2017
DOI: 10.4049/jimmunol.1700893
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NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Abstract: Cytotoxic T cells are of central importance in the immune system’s response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC (major histocompatibility complex) class I molecules. Peptide binding to MHC molecules is the single most selective step in the antigen presentation pathway. On the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has therefore attracted large attention. In the past, predictors of peptide-MHC interaction h… Show more

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Cited by 1,154 publications
(1,000 citation statements)
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References 31 publications
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“…The NNAlign models generated by the MS‐rescue pipeline are conceptually very similar to NetMHCpan, a popular method for the prediction of peptide‐MHC interactions . In fact, NNAlign is the machine‐learning engine at the core of many pieces of software in the NetMHC family .…”
Section: Resultsmentioning
confidence: 99%
“…The NNAlign models generated by the MS‐rescue pipeline are conceptually very similar to NetMHCpan, a popular method for the prediction of peptide‐MHC interactions . In fact, NNAlign is the machine‐learning engine at the core of many pieces of software in the NetMHC family .…”
Section: Resultsmentioning
confidence: 99%
“…44 NetMHCpan4 was trained on naturally eluted ligands as well as on binding affinity data and therefore returns two predicted values: likelihood of a peptide becoming a natural ligand (EL), and predicted binding affinity (BA). We used NetMHCpan-4.0 to perform predictions for the NCI dataset.…”
Section: Resultsmentioning
confidence: 99%
“…The newest version of this algorithm, NetMHCpan-4.0, is taking length preferences for naturally eluted ligands directly into consideration during training, and which provides a simpler implementation of length-specific scores, and shows similar improvements in the identification of neo-epitopes. 44,54,55 …”
Section: Discussionmentioning
confidence: 99%
“…Bioinformatics tools have been developed for different aspects of immunological features such as epitope prediction and mapping, molecular modeling and structural vaccinology (Sirskyj et al 2011). These tools can predict the highly immunogenic epitopes in a short time with high specificity, and can be used for development of an effective vaccine (Jurtz et al 2017). In this study, to design a novel therapeutic vaccine against major high risk HPV types 16, 18, 31 and 45, we used both sequence-based and structural vaccinology immunoinformatics tools.…”
Section: Introductionmentioning
confidence: 99%