Recent advances in B-cell epitope prediction methods

EL-Manzalawy, Yasser; Honavar, Vasant

doi:10.1186/1745-7580-6-s2-s2

Cited by 155 publications

(112 citation statements)

References 92 publications

(127 reference statements)

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“…Protein disorder tendency has also not been proposed for B-cell epitope prediction from primary amino acid sequences, although many protein property scales, particularly aa propensity scales, have been tested and recommended for B-cell epitope prediction (16,(63)(64)(65). In our study, the IUPred-L disorder scale has the highest epitope discriminatory power in all datasets.…”

Section: Discussionmentioning

confidence: 67%

“…A protein surface can be thought of as a continuous landscape of epitopic regions, and any region of this landscape may be identified as an epitope under specific conditions (56,(63)(64)(65). For instance, Singh et al (24) reported that all nonepitopes in the LBTope_Confirm dataset have been reported as "non-epitopes" in at least two studies.…”

Section: Discussionmentioning

confidence: 99%

“…This approach does not identify which residues of the peptide are in binding contact with antibody CDR residues and which actually contribute to the antigen-antibody complex formation. Nonetheless, antibody-reactive peptide sequences, particularly those identified by systematic mapping with overlapping peptides, are commonly referred to as B-cell epitopes (16,(63)(64)(65). This terminology is justified because even non-binding residues are specifically required to provide the structural context for binding residues, thus the linear peptide sequence is still an indispensable, if not complete, characterization of a B-cell epitope.…”

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: 67%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Although the majority of B-cell epitopes are conformational epitopes, the experimental determination of epitopes has primarily focused on the identification of linear B-cell epitopes (El-Manzalawy and Honavar, 2010). Therefore, the prediction of linear B-cell epitopes of the GldJ protein may help us develop a vaccine and diagnostic reagents and understand the pathogenesis and immunological action of RA (Tian et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Characterization of Riemerella anatipestifer CH-1 gldJ gene and GldJ protein

Yuan¹,

Cheng²,

Wang³

2014

Genet. Mol. Res.

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ABSTRACT. Riemerella anatipestifer (RA) CH-1, a highly virulent field strain, was isolated and identified by our laboratory. The gldJ gene was conserved in RA, and it had a typical TATA promoter region and AU-rich sequence within the 5' untranslated region. The GldJ protein was an outer-membrane lipoprotein with a signal peptide cleavage site between amino acids 20 and 21. GldJ was also a member of proteins involved in gliding motility. The RA GldJ protein had 16 phosphorylation sites and 4 N-glycosylation sites. These functional sites played an important role in the posttranslational modification of the GldJ protein. In addition, the GldJ protein of RA CH-1 had strong immunogenicity. Fifteen B-cell epitopes were identified in the GldJ protein, which might be a good biological material for the development of a subunit vaccine and diagnostic reagents. The GldJ protein also had the activity of formylglycine-generating sulfatase enzyme. The 3-dimensional structure models of GldJ were constructed based on 2 templates using the SwissModel automatic modeling mode in the SWISS-MODEL workplace. Here, we characterized the RA gldJ gene and GldJ protein to contribute to the functional annotation for the GldJ protein.

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“…Numerous computational tools have shown promise for reliable epitope prediction and have been reviewed extensively [16,[24][25][26][27][28][29][30][31][32]. Among this list of computational tools, Molecular Dynamics (MD) simulations are particularly useful for structure-based epitope prediction that exploits not only static tertiary structural information about the relevant antibody and antigen, but also the time-dependent dynamical and conformational properties of both proteins and their predicted thermodynamic interaction energies.…”

Section: Introductionmentioning

confidence: 99%