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.
Ivanciuc
Ovidiu
O
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.
Braun
Werner
W
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