Structural Mining:  Self-Consistent Design on Flexible Protein−Peptide Docking and Transferable Binding Affinity Potential

Date Published:

2004

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 R2 = 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 Å dRMS (and in many cases less than 1.0 Å) from the experimentally determined structures.

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