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Background: Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances., Results: Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method., Conclusion: The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, "optimized" methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.

Background: Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a protein which is difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitopes predicted by bioinformatics tools are commonly used and these sequential epitopes are used as is in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools have been proposed so far to predict peptide sequences: Superficial and PEPOP 2.0. PEPOP 2.0 can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody., Results: We have developed an improved version of PEPOP (PEPOP 2.0) dedicated to answer to experimentalists' need for a tool able to handle proteins and to turn them into peptides. The PEPOP 2.0 web site has been reorganized by peptide prediction category and is therefore better formulated to experimental designs. Since the first version of PEPOP, 32 new methods of peptide design were developed. In total, PEPOP 2.0 proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature., Conclusion: Through the presentation of its user-friendly, well-structured new web site conceived in close proximity to experimentalists, we report original methods that show how PEPOP 2.0 can assist biologists in dealing with discontinuous epitopes.

Computational identification of potential epitopes with an immunogenic capacity challenges immunological research. Several methods show considerable success, and together with experimental studies, the efficiency of the algorithms to identify potential peptides with biological activity has improved. Herein, an epitope was designed by combining bioinformatics, docking, and molecular dynamics simulations. The hemagglutinin protein of the H1N1 influenza pandemic strain served as a template, owing to the interest of obtaining a scheme of immunization. Afterward, we performed enzyme-linked immunosorbent assay (ELISA) using the epitope to analyze if any antibodies in human sera before and after the influenza outbreak in 2009 recognize this peptide. Also, a plaque reduction neutralization test induced by virus-neutralizing antibodies and the IgG determination showed the biological activity of this computationally designed peptide. The results of the ELISAs demonstrated that the serum of both prepandemic and pandemic recognized the epitope. Moreover, the plaque reduction neutralization test evidenced the capacity of the designed peptide to neutralize influenza virus in Madin-Darby canine cells., (Copyright © 2015 John Wiley & Sons, Ltd.)

The occurrence of alloantibodies against Factor VIII (FVIII) is the main iatrogenic complication in haemophilia A (HA). Anti-FVIII autoantibodies may also spontaneously appear in non-HA patients, leading to acquired haemophilia A. In both contexts, the antibody response against FVIII is complex and difficult to analyse due to the lack of suitable tools. Our purpose was to comprehensively map, at the amino acid level, discontinuous epitopes of the C2 domain of FVIII targeted by patients' antibodies. We synthesized 33 synthetic peptides, which were predicted by the bioinformatic algorithm PEPOP to mimic C2 domain discontinuous epitopes. Using an inhibition assay based on the x-MAP technology, we evaluated their ability to block the binding to the C2 domain of anti-C2 domain antibodies from pooled plasma samples. Nine peptides were thus selected and tested again in individual plasma samples. Our results support the view that C2 domain epitopes are organized as an epitopic mosaic distributed around the molecule, showed that each patient displayed a specific anti-C2 epitopic profile, and confirmed the complexity and variability of the immune response against the C2 domain of FVIII. This ability to finely map epitopes could be further used to follow the antibody specificity modifications over time., (© 2011 Blackwell Publishing Ltd.)

Background: Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides., Results: PEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated., Conclusion: PEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at http://diagtools.sysdiag.cnrs.fr/PEPOP/.

Motivation: Phage display is a widespread technique used to obtain peptide mimotopes selected by binding to a given monoclonal antibody in a similar way as the native epitope. However, the localization of the interaction site mimicked by the mimotopes on the surface of the antigen is not always a straightforward task. MIMOP is a computational tool developed with the aim of helping experimentalists to analyze a set of mimotope sequences and guide them in the identification of the mimicked region., Results: To predict potential epitopic regions, MIMOP integrates two different approaches combining two- and three-dimensional analyses: MimAlign starts from degenerated alignment analyses, and MimCons is based on consensus identification. The relevance and usefulness of the tool are illustrated by four use cases corresponding to real-life situations.