title: Structural mining: self-consistent design on flexible protein-peptide docking and transferable binding affinity potential.
abstract: A flexible protein-peptide docking method has been designed to consider not only ligand flexibility but also the flexibility of the protein. The method is based on a Monte Carlo annealing process. Simulations with a distance root-mean-square (dRMS) virtual energy function revealed that the flexibility of protein side chains was as important as ligand flexibility for successful protein-peptide docking. On the basis of mean field theory, a transferable potential was designed to evaluate distance-dependent protein-ligand interactions and atomic solvation energies. The potential parameters were developed using a self-consistent process based on only 10 known complex structures. The effectiveness of each intermediate potential was judged on the basis of a Z score, approximating the gap between the energy of the native complex and the average energy of a decoy set. The Z score was determined using experimentally determined native structures and decoys generated by docking with the intermediate potentials. Using 6600 generated decoys and the Z score optimization criterion proposed in this work, the developed potential yielded an acceptable correlation of R(2) = 0.77, with binding free energies determined for known MHC I complexes (Class I Major Histocompatibility protein HLA-A(*)0201) which were not present in the training set. Test docking on 25 complexes further revealed a significant correlation between energy and dRMS, important for identifying native-like conformations. The near-native structures always belonged to one of the conformational classes with lower predicted binding energy. The lowest energy docked conformations are generally associated with near-native conformations, less than 3.0 Angstrom dRMS (and in many cases less than 1.0 Angstrom) from the experimentally determined structures.
authors.affiliation: Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA.
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Liu
authors.forename: Brian N
authors.initials: BN
authors.lastname: Dominy
authors.forename: Eugene I
authors.initials: EI
authors.lastname: Shakhnovich
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D015234:HLA-A Antigens / Q000737:chemistry / Q000378:metabolism; D015789:HLA-A2 Antigen; D007700:Kinetics; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009010:Monte Carlo Method; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012996:Solutions; D013816:Thermodynamics; D018909:src Homology Domains
chemical_list: D015234:HLA-A Antigens; C435939:HLA-A*02:01 antigen; D015789:HLA-A2 Antigen; D010455:Peptides; D011506:Proteins; D012996:Solutions