In silico prediction of peptide-MHC binding affinity using SVRMHC.
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.
Liu
Wen
W
Department of Neuroscience, University of Minnesota, Minneapolis, USA.
Wan
Ji
J
Meng
Xiangshan
X
Flower
Darren R
DR
Li
Tongbin
T
D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology; D000939:Epitopes / Q000737:chemistry / Q000378:metabolism; D015395:Histocompatibility Antigens Class I / Q000378:metabolism; D000949:Histocompatibility Antigens Class II / Q000378:metabolism; D006801:Humans; D007125:Immunogenetics; D008285:Major Histocompatibility Complex; D010455:Peptides / Q000737:chemistry / Q000276:immunology / Q000378:metabolism*; D011485:Protein Binding
D000939:Epitopes; D015395:Histocompatibility Antigens Class I; D000949:Histocompatibility Antigens Class II; D010455:Peptides