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J Comput Chem. 2011 Apr 15;32(5):787-96. doi: 10.1002/jcc.21657. Epub 2010 Oct 12.

Using correlated parameters for improved ranking of protein-protein docking decoys.

Author information

1
Bioinformatics Centre, Indian Institute of Science, Bangalore, Karnataka, India.

Abstract

A successful protein-protein docking study culminates in identification of decoys at top ranks with near-native quaternary structures. However, this task remains enigmatic because no generalized scoring functions exist that effectively infer decoys according to the similarity to near-native quaternary structures. Difficulties arise because of the highly irregular nature of the protein surface and the significant variation of the nonbonding and solvation energies based on the chemical composition of the protein-protein interface. In this work, we describe a novel method combining an interface-size filter, a regression model for geometric compatibility (based on two correlated surface and packing parameters), and normalized interaction energy (calculated from correlated nonbonded and solvation energies), to effectively rank decoys from a set of 10,000 decoys. Tests on 30 unbound binary protein-protein complexes show that in 16 cases we can identify at least one decoy in top three ranks having ≤10 Å backbone root mean square deviation from true binding geometry. Comparisons with other state-of-art methods confirm the improved ranking power of our method without the use of any experiment-guided restraints, evolutionary information, statistical propensities, or modified interaction energy equations. Tests on 118 less-difficult bound binary protein-protein complexes with ≤35% sequence redundancy at the interface showed that in 77% cases, at least 1 in 10,000 decoys were identified with ≤5Å backbone root mean square deviation from true geometry at first rank. The work will promote the use of new concepts where correlations among parameters provide more robust scoring models. It will facilitate studies involving molecular interactions, including modeling of large macromolecular assemblies and protein structure prediction.

PMID:
20941737
DOI:
10.1002/jcc.21657
[Indexed for MEDLINE]

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