Efficient Docking Algorithm Using Conserved Residue Information to Study Protein-Protein Interactions

摘要

Many protein-protein docking algorithms generate numerous possible complex structures with only a few of them resembling the native structure. The major challenge is choosing the near-native structures from the generated set. Recently it has been observed that the density of conserved residue positions is higher at the interface regions of interacting protein surfaces, except for antibody-antigen complexes, where a very low number of conserved positions is observed at the interface regions. In the present study we have used this observation to identify putative interacting regions on the surface of interacting partners. We studies 59 protein complexes, used previously as a benchmark dataset for docking investigations. We computed conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences of interacting proteins using available homologous sequences and used this information to filter out from 55% to 88% of generated docked models, retaining near-native structures for further evaluation. We used a reverse filter of conservation score to filter out the majority of non-native antigen-antibody complex structures. For each docked model in the filtered subsets, we relaxed the conformation of the side chains by minimizing the energy with CHARMM. We then calculated the binding free energy using a generalized Born method and solvent accessible surface area calculations. Using the free energy along with conservation information and other descriptors used in the literature for ranking docking solutions, such as shape complementarity and pair-potentials, we developed a global ranking procedure that significantly improves the docking results by giving top ranks to the near-native complex structures.