epitope ◽  
T Cell Epitopes ◽  
Phaseolus Vulgaris L ◽  
B Cell Epitopes ◽  
B Cell Epitope

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.

Epitope Mapping of Porphyromonas gingivalis Heat-shock Protein and Human Heat-shock Protein in Human Atherosclerosis

2004 ◽  
Vol 83 (12) ◽  
pp. 936-940 ◽  
Author(s):  
J.-I. Choi ◽  
S.-W. Chung ◽  
H.-S. Kang ◽  
B.Y. Rhim ◽  
Y.-M. Park ◽  
...  
Keyword(s):  
T Cell ◽  
Heat Shock ◽  
Heat Shock Protein ◽  
B Cell ◽  
Cell Epitope ◽  
T Cell Epitopes ◽  
Igg Antibody ◽  
B Cell Epitopes ◽  
Human Heat Shock Protein ◽  
Human Hsp60

To identify T- and/or cross-reactive B-cell epitopes of P. gingivalis and human heat-shock protein (HSP)60 in atherosclerosis patients, we synthesized 104 overlapping synthetic peptides spanning whole molecules of P. gingivalis HSP60 and human HSP60, respectively. T-cell epitopes of P. gingivalis HSP were identified with the use of previously established P. gingivalis HSP-reactive T-cell lines. B-cell epitopes of P. gingivalis HSP60 and human HSP60 were identified by the use of patients’ sera. Anti- P. gingivalis, anti- P. gingivalis HSP60, or anti-human HSP60 IgG antibody titers were higher in the atherosclerosis patients compared with the healthy subjects. Five immunodominant peptides of P. gingivalis HSP60, identified as T-cell epitopes, were also found to be B-cell epitopes. Moreover, 6 cross-reactive B-cell epitopes of human HSP60 were identified. It was concluded that P. gingivalis HSP60 might be involved in the immunoregulatory process of atherosclerosis, with common T- and/or B-cell epitope specificities and with cross-reactivity with human HSP60.

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