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In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC

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Part of the Methods in Molecular Biology™ book series (MIMB,volume 409)

Summary

The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide–MHC binding affinities based on support vector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide–MHC binding.

Key Words

  • SVR
  • SVRMHC
  • epitope binding
  • modeling

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Liu, W., Wan, J., Meng, X., Flower, D.R., Li, T. (2007). In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC. In: Flower, D.R. (eds) Immunoinformatics. Methods in Molecular Biology™, vol 409. Humana Press. https://doi.org/10.1007/978-1-60327-118-9_20

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  • DOI: https://doi.org/10.1007/978-1-60327-118-9_20

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-699-3

  • Online ISBN: 978-1-60327-118-9

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