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2). protocol explains the relative advantages of the server compared to other epitope mapping tools, its limitations, and potential areas of improvement. No special skills or experience is required for users other than preparing PDB input files for the target antigen and the sequence in FASTA format or PDB input for the antibody if present. The server may take 45 90 minutes depending on the size of the proteins. == EDITORIAL SUMMARY: == AbEMap generates large ensembles of docked antigen-antibody structures based on the structure of an antigen and either the structure or the sequence of an antibody. For each antigen residue, a likelihood score of being part of the epitope is usually obtained. == TWEET: == #EpitopeMappingWithPIPER the AbEMap web server predicts the likelihood of antigen residues interacting with a specific antibody based on the structure or Moxisylyte hydrochloride the sequence of the antibody. == Teaser: == AbEMap predicts antibody-specific epitopes == INTRODUCTION == Antibodies form one of the key arms of the adaptive immune system in vertebrates. They target solvent-exposed proteins called antigens around the surfaces of pathogens. After recognition and contact, the antibodies mediate the humoral immune response to the attached pathogen1. The diversity and specificity of antibodies are the reason why harnessing their unique features is usually paramount in the pharmaceutical industry. Understanding and accurately predicting atomic-level details of the antibody-antigen interface are crucial for utilizing antibodies2. Moxisylyte hydrochloride Finding the antigen residues in the interface, henceforth called epitope mapping, can be useful for the Moxisylyte hydrochloride design of monoclonal antibodies3, for developing vaccines4, and for investigating immune responses5. The development of methods for predicting antibody-antigen interactions and for antibody-based drug discovery was traditionally handicapped by the difficulty of obtaining high numbers of antibody sequences. However, due to advances in high-throughput single-cell and Variable-Diversity-Joining(VDJ) sequencing of B-cell receptor repertoire6,7, the availability of antibody sequences is usually no longer an issue in the race towards developing antibody-based drugs. Fast and accurate prediction of the epitopes for these antibody targets has become the new bottleneck8. Currently, epitope mapping efforts are dominated by experimental techniques such as X-ray crystallography, mutagenesis (for instance, alanine scanning), and phage display. X-ray crystallography is usually laborious and expensive, whereas mutagenesis and phage display generally do not provide atomic-level details9. Importantly, none of these experimental methods can be used in a high throughput manner. In view of these limitations, substantial efforts have been devoted to the development of computational epitope mapping methods10-15. However, epitope prediction for a given antigen and a given antibody is usually a difficult computational problem that requires further development to improve the accuracy and reliability of the predictions16. Part of the difficulty is due to the paucity of nonredundant structural data on antibody-antigen interactions since, as reported by Jespersen et al. in 2017, less than 25% of the antibody-antigen complexes found in the Moxisylyte hydrochloride Protein Data Lender (PDB) are unique when taking a 70% sequence identity threshold cutoff for the antigen17. The challenge of epitope mapping can be partially addressed by obtaining residues around the antigens surface that are most likely to Rabbit Polyclonal to PKA alpha/beta CAT (phospho-Thr197) interact with a generic antibody (as opposed to a specific antibody)10,12,17,18. Some examples of such an approach are implemented in the servers Spatial Epitope Prediction for Protein Antigens (SEPPA)10,12and BEpro (formerly known as PEPITO)18. SEPPA uses a logistic regression algorithm with features such as antigen residue surface accessibility and propensity of unit-triangle patches (3 residue-groups around the antigens surface) among other factors to score the surface residues10-12. BEpro adds amino-acid propensity scale and side-chain orientations besides other features18. Despite the achievements of the antibody-agnostic approach, it is crucial to spotlight that epitopes are, by definition, relational entities and that epitope mapping ought to be antibody-specific. This is evidenced by several antigens with particular affinities to different antibodies at different interfaces. A well-studied example is usually hen egg lysozyme.