NetMHCpan, a method for MHC class I binding prediction beyond humans

Abstract

Binding 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 genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method's ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.

Predicting T cell recognition of MHC class I restricted neoepitopes

“…A number of algorithms are available that can perform this task. The IEDB analysis resource hosts validated and benchmarked algorithms that are freely available to the community 35 , among them, NetMHCpan 36 , which is a commonly utilized tool that provides quantitative affinity predictions for all alleles in our panel. Using the NetMHCpan tool, we wanted to define thresholds that could be utilized in mutated neoepitope prediction pipelines, at least partially removing the need for experimental determination of HLA binding.…”

“…35,36,66 NetMHCpan was selected because it consistently performs as one of the best prediction tools across a wide array of alleles, and also provides predicted IC50 nM values for the complete set of common class I alleles considered here. 67,68 In addition to predicted affinity (IC50), NetMHCpan also provides a percentile score expressing the relative capacity of each peptide to bind each specific allele, compared to a universe of potential sequences of the same size.…”

A Targeted LC‐MS Strategy for Low‐Abundant HLA Class‐I‐Presented Peptide Detection Identifies Novel Human Papillomavirus T‐Cell Epitopes

“…The predictions were performed for 8–11‐mer peptides with NetMHC 4.0,18 NetMHC 3.4,19 NetMHCpan 3.0,20 NetMHCpan 2.8,20 NetMHCcons 1.1,21 Consensus,22 PickPocket 1.1,23 SMM,24 SMMPMBEC,25 BIMAS,26 and SYFPEITHI 27. Results of all prediction algorithms were combined.…”

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).…”