A combined bioinformatic approach oriented to the analysis and design of peptides with high affinity to MHC class I molecules.
We report on a new method to compute the antigenic degree of peptides from available experimental data on peptide binding affinity to class I MHC molecules. The methodology is a combination of two strategies at different levels of information. The first, at the primary structure level, consists in expressing the peptides binding activity as a profile of amino acid contributions, amino acid similarity being accounted for by their characteristic physicochemical properties and their position within the sequence. The higher level of the strategy is based on a meticulous analysis of the contact interface of the peptides with the cleft constituting the receptor region of a particular class I MHC molecule. Interaction interfaces are inferred by docking the peptide onto the receptor groove of the MHC molecule; evaluation of the affinity of the peptide to the receptor is then performed by analysis of the electrostatic and hydrophobic energies on points of the interaction interface. The result is a robust system for analysis of peptide affinity to class I MHC molecules since while the first analysis dictates the composition of active sequences at the amino acid level, the second translates this information to the atomic level, where the molecular interaction can be analyzed in terms of the intrinsic interatomic forces and energies. Evaluation results for the methodology are encouraging since high affinity peptides are reflected by high scores at both levels of information, and are proportionally lower for peptides of medium and lower affinity for which interaction surfaces show relatively lower electrostatic complementarity and hydrophobic correlation than for the former.
Del Carpio
Carlos A
CA
Laboratory for Bioinformatics, Departmentof Ecological Engineering, Toyohashi University of Technology, Tempaku, Toyohashi, Japan. carlos@translell.eco.tut.ac.jp
Hennig
Tabea
T
Fickel
Simonetta
S
Yoshimori
Atsushi
A
D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D017076:Computer-Aided Design / Q000295:instrumentation; D003658:Decision Making, Computer-Assisted; D005805:Genes, MHC Class I; D015234:HLA-A Antigens / Q000032:analysis / Q000145:classification / Q000378:metabolism; D015789:HLA-A2 Antigen / Q000032:analysis / Q000145:classification / Q000378:metabolism; D059871:HLA-A24 Antigen; D015395:Histocompatibility Antigens Class I / Q000378:metabolism*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D010455:Peptides / Q000032:analysis* / Q000737:chemistry*; D011485:Protein Binding; D011946:Receptors, Antigen / Q000276:immunology / Q000378:metabolism; D055672:Static Electricity
D000596:Amino Acids; D015234:HLA-A Antigens; D015789:HLA-A2 Antigen; D059871:HLA-A24 Antigen; D015395:Histocompatibility Antigens Class I; D010455:Peptides; D011946:Receptors, Antigen