title: Limitations of Ab initio predictions of peptide binding to MHC class II molecules.
abstract: Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.
authors.affiliation: Center for Biological Sequence Analysis, Department for Systems Biology, Technical University of Denmark, Lyngby, Denmark.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
authors.forename: Nikitas
authors.initials: N
authors.lastname: Papangelopoulos
authors.forename: Ying
authors.initials: Y
authors.lastname: Xu
authors.forename: Alessandro
authors.initials: A
authors.lastname: Sette
authors.forename: Philip E
authors.initials: PE
authors.lastname: Bourne
authors.forename: Ole
authors.initials: O
authors.lastname: Lund
authors.forename: Julia
authors.initials: J
authors.lastname: Ponomarenko
authors.forename: Morten
authors.initials: M
authors.lastname: Nielsen
authors.forename: Bjoern
authors.initials: B
authors.lastname: Peters
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D030562:Databases, Protein; D000939:Epitopes / Q000737:chemistry* / Q000378:metabolism; D015234:HLA-A Antigens / Q000737:chemistry / Q000378:metabolism; D059811:HLA-DRB1 Chains; D000949:Histocompatibility Antigens Class II / Q000737:chemistry* / Q000378:metabolism; D006801:Humans; D006860:Hydrogen Bonding; D057927:Hydrophobic and Hydrophilic Interactions; D056004:Molecular Dynamics Simulation; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism; D011485:Protein Binding; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D000939:Epitopes; D015234:HLA-A Antigens; D059811:HLA-DRB1 Chains; C487961:HLA-DRB1*01:01 antigen; D000949:Histocompatibility Antigens Class II; D010455:Peptides