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

What you can do:
Predicts binding of peptides to a number of different HLA alleles using artificial neural networks (ANNs) and weight matrices.
  • 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 IC(50) 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.
  • artificial neural networks
  • MHC
  • major histocompatibility complex
  • peptide
  • immune epitope
This record last updated: 07-29-2008
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