Motivation: Protein-protein docking algorithms typically generate large numbers of possible complex structures with only a few of them resembling the native structure. Recently (Duan et al., Protein Sci, 14:316-218, 2005), it was observed that the surface density of conserved residue positions is high at the interface regions of interacting protein surfaces, except for antibody-antigen complexes, where a lesser number of conserved positions than average is observed at the interface regions. Using this observation, we identified putative interacting regions on the surface of interacting partners and significantly improved docking results by assigning top ranks to near-native complex structures. In this paper, we combine the residue conservation information with a widely used shape complementarity algorithm to generate candidate complex structures with a higher percentage of near-native structures (hits). What is new in this work is that the conservation information is used early in the generation stage and not only in the ranking stage of the docking algorithm. This results in a significantly larger number of generated hits and an improved predictive ability in identifying the native structure of protein-protein complexes. Results: We report on results from 48 well-characterized protein complexes, which have enough residue conservation information from the same 59 benchmark complexes used in our previous work. We compute conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences from UNIPROT and calculate the solvent accessible surface area. We combine this information with shape-complementarity scores to generate candidate protein-protein complex structures. When compared with pure shape-complementarity algorithms, performed by FTDock, our method results in significantly more hits, with the improvement being over 100% in many instances. We demonstrate that residue conservation information is useful not only in refinement and scoring of docking solutions, but also helpful in enrichment of near-native-structures during the generation of candidate geometries of complex structures.
|Original language||English (US)|
|Number of pages||14|
|Journal||Journal of Bioinformatics and Computational Biology|
|State||Published - Aug 2006|
Bibliographical noteFunding Information:
This work was supported in part of the National Science Foundation (BES-0425882). We thank David Breslauer for helping with code development, while he was an intern at the University of Minnesota Bioinformatics Summer Institute (EEC-0234112). This work is also supported by the Army High Performance Computing Research Center (AHPCRC) under the auspices of the Department of the Army, Army Research Laboratory, contract number DAAD10-01-2-0014. The content does not necessarily reflect the position or the policy of the government and no official endorsement should be inferred. The National Computational Science Alliance (TG-MCA04N033) and the Minnesota Supercomputing Institute provided access to computing facilities.
- Molecular recognition
- Protein complexes
- Protein evolution
- Protein-protein interaction
- Sequence conservation