A generalized approach to sampling backbone conformations with RosettaDock for CAPRI rounds 13–19

Authors

  • Aroop Sircar,

    1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218
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  • Sidhartha Chaudhury,

    1. Program in Molecular and Computational Biophysics, Johns Hopkins University, Baltimore, Maryland 21218
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  • Krishna Praneeth Kilambi,

    1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218
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  • Monica Berrondo,

    1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218
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  • Jeffrey J. Gray

    Corresponding author
    1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218
    2. Program in Molecular and Computational Biophysics, Johns Hopkins University, Baltimore, Maryland 21218
    • Department of Chemical & Biomolecular Engineering, Johns Hopkins University, 3400 N, Charles St., Baltimore, Maryland 21218
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  • The authors state no conflict of interest.

Abstract

In CAPRI rounds 13–19, the most native-like structure predicted by RosettaDock resulted in two high, one medium, and one acceptable accuracy model out of 13 targets. The current rounds of CAPRI were especially challenging with many unbound and homology modeled starting structures. Novel docking methods, including EnsembleDock and SnugDock, allowed backbone conformational sampling during docking and enabled the creation of more accurate models. For Target 32, α-amylase/subtilisin inhibitor-subtilisin savinase, we sampled different backbone conformations at an interfacial loop to produce five high-quality models including the most accurate structure submitted in the challenge (2.1 Å ligand rmsd, 0.52 Å interface rmsd). For Target 41, colicin-immunity protein, we used EnsembleDock to sample the ensemble of nuclear magnetic resonance (NMR) models of the immunity protein to generate a medium accuracy structure. Experimental data identifying the catalytic residues at the binding interface for Target 40 (trypsin-inhibitor) were used to filter RosettaDock global rigid body docking decoys to determine high accuracy predictions for the two distinct binding sites in which the inhibitor interacts with trypsin. We discuss our generalized approach to selecting appropriate methods for different types of docking problems. The current toolset provides some robustness to errors in homology models, but significant challenges remain in accommodating larger backbone uncertainties and in sampling adequately for global searches. Proteins 2010. © 2010 Wiley-Liss, Inc.

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