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

By M.N. Davies, C.K. Hattotuwagama, David S. Moss, M.G.B. Drew and D.R. Flower


Background:\ud 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.\ud \ud Results:\ud A large dataset comprising MHC-peptide structural complexes was created by re-modelling 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.\ud \ud Conclusion:\ud 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

Topics: bcs
Publisher: Springer
Year: 2006
OAI identifier:

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  1. (2002). Abe N: Prediction of MHC class I binding peptides by a query learning algorithm based on hidden Markov models. doi
  2. AMBER 6. Univ of California,
  3. (2001). Biddison WE: Identification of a crucial energetic footprint on the alpha1 helix of human histocompatibility leukocyte antigen (HLA)-A2 that provides functional interactions for recognition by tax peptide/HLA-A2-specific T cell receptors. doi
  4. BM: Strategic mutations in the class I
  5. (2004). BM: Strategic mutations in the class I major histocompatibility complex HLA-A2 independently affect both peptide binding and T cell receptor recognition. doi
  6. (2003). BM: Thermodynamic and kinetic analysis of a peptide-class I MHC interaction highlights the noncovalent nature and conformational dynamics of the class I heterotrimer. Biochemistry doi
  7. (1988). Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies. Quant Struct-Act Relat doi
  8. Bootstrapping, and Partial Least Squares Compared with Multiple Regression in Conventional QSAR Studies. Quant Struct-Act Relat 1988, 7:18-25.Page 13 of 13 (page number not for citation purposes) doi
  9. (2002). Comparative binding energy (COMBINE) analysis of influenza neuraminidase-inhibitor complexes. doi
  10. (2002). Comparative binding energy (COMBINE) analysis of OppA-peptide complexes to relate structure to binding thermodynamics. doi
  11. (2001). DC: Crystal structures of two closely related but anti-Page 12 of 13 (page number not for citation purposes) MHC – Major Histocompatibility Complex genically distinct HLA-A2/melanocyte-melanoma tumorantigen peptide complexes. doi
  12. (2001). DC: Crystal structures of two closely related but antigenically distinct HLA-A2/melanocyte-melanoma tumorantigen peptide complexes. doi
  13. (1991). DC: Refined structure of the human histocompatibility antigen HLA-A2 at 2.6Å resolution. doi
  14. (1993). DC: The antigenic identity of peptide-MHC complexes: a comparison of the conformations of five viral peptides presented by HLA-A2. Cell doi
  15. (2004). DR: Coupling identifiying human MHC supertypes using bioinformatic methods. doi
  16. (2004). DR: Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes. doi
  17. (2002). DR: JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics doi
  18. (2003). DR: JenPep: A novel computational information resource for immunology and vaccinology. doi
  19. (2004). DR: New Horizons in Mouse Immunoinformatics: Reliable In Silico Prediction of Mouse Class I Histocompatibility Major Complex Peptide Binding Affinity. Org Biomolec Chem doi
  20. (2004). DR: Quantitative online prediction of peptide binding to the major histocompatibility complex. doi
  21. (2005). DR: Towards the chemometric dissection of peptide – HLA-A*0201 binding affinity: comparison of local and global QSAR models. J Comput Aided Mol Des doi
  22. (2002). Elofsson A: Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics doi
  23. Flower DR: Class II Mouse Major Histocompatibility Complex Peptide Binding Affinity: In Silico bioinformatic prediction using robust multivariate statistics. Bioinformatics doi
  24. Flower DR: In Silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: A Comparative Molecular Similarity Index Analysis (CoMSIA) study. doi
  25. Flower DR: In Silico QSAR-Based Predictions of Class I and Class II MHC Epitopes. Immunoinformatics: Opportunities and Challenges of Bridging Immunology with Computer and Information Sciences. doi
  26. (2001). Hellinga HW: Manipulation of ligand binding affinity by exploitation of conformational coupling.
  27. (1994). Honig B: Accurate calculation of hydration free energies using macroscopic solvent models. doi
  28. (1994). Hopfinger AJ: Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships. Chem Inf Comput Sci doi
  29. (1997). Hopfinger AJ: Prediction of ligand-receptor binding thermodynamics by free energy force field (FEFF) 3D-QSAR analysis: application to a set of peptidometic renin inhibitors. doi
  30. (2005). Impact of remote mutations on metallo-{beta}-lactamase substrate specificity: Implications for the evolution of antibiotic resistance. Protein Sci doi
  31. (1994). Kubo RT: Peptide binding to the most frequent HLA-A class I alleles measured by quantitative molecular binding assays. Mol Immunol doi
  32. (1998). LC: Prediction of MHC class-II binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics doi
  33. (1983). ML: Comparison of simple potential functions for simulating liquid water. J Chem Phys doi
  34. (1998). Molecular Simulations/Biosym inc.
  35. (1988). Multivariate Adaptive Regression. In Spline doi
  36. (1995). Nicholls A: Classical electrostatics in biology and chemistry. Science doi
  37. (1999). Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism. Immunogenetics doi
  38. (2002). Reinherz EL: Prediction of MHC class I binding peptides using profile motifs. Hum Immunol doi
  39. (2001). Sette A: Majority of peptides binding HLA-A*0201 with high affinity crossreact with other A2-supertype molecules. Hum Immunol doi
  40. (1997). TA: Model-building and refinement practice. Methods Enzymol doi
  41. Tripos Inc.,
  42. (2002). W: Immunoinformatics: mining genomes for vaccine components. Immunol Cell Biol doi

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