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

By I. Hoof, Bjoern Peters, John Sidney, L. E. Pedersen, Alessandro Sette, Ole Lund, Søren Buus, Morten Nielsen. 2008

807 citations recorded by the source corpus.

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.

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Citations in the corpus

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma (2016, unknown): “8 (Hoof et al., 2009; Nielsen et al., 2007) and NetMHCIIpan v3.”
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens (2016, methodology): “Previous studies [38, 39] have shown that allele-specific epitope prediction software, such as NetMHC, perform slightly better when compared to pan-specific methods such as NetMHCpan [40 – 42] in case of well-characterized alleles due to availability of large amounts of training data.”
Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction (2017, methodology): “Rules for peptide binding to HLAmolecules have been studied extensively for a subset of HLA alleles (Vita et al., 2015) and have been encoded in modern advanced neural-network-based algorithms (Hoof et al., 2009; Lundegaard et al., 2008).”
A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction (2020, methodology): “Specifically, two NN-based prediction tools and one PSSM-based tool have been included in this consensus method, namely (i) NetMHC 3.4 [94, 95], (ii) NetMHCpan 2.8 [86, 96] and (iii) Pickpocket 1.1 [24].”
MHCflurry: open-source class I MHC binding affinity prediction (2017, methodology): “The NetMHC family of tools, which includes NetMHC[5] and NetMHCpan [6], are considered the state of the art predictive models for this task[7].”
NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets (2016, methodology): “training approach that allows for leveraging of binding information across multiple MHC molecules leads to a significant boost in predictive performance for alleles covered with limited or even no binding data [2].”
Gapped sequence alignment using artificial neural networks: application to the MHC class I system (2016, background): “Contact: mniel@cbs.dtu.dk Supplementary information: Supplementary data are available at Bioinformatics online.”
Immune epitope database analysis resource (2012, methodology): “This choice was motivated by the expected predictive performance of the methods in decreasing order: Consensus > NetMHC > SMM > NetMHCpan > Comb-Lib.”