title: EpicCapo: epitope prediction using combined information of amino acid pairwise contact potentials and HLA-peptide contact site information.
abstract: BACKGROUND
  Epitope identification is an essential step toward synthetic vaccine development since epitopes play an important role in activating immune response. Classical experimental approaches are laborious and time-consuming, and therefore computational methods for generating epitope candidates have been actively studied. Most of these methods, however, are based on sophisticated nonlinear techniques for achieving higher predictive performance. The use of these techniques tend to diminish their interpretability with respect to binding potential: that is, they do not provide much insight into binding mechanisms.
  
  
  RESULTS
  We have developed a novel epitope prediction method named EpicCapo and its variants, EpicCapo(+) and EpicCapo(+REF). Nonapeptides were encoded numerically using a novel peptide-encoding scheme for machine learning algorithms by utilizing 40 amino acid pairwise contact potentials (referred to as AAPPs throughout this paper). The predictive performances of EpicCapo(+) and EpicCapo(+REF) outperformed other state-of-the-art methods without losing interpretability. Interestingly, the most informative AAPPs estimated by our study were those developed by Micheletti and Simons while previous studies utilized two AAPPs developed by Miyazawa & Jernigan and Betancourt & Thirumalai. In addition, we found that all amino acid positions in nonapeptides could effect on performances of the predictive models including non-anchor positions. Finally, EpicCapo(+REF) was applied to identify candidates of promiscuous epitopes. As a result, 67.1% of the predicted nonapeptides epitopes were consistent with preceding studies based on immunological experiments.
  
  
  CONCLUSIONS
  Our method achieved high performance in testing with benchmark datasets. In addition, our study identified a number of candidates of promiscuous CTL epitopes consistent with previously reported immunological experiments. We speculate that our techniques may be useful in the development of new vaccines. The R implementation of EpicCapo(+REF) is available at http://pirun.ku.ac.th/~fsciiok/EpicCapoREF.zip. Datasets are available at http://pirun.ku.ac.th/~fsciiok/Datasets.zip.
authors.affiliation: Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan. thammakorn.kmutt@gmail.com
authors.forename: Thammakorn
authors.initials: T
authors.lastname: Saethang
authors.forename: Osamu
authors.initials: O
authors.lastname: Hirose
authors.forename: Ingorn
authors.initials: I
authors.lastname: Kimkong
authors.forename: Vu Anh
authors.initials: VA
authors.lastname: Tran
authors.forename: Xuan Tho
authors.initials: XT
authors.lastname: Dang
authors.forename: Lan Anh T
authors.initials: LA
authors.lastname: Nguyen
authors.forename: Tu Kien T
authors.initials: TK
authors.lastname: Le
authors.forename: Mamoru
authors.initials: M
authors.lastname: Kubo
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Yamada
authors.forename: Kenji
authors.initials: K
authors.lastname: Satou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000032:analysis; D000939:Epitopes / Q000032:analysis* / Q000737:chemistry / Q000276:immunology / Q000378:metabolism; D006680:HLA Antigens / Q000032:analysis / Q000737:chemistry / Q000276:immunology / Q000378:metabolism; D006801:Humans; D009980:Influenza A virus / Q000276:immunology; D007252:Influenza Vaccines / Q000276:immunology; D011485:Protein Binding; D060388:Support Vector Machine; D013602:T-Lymphocytes, Cytotoxic / Q000276:immunology
chemical_list: D000596:Amino Acids; D000939:Epitopes; D006680:HLA Antigens; D007252:Influenza Vaccines