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Title:
Antibody-protein interactions: benchmark datasets and prediction tools evaluation | BMC Structural Biology
Description:
Background The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. Results Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. Conclusion It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found.
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Keywords {🔍}

epitope, protein, methods, epitopes, residues, prediction, pubmed, article, google, scholar, complexes, structures, cas, structure, residue, antibody, proteins, docking, proteinprotein, dataset, values, structural, antibodyprotein, discotope, antigen, auc, bcell, method, authors, predicted, promate, representative, figure, consurf, score, considered, binding, performance, file, table, inferred, data, surface, nonepitope, number, patchdock, size, analysis, datasets, evaluation,

Topics {✒️}

loosely-packed multi-domain protein linear b-cell epitopes van regenmortel mh initial-stage protein-docking algorithm protein-protein binding site obligate protein-protein interactions decipher b-cell epitopes protein data bank article download pdf protein-protein binding sites b-cell epitope prediction protein-protein interaction sites continuous b-cell epitopes discontinuous b-cell epitopes b-cell antigenic epitopes protein-protein recognition sites providing protein-protein docking structure-based methods applying antibody-protein complexes grows protein-protein docking methods virus-specific synthetic peptide full size image antigen-antibody interactions provided single scale-based methods databases developed x-ray crystallography interaction energy function sequence-dependent methods based protein-protein interactions modeling antibody-antigen complexes antibody-protein complexes presenting antibody-protein complexes defining smith-gill sj b-cell epitope b-cell receptor privacy choices/manage cookies antibody-protein interactions obligate hetero-interactions full access residue-residue matches relative antibody-protein complexes represented b-cell epitopes compare scale-based methods receiver operating characteristic antibody-protein complex dissimilar putative binding sites support vector machine site-directed mutagenesis antibody-antigen interactions common format

Questions {❓}

An important question to consider is how to define individual epitopes yet avoid bias by over-presentation of particular epitopes?
Are HC45 and BH151 epitopes similar or different?
Eigenmann PA: Do we have suitable in-vitro diagnostic tests for the diagnosis of food allergy?
Given these shortcomings and current success rates, how can epitope prediction be improved?
How can we compare scale-based methods with patch prediction and docking methods?
Is this a fair comparison?
Perhaps the more fundamental question is whether it makes sense to consider a B-cell epitope a discrete feature of a protein at all?

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