title: TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules.
abstract: BACKGROUND
  Accurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules.
  
  
  METHODS
  We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE.
  
  
  RESULTS
  The performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.
authors.affiliation: School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.
authors.forename: Lianming
authors.initials: L
authors.lastname: Zhang
authors.forename: Yiqing
authors.initials: Y
authors.lastname: Chen
authors.forename: Hau-San
authors.initials: HS
authors.lastname: Wong
authors.forename: Shuigeng
authors.initials: S
authors.lastname: Zhou
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Mamitsuka
authors.forename: Shanfeng
authors.initials: S
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D000483:Alleles; D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D018360:Crystallography, X-Ray / Q000379:methods; D000939:Epitopes / Q000737:chemistry*; D005786:Gene Expression Regulation; D006684:HLA-DR Antigens / Q000235:genetics* / Q000276:immunology*; D000949:Histocompatibility Antigens Class II / Q000235:genetics*; D006801:Humans; D008024:Ligands; D015233:Models, Statistical; D019151:Peptide Library; D010455:Peptides / Q000737:chemistry*; D011110:Polymorphism, Genetic; D011485:Protein Binding; D011487:Protein Conformation; D015203:Reproducibility of Results; D013601:T-Lymphocytes / Q000166:cytology
chemical_list: D000939:Epitopes; D006684:HLA-DR Antigens; D000949:Histocompatibility Antigens Class II; D008024:Ligands; D019151:Peptide Library; D010455:Peptides