title: 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.
authors.affiliation: From the Department of Pathobiology, Auburn University, Auburn, Alabama 36849 and.
authors.forename: Kh Shamsur
authors.initials: KhS
authors.lastname: Rahman
authors.affiliation: From the Department of Pathobiology, Auburn University, Auburn, Alabama 36849 and.
authors.forename: Erfan Ullah
authors.initials: EU
authors.lastname: Chowdhury
authors.affiliation: the Federal Institute for Animal Health, D-07743 Jena, Germany.
authors.forename: Konrad
authors.initials: K
authors.lastname: Sachse
authors.affiliation: From the Department of Pathobiology, Auburn University, Auburn, Alabama 36849 and kaltebe@auburn.edu.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Kaltenboeck
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001666:Binding Sites, Antibody; D002417:Cattle; D002689:Chlamydia / Q000276:immunology*; D002690:Chlamydia Infections / Q000276:immunology*; D018604:Epitope Mapping / Q000379:methods*; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry / Q000276:immunology*; D006801:Humans; D000069550:Machine Learning; D051379:Mice; D018448:Models, Immunological; D010455:Peptides / Q000737:chemistry / Q000276:immunology
chemical_list: D018985:Epitopes, B-Lymphocyte; D010455:Peptides
keywords: antibody; antigen; bioinformatics; epitope mapping; immunogenicity; protein motif; protein-protein interaction