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Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Davies, M.N., Hattotuwagama, C.K., Moss, D.S., Drew, M.G.B. and Flower, D.R. (2006) Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity. BMC Structural Biology, 6. p. 5. ISSN 1471-2237

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To link to this item DOI: 10.1186/1472-6807-6-5

Abstract/Summary

Background: MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results: A large dataset comprising MHC-peptide structural complexes was created by remodelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion: The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Chemistry, Food and Pharmacy > Department of Chemistry
ID Code:11180
Uncontrolled Keywords:MAJOR HISTOCOMPATIBILITY COMPLEX, IN-SILICO, HLA-A, PREDICTION, MODELS, HLA-A-ASTERISK-0201, THERMODYNAMICS, RECOGNITION, MUTATIONS, ALGORITHM

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