Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data

Peptide
binding to MHC class I molecules is the single most selective
step in antigen presentation and the strongest single correlate to
peptide cellular immunogenicity. The cost of experimentally characterizing
the rules of peptide presentation for a given MHC-I molecule is extensive,
and predictors of peptide–MHC interactions constitute an attractive
alternative. Recently, an increasing amount of MHC presented peptides
identified by mass spectrometry (MS ligands) has been published. Handling
and interpretation of MS ligand data is, in general, challenging due
to the polyspecificity nature of the data. We here outline a general
pipeline for dealing with this challenge and accurately annotate ligands
to the relevant MHC-I molecule they were eluted from by use of GibbsClustering
and binding motif information inferred from in silico models. We illustrate
the approach here in the context of MHC-I molecules (BoLA) of cattle.
Next, we demonstrate how such annotated BoLA MS ligand data can readily
be integrated with in vitro binding affinity data in a prediction
model with very high and unprecedented performance for identification
of BoLA-I restricted T-cell epitopes. The prediction model is freely
available at http://www.cbs.dtu.dk/services/NetMHCpan/NetBoLApan. The approach has here been applied to the BoLA-I system, but the
pipeline is readily applicable to MHC systems in other species.