Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors.
Major histocompatibility complex (MHC) molecules bind short peptides resulting from intracellular processing of foreign and self proteins, and present them on the cell surface for recognition by T-cell receptors. We propose a new robust approach to quantitatively model the binding affinities of MHC molecules by quantitative structure-activity relationships (QSAR) that use the physical-chemical amino acid descriptors E1-E5. These QSAR models are robust, sequence-based, and can be used as a fast and reliable filter to predict the MHC binding affinity for large protein databases.
Sealy Center for Structural Biology and Molecular Biophysics, Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas 77555-0857, USA.
Ovidiu
O
Ivanciuc
Werner
W
Braun
D000595:Amino Acid Sequence; D017951:Antigen Presentation; D000941:Antigens / Q000737:chemistry / Q000378:metabolism*; D002138:Calibration; D006649:Histocompatibility Antigens / Q000737:chemistry / Q000378:metabolism*; D016018:Least-Squares Analysis; D016014:Linear Models; D008433:Mathematics; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D009842:Oligopeptides / Q000737:chemistry / Q000276:immunology / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship
D000941:Antigens; D006649:Histocompatibility Antigens; C408902:MHC binding peptide; D009842:Oligopeptides