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Lavigne, G.

Publications and source records attributed to Lavigne, G..

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PEPOP: new approaches to mimic non-continous epitopes

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 as replacing a protein difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitope predicted by bioinformatics tools are commonly used and these sequential epitopes are used as such in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools proposed so far to predict peptide sequences: Superficial and PEPOP. PEPOP can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody. We have developed an improved version of PEPOP dedicated to answer to the experimentalists need for a tool able to handle proteins and to turn them into peptides. The PEPOP 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 proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature.\n\nWe present the user-friendly, well-structured web-site of PEPOP and its validation through the use of predicted immunogenic or antigenic peptides mimicking discontinuous epitopes in different experimental ways. PEPOP proposes 35 methods of peptide design to guide experimentalists in using peptides potentially capable of replacing the cognate protein in its interaction with an Ab.

bioinformatics

Benchmarking the PEPOP methods for mimicking discontinuous epitopes

Computational methods provide approaches to identify epitopes in protein antigens 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 antigen in its interaction with an antibody. In the present work, we describe these new methods and the benchmarking of their performances.\n\nBenchmarking 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 antigen-antibody 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.\n\nThe 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.

bioinformatics