NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Abstract

BackgroundBinding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking.Principal FindingsHere, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis.ConclusionsEncompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.

Alloreactive cytotoxic T cells provide means to decipher the immunopeptidome and reveal a plethora of tumor-associated self-epitopes

“…Selected targets were subjected to affinity-based algorithmic prediction of HLA-A*02:01-restricted peptides (cbs.dtu.dk/ services/NetMHC/), and those predicted to bind with the highest affinities were synthesized (5). Peptides were complexed with HLA-A*02:01 monomers by UV-induced ligand exchange, followed by multimerization using streptavidin (SA) conjugated to PE, APC, or PerCP-Cy5.5, respectively (6). By end of culture, CD8 pos T cells reactive to different epitopes were detected by flow cytometric measurements of combinations of fluorescently labeled pHLA-A2 multimers.…”

“…Indeed, although 100,000-750,000 peptide-MHC class I complexes are expressed for each allelic product on the cell surface (for HLA-A and HLA-B loci) (3,4), the largest HLA ligandome identified to date contains 14,065 peptides (5). In contrast, the predicted number of different HLA class I ligands would be 352,000 using the well-renowned computer algorithm NetMHCpan, considering that on average, 4.4% of all nonamers bind HLA class I (6) and that a cell contains at least 8 × 10 6 distinct nonamers (7). Thus, there is a very large gap between the number of confirmed and predicted HLA ligands.…”

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

“…To ensure concordance, we manually compared ATHLATES' calls of the normal versus tumor samples and ascertained there was at least no two-digit HLA typing discrepancy between any normaltumor pair. For each non-synonymous coding mutation from a tumor, we predicted its impact on the patient's HLA class I and II binding using the stand-alone version of the programs NetMHCpan v2.8 (Hoof et al, 2009;Nielsen et al, 2007) and NetMHCIIpan v3.0 (Karosiene et al, 2013), respectively. Specifically, for HLA class I binding prediction using netMHCpan v2.8, we tested all 9-11-mer peptides containing the mutated amino acids for binding to the patient's HLA-A, -B and -C. A peptide was defined as a neoepitope based on two criteria : i) predicted binding affinity ≤ 500nM, and ii) rank percentage ≤ 2% (default cutoff).…”

Characterization ofPlasmodium falciparumProteome at Asexual Blood Stages for Screening of Effective Vaccine Candidates: An Immunoinformatics Approach

“…Hence, with the above criteria, the consensually predicted 16 immunogenic antigens (nos. 1,3,5,6,7,9,11,13,15,16,19,20,22,25,26,31) using NetCTL and MULTIPRED2 could be considered as good vaccine targets for malaria and may be effective for larger human populations.…”