Structural templates were searched in the PDB using PSI-BLAST. (http://www.ncbi.nih.gov/blast) at an E value cut-off
12 Grouping of Class I HLA Alleles Using Electrostatic Distribution Maps of the Peptide Binding Grooves Pandjassarame Kangueane and Meena Kishore Sakharkar
Summary Human leukocyte antigen (HLA) molecules involved in immune function by binding to short peptides (8–20 residues) have different sequences in different individuals belonging to distinct ethnic population. Hence, the peptide-binding function of HLA alleles is specific. Class I HLA alleles (alternative forms of a gene) are associated with CD8+ T cells, and their allele-specific sequence information is available at the IMGT/HLA database. The available sequences are onedimensional (1D), and the peptide-binding functional inference often requires 3-dimensional (3D) structural models of respective alleles. Hence, 3D structures were constructed for 1,000 class I HLA alleles (310 A, 570 B, and 120 C) using MODELLER (a comparative protein modeling program for modeling protein structures). The electrostatic distribution maps were generated for each modeled structure using Deep View (Swiss PDB Viewer Version 3.7). The 1,000 models were then grouped into different categories by visual inspection of their electrostatic distribution maps in the peptide binding grooves. The distribution of the models based on electrostatic distribution was 30% negative (300), 1% positive (12), 8% neutral (84), and 60% (604) mixed (random mixture of negative, positive, and neutral). This grouping provides insight toward the inference for functional overlap among HLA alleles.
Key Words: HLA; alleles; grouping; peptide binding groove; electrostatic potential; negative; positive; neutral
1. Introduction Human leukocyte antigen (HLA) proteins are involved in T-cell-mediated immune response by binding to short peptides of 8–20 residues long (1,2). The binding of HLA molecules to peptides is highly specific. However, HLA alleles From: Methods in Molecular Biology, vol. 409: Immunoinformatics: Predicting Immunogenicity In Silico Edited by: D. R. Flower © Humana Press Inc., Totowa, NJ
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are highly polymorphic among different ethnic groups, and more than 1,500 HLA alleles are known (3). Therefore, HLA–peptide binding is combinational in nature. Nonetheless, peptide binding is determined by groove geometry and chemistry. Zhang et al. (4) grouped HLA alleles using structural pockets. Chelvanayagam (5,6) classified class I and DR alleles using pocket information. Recently, Zhao et al. (7), Kangueane et al. (8), and Guan et al (9) developed A–F pocket systems for grouping HLA molecules. Doytchinova et al. (10) used molecular interaction fields (MIF), hierarchical clustering (HC), and principal component analysis (PCA), and Lund et al. (11) used clustering procedures for grouping HLA alleles into putative supertypes (where different members bind similar peptides, yet exhibiting distinct repertoires). Here, we describe a novel methodology to group HLA alleles using electrostatic distribution map of the peptide binding groove in HLA molecules. 2. Methodology The procedure used in this analysis is outlined in Fig. 1 using a work flow diagram. The work flow diagram describes the different steps involved in HLA modeling, electrostatic calculation for the model, and grouping of HLA alleles. 2.1. HLA modeling HLA modeling consists of (1) identification of suitable templates from Protein databank (PDB), (2) selection of structural templates, (3) targetto-template alignment, (4) model building, and (5) generation of 3D models. Structural templates were searched in the PDB using PSI-BLAST (http://www.ncbi.nih.gov/blast) at an E value cut-off
