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

Abstract

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...

Disordered epitopes as peptide vaccines

“…In fact, we found a positive correlation between predicted disorder and the likelihood that any putative epitope will test positive in an antibody binding assay reported in the IEDB, establishing that disorder is no impediment to effective antibody recognition (Figure A). Others have recently made the same observation, and extended it to show that predictors of protein disorder out‐perform dedicated sequence‐based predictors for the identification of B‐cell epitopes . This may reflect the fact that residues that score highly in disorder predictors are more likely to be solvent‐exposed, as solvent exposure is both a prerequisite for, and established predictor of, antigenicity …”

“…In our studies of MSP2, we have compared experimental measures of conformational disorder derived from NMR relaxation rates with observed patterns of antigenicity inferred from experimental immunizations of animals and humans . The result of that study, in contrast to the bioinformatic analysis, was a negative correlation between antigenicity and disorder, with the most flexible regions of MSP2 being the least likely to be recognized by antibodies in MSP2‐immune sera, and with regions that show slight conformational restriction apparently dominating the antibody response in these immunizations. It is important to note that for MSP2 the experimental measures of disorder used in this study correlate poorly with the disorder prediction scores used in the bioinformatics analysis, reflecting the uniformly high levels of disorder across MSP2 (>85% of MSP2 residues have IUPred scores >0.8).…”

Mixed Chlamydia trachomatis Peptide Antigens Provide a Specific and Sensitive Single-Well Colorimetric Enzyme-Linked Immunosorbent Assay for Detection of Human Anti-C. trachomatis Antibodies

“…Previously, we identified highly reactive and specific B-cell epitopes of immunodominant proteins of all Chlamydia species ( 16 – 21 ). An important finding was that 16- to 30-amino-acid peptide antigens combined with an N-terminal hydrophilic serine-glycine-serine-glycine spacer were required for optimal signal strength ( 16 – 18 ).…”

“…Previously, we identified highly reactive and specific B-cell epitopes of immunodominant proteins of all Chlamydia species ( 16 – 21 ). An important finding was that 16- to 30-amino-acid peptide antigens combined with an N-terminal hydrophilic serine-glycine-serine-glycine spacer were required for optimal signal strength ( 16 – 18 ). Further testing of predicted peptide antigens with sera from C. trachomatis - infected women revealed additional human host-specific C. trachomatis B-cell epitopes that were recognized only by the natural human host ( 20 ) but did not react with sera from mice that were hyperimmunized to C. trachomatis ( 16 ).…”

Humoral immune response against two surface antigens of Chlamydia pecorum in vaccinated and naturally infected sheep

“…This strategy holds some promise although the cross-reactivity observed for a number of the analysed proteins to those from other chlamydial species was a significant limiting factor [ 18 ]. To overcome some of these issues, others have utilised in-silico prediction methods [ 19 ] and screening synthetic peptide libraries in order to capture antibodies that are specific to several chlamydial polymorphic immuno-dominant antigens (OmpA, CT618, PmpD, IncA, CT529, CT442, IncG, Omp2, TarP, and IncE proteins) for each of the currently described chlamydial species [ 19 ]. This work has revealed some promising new antigens for development of a C .…”