Benchmark datasets of immune receptor-epitope structural complexes

Abstract

BackgroundThe development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets.ResultsTo address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need.ConclusionsSCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.

Peptide-Based Vaccines for Tuberculosis

“…The home page search is designed to simplify the search process for many commonly queries such as Epitope (Linear peptide, discontinuous peptide, non-peptidic, and Any), Assay (T cell, B cell, and MHC ligand), Epitope Source (Organism and Antigen), MHC Restriction (Class I, Class II, Non-classical, and Any), hosts (humans, non-human primates, and other animal species), and Disease. The Analysis Resource component provides a set of tools for predicting and analyzing immune epitopes, which can be divided into three categories: (1) T Cell Epitope Prediction Tools: Peptide binding to MHC class I or II molecules ( 29 , 37 ), peptide processing predictions and immunogenicity predictions ( 67 , 104 – 106 ), TCRmatch ( 107 ), and structure tools such as LYRA (Lymphocyte Receptor Automated Modelling) ( 108 ), SCEptRe (Structural Complexes of Epitope Receptor) ( 109 ), and Docktope ( 109 ); (2) B Cell Epitope Prediction Tools: Prediction of linear epitopes from protein sequence including Chou & Fasman Beta-Turn Prediction, Emini Surface Accessibility Prediction, Karplus & Schulz Flexibility Prediction, Kolaskar & Tongaonkar Antigenicity, Parker Hydrophilicity Prediction, Bepipred Linear Epitope Prediction, and Bepipred Linear Epitope Prediction 2.0 ( 46 – 52 ); Discotope ( 110 ), ElliPro ( 59 ), methods for modeling and docking of antibody and protein 3D structures ( 111 ), LYRA server ( 108 ), and SCEptRe ( 109 ); (3) Analysis tools: Population Coverage ( 28 ), Epitope Conservancy Analysis ( 112 ), Epitope Cluster Analysis ( 80 ), Computational Methods for Mapping Mimotopes to Protein Antigens ( ), RATE (Restrictor Analysis Tool for Epitopes) ( 113 ), and ImmunomeBrowser ( 114 ). The components of the IEDB database related to peptide-based vaccine development are described in detail below.…”

Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets

“…Peptide epitopes of various lengths (ranging from 7 to 20 residues) which presented on MHC Class I and II molecules were retrieved from SCEptRe (Structural Complexes of Epitope Receptors) [39], AutoPeptiDB [40], and Protein Data Bank (PDB) [41].…”

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

“…It has been observed repeatedly that paratopes localize mostly, but not exclusively, to CDRs (Kunik et al, 2012a), and that certain amino acids are preferentially enriched or depleted in the antibody binding regions (ABRs) (Mian et al, 1991;Nguyen et al, 2017;Ramaraj et al, 2012;Sela-Culang et al, 2013;Wang et al, 2018). For epitopes, several analyses have shown that their amino acid composition is essentially indistinguishable from that of other surface-exposed non-epitope residues if the corresponding antibody is not taken into account (Benjamin et al, 1984;Berzofsky, 1985;Burkovitz et al, 2013;Dalkas et al, 2014;Greiff et al, 2020;Jespersen et al, 2019;Kringelum et al, 2013;Kunik and Ofran, 2013;Lawrence and Colman, 1993;MacCallum et al, 1996;Mahajan et al, 2019;Ofran et al, 2008;Peng et al, 2014;Ponomarenko and Bourne, 2007;Raghunathan et al, 2012;Sela-Culang et al, 2013;Sivalingam and Shepherd, 2012).…”