Title :
Peptide binding to major histocompatibility complex
Author :
Rajapakse, Menaka ; Feng, Lin
Author_Institution :
Lilly Singapore Center for Drug Discovery, Singapore, Singapore
Abstract :
Peptide binding to major histocompatibility complex (MHC) molecules is a prerequisite for initiating an immune response. This article first describes an approach to predict MHC-peptide-binding sites by using an evolutionary algorithm (EA). The predicted binders are subsequently characterized for their physicochemical properties. The details and implementation issues of the peptide-binding prediction technique are discussed, and the performance comparison with the existing methods is provided. The binding motif derived in silico is used to characterize the physicochemical properties of the experimentally determined binders.
Keywords :
biochemistry; biology computing; evolutionary computation; molecular biophysics; proteins; evolutionary algorithm; immune response; major histocompatibility complex; peptide binding; physicochemical properties; Artificial neural networks; Cancer; Evolutionary computation; Hidden Markov models; Immune system; Peptides; Predictive models; Sequences; Support vector machine classification; Support vector machines; Algorithms; Amino Acids; Animals; Artificial Intelligence; Computational Biology; Computer Simulation; Histocompatibility Antigens Class II; Humans; Hydrophobicity; Major Histocompatibility Complex; Mice; Mice, Inbred NOD; Models, Biological; Models, Genetic; Models, Immunological; Peptides; Reproducibility of Results; Thermodynamics;
Journal_Title :
Engineering in Medicine and Biology Magazine, IEEE
DOI :
10.1109/MEMB.2009.932922
