Abstract
The development of an adequate immune response against pathogens is mediated by molecular interactions between different cell types. Among them, binding of antigenic peptides to the Major Histocompatibility Complex (MHC) molecule expressed on the membrane of antigen presenting cells (APCs), and their subsequent recognition by the T cell receptor have been demonstrated to be crucial for developing an adequate immune response. The present review compiles computational quantum chemistry studies about the electrostatic potential variations induced on the MHC binding region by peptide’s amino acids, carried out with the aim of describing MHC–peptide binding interactions. The global idea is that the electrostatic potential can be represented in terms of a series expansion (charge, dipole, quadrupole, hexadecapole, etc.) whose three first terms provide a good local approximation to the molecular electrostatic ‘landscape’ and to the variations induced on such landscape by targeted modifications on the residues of the antigenic peptide. Studies carried out in four MHC class II human allele molecules, which are the most representative alleles of their corresponding haplotypes, showed that each of these molecules have conserved as well as specific electrostatic characteristics, which can be correlated at a good extent with the peptide binding profiles reported experimentally for these molecules. The information provided by such characteristics would help increase our knowledge about antigen binding and presentation, and could ultimately contribute to developing a logical and rational methodology for designing chemically synthesized, multi-antigenic, subunit-based vaccines, through the application of quantum chemistry methods.
Highlights
In general terms, presentation of antigenic peptides to the TCR, a key process for inducing an adequate immune response against any foreign antigen, is mainly driven by two types of molecules encoded by the Major Histocompatibility Complex (MHC) genes located on the short arm of chromosome 6: (a) Class I molecules (MHCI) expressed by all nucleated cells, and (b) Class II MHC molecules (MHCII) expressed only by antigen presenting cells (APCs), which include macrophages, dendritic cells, Langerhans’ cells, B lymphocytes, monocytes, etc.MHCI and MHCII molecules are structurally and functionally different
This model has been applied to four MHC class II human molecules (HLA-DR1* alleles) for which a crystallographic model is available in the PDB protein data bank: HLADR1*0101 (PDB: 1dlh), Human Leukocyte Antigens (HLA)-DR1*0401 (PDB: 1j8h and 2seb), HLA-DR1*0301 (PDB: 1a6a) and HLA-DR1*1501 (PBD: 1bx2) [12,13,14,15,16,17]
Two methods have been commonly used for estimating molecular electrostatic properties: methods based on the Poisson-Boltzmann equation (PBE) and methods based on the electron density
Summary
Presentation of antigenic peptides to the TCR, a key process for inducing an adequate immune response against any foreign antigen, is mainly driven by two types of molecules encoded by the MHC genes located on the short arm of chromosome 6: (a) Class I molecules (MHCI) expressed by all nucleated cells, and (b) Class II MHC molecules (MHCII) expressed only by antigen presenting cells (APCs), which include macrophages, dendritic cells, Langerhans’ cells, B lymphocytes, monocytes, etc. Binding profiles describe which amino acids are most frequently found occupying a particular position in peptides binding to a specific MHCII allele and are used for building scoring systems (score matrices [2]) These score matrices are in turn used for designing linear prediction schemes based on the following hypotheses: first, that each position within the peptide contributes independently to the binding interaction; and second, that residues located on a given position within a peptide contribute to peptide binding, even if they belong to different peptides. Structure-based models are constructed based on the information gathered from the crystal structures of MHC molecules loaded with a particular antigenic peptide, and do not need to be fed with large amounts of binding data. For each HLA-DR1* pocket, we have been able to identify which peptide amino acids would have a significant interaction, obtaining a very good agreement with in vitro results
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