title: DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations.
abstract: BACKGROUND
  The binding of endogenous antigenic peptides to MHC class I molecules is an important step during the immunologic response of a host against a pathogen. Thus, various sequence- and structure-based prediction methods have been proposed for this purpose. The sequence-based methods are computationally efficient, but are hampered by the need of sufficient experimental data and do not provide a structural interpretation of their results. The structural methods are data-independent, but are quite time-consuming and thus not suited for screening of whole genomes. Here, we present a new method, which performs sequence-based prediction by incorporating information obtained from molecular modeling. This allows us to perform large databases screening and to provide structural information of the results.
  
  
  RESULTS
  We developed a SVM-trained, quantitative matrix-based method for the prediction of MHC class I binding peptides, in which the features of the scoring matrix are energy terms retrieved from molecular dynamics simulations. At the same time we used the equilibrated structures obtained from the same simulations in a simple and efficient docking procedure. Our method consists of two steps: First, we predict potential binders from sequence data alone and second, we construct protein-peptide complexes for the predicted binders. So far, we tested our approach on the HLA-A0201 allele. We constructed two prediction models, using local, position-dependent (DynaPred(POS)) and global, position-independent (DynaPred) features. The former model outperformed the two sequence-based methods used in our evaluation; the latter shows a much higher generalizability towards other alleles than the position-dependent models. The constructed peptide structures can be refined within seconds to structures with an average backbone RMSD of 1.53 A from the corresponding experimental structures.
authors.affiliation: MPI für Informatik, Stuhlsatzenhausweg 85, D-66123 Saarbrücken, Germany. antes@mpi-sb.mpg.de
authors.forename: Iris
authors.initials: I
authors.lastname: Antes
authors.forename: Shirley W I
authors.initials: SW
authors.lastname: Siu
authors.forename: Thomas
authors.initials: T
authors.lastname: Lengauer
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D015395:Histocompatibility Antigens Class I / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D010455:Peptides / Q000737:chemistry*; D011485:Protein Binding; D011487:Protein Conformation; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D015395:Histocompatibility Antigens Class I; D010455:Peptides