NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11

By Claus Lundegaard, Kasper Lamberth, M. Harndahl, Søren Buus, Ole Lund, Morten Nielsen. Nucleic Acids Res., 2008

815 citations recorded by the source corpus.

NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8–11 for all 122 alleles. artificial neural network predictions are given as actual IC50 values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75–80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.

Subfields: Immunology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2b60a7f6a75c3a8c6198d4e3af857d9fef139aa4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

In Molecular Biology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Predicting Peptide-MHC Binding Affinities with Imputed Training Data (2016, methodology): “Two of these predictors, NetMHC (7) and NetMHCpan (8), have emerged as the methods of choice across multiple fields of study within immunology, including virology (9), tumor immunology (10), and autoimmunity (11).”
PD-1 Blockade in Tumors with Mismatch-Repair Deficiency (2015, neutral): “To assess the potential for mutant peptide binding, somatic exome data combined with each individual patient’s major histocompatibility complex (MHC) class I HLA haplotype were applied to an epitope prediction algorithm.(31,32) This algorithm provided an estimate of the total number of mutation-asso”
Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer (2016, methodology): “The HLA genotype served as input to netMHCpan to predict the MHC class I binding potential of each somatic and wild-type peptide (IC50 nM), with each peptide classified as a strong binder (SB), weak binder (WB) or non-binder (NB) (41-43).”
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].”
pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens (2016, background): “However, it should be noted that most epitope prediction algorithms, including NetMHC [13, 14], only work with an algorithm-specific subset of HLA alleles, so we are constrained to the set of NetMHC-compatible alleles.”
Peptide binding predictions for HLA DR, DP and DQ molecules (2010, methodology): “This neural network based approach combines the peptide sequence representation used in the NetMHC algorithm [26,27] that was highly successful in predicting the binding specificity of HLA class I molecules [28,29] with the representation of peptide flanking residues and peptide length used in NetMH”
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).”
MHCflurry: open-source class I MHC binding affinity prediction (2017, neutral): “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].”