title: Class II HLA-peptide binding prediction using structural principles.
abstract: The precise prediction of class II human leukocyte antigen (HLA) peptide binding finds application in epitope design for the development of vaccines and diagnostics of diseases associated with CD4+ T-cellular immunity. HLA II binding peptides have an extended conformation at the binding groove unlike class I. This increases peptide binding combinations of varying length at the groove, having an eventual effect in the host immune response to infectious agents. Here we describe the development of a prediction model using information gleaned from HLA II-peptide (HLA II-p) structural data. We created a manually curated dataset of 15 HLA II-p structural complexes from Protein databank (PDB). The dataset was used to develop virtual binding pockets for accommodating HLA-II-specific short peptides. The binding of peptides to the virtual pockets is estimated using the Q matrix (a quantitative matrix based on amino acid residue properties). Internal cross-validation of the model using the 15 HLA II-p structural complexes produced an accuracy of 53% with a sensitivity of 53%. The model was further evaluated using a dataset of 3676 class II-specific peptides consisting of 1188 binders and 2488 nonbinders derived from MHCBN (a database of HLA binders and nonbinders). The model produced an accuracy of 53% with 70.8% specificity and 27.6% sensitivity. The positive predictive value (PPV) was 62% and the negative predictive value (NPV) 58%. A 62% PPV suggests that the model fairly predicts a good number of binders among predicted binders and thus that the success rate among predicted binder for further verification is good. The described model is simple and rapid, with large HLA allele coverage representing the sampled global population, despite weak prediction accuracy. The ability of the model to predict a wide array of defined class II alleles is found to be applicable for proteome-wide scanning of parasitic genomes.
authors.affiliation: School of Biotechnology, Chemical and Biomedical Engineering, Vellore Institute of Technology University, Tamil Nadu, India.
authors.forename: Arumugam
authors.initials: A
authors.lastname: Mohanapriya
authors.forename: Sajitha
authors.initials: S
authors.lastname: Lulu
authors.forename: Rajarathinam
authors.initials: R
authors.lastname: Kayathri
authors.forename: Pandjassarame
authors.initials: P
authors.lastname: Kangueane
mesh_terms: D000483:Alleles; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology; D030562:Databases, Protein; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000276:immunology* / Q000378:metabolism*; D005544:Forecasting / Q000379:methods; D000949:Histocompatibility Antigens Class II / Q000737:chemistry / Q000276:immunology* / Q000378:metabolism; D006801:Humans; D007111:Immunity, Cellular; D018448:Models, Immunological; D010455:Peptides / Q000737:chemistry / Q000276:immunology* / Q000378:metabolism; D011485:Protein Binding; D011487:Protein Conformation; D054730:Protein Interaction Domains and Motifs / Q000276:immunology*; D012680:Sensitivity and Specificity
chemical_list: D018984:Epitopes, T-Lymphocyte; D000949:Histocompatibility Antigens Class II; D010455:Peptides