• protein–protein interaction;
  • docking;
  • conservation index;
  • binding free energy;
  • molecular recognition;
  • computer simulations


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 studied 59 protein complexes, used previously as a benchmark data set for docking investigations. We computed conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences and used this information to filter out from 56% to 86% 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 nonnative 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, and 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 near-native complex structures.