Abstract
BackgroundComputational 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.ResultsBenchmarking 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.ConclusionThe 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.
Highlights
Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments
Benchmark studies have highlighted that tools for predicting continuous epitopes have low efficiency [22,23,24] and that methods based on the Ag 3D structure show limited sensitivity (Se) and positive predictive value (PPV) [25]
To bioinformatically design a peptide from the 3D structure of a given protein, Prediction capacity controls To assess the capacity of the different PEPOP methods to predict peptides that mimic epitopes, we used a dataset of experimentally (X-ray crystallography) determined epitopes that was filtered to eliminate any epitope redundancy (Additional file 1: Table S1) [25]
Summary
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 improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and 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. Benchmark studies have highlighted that tools for predicting continuous epitopes have low efficiency [22,23,24] and that methods based on the Ag 3D structure show limited sensitivity (Se) and positive predictive value (PPV) [25]
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