A combined prediction strategy increases identific

Authors: Lasse Eggers Pedersen Michael Rasmussen Mikkel Harndahl Morten Nielsen Søren Buus Gregers Jungersen

Publish Date: 2015/11/14

Volume: 68, Issue: 2, Pages: 157-165

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Abstract

Affinity and stability of peptides bound by major histocompatibility complex MHC class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes CTLs In silico prediction methods of peptideMHC binding followed by experimental analysis of peptideMHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides We have earlier reported the peptide binding motif of the porcine MHCI molecules SLA10401 and SLA20401 identified by an ELISA affinitybased positional scanning combinatorial peptide library PSCPL approach Here we report the peptide binding motif of SLA30401 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptideMHC complexes to improve rational peptide selection Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms NetMHCpan peptide ligands from a repository of 8900 peptides were predicted for binding to SLA10401 SLA20401 and SLA30401 and validated by affinity and stability assays From the pool of predicted peptides for SLA10401 SLA20401 and SLA30401 a total of 71 28 and 38  were binders with affinities below 500 nM respectively Comparison of peptideSLA binding affinity and complex stability showed that peptides of high affinity generally but not always produce complexes of high stability In conclusion we demonstrate how stateoftheart prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding hence providing an expansion of the field of peptideMHC analysis also to include pigs as a livestock experimental model

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