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 rector 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.
CITATION STYLE
Liu, W., Wan, J., Meng, X., Flower, D. R., & Li, T. (2007). In silico prediction of peptide-MHC binding affinity using SVRMHC. Methods in Molecular Biology (Clifton, N.J.), 409, 283–291. https://doi.org/10.1007/978-1-60327-118-9_20
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