Improving ranking of models for protein complexes with side chain modeling and atomic potentials

Authors

  • Shruthi Viswanath,

    1. Department of Computer Science, University of Texas, Austin, Texas 78712
    2. Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712
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  • D. V. S. Ravikant,

    1. WalmartLabs, San Bruno, California 94066
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  • Ron Elber

    Corresponding author
    1. Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas 78712
    2. Department of Chemistry and Biochemistry, University of Texas, Austin, Texas 78712
    • 201 East 24th St. STOP C0200, Austin TX 78712-1229
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Abstract

An atomically detailed potential for docking pairs of proteins is derived using mathematical programming. A refinement algorithm that builds atomically detailed models of the complex and combines coarse grained and atomic scoring is introduced. The refinement step consists of remodeling the interface side chains of the top scoring decoys from rigid docking followed by a short energy minimization. The refined models are then re-ranked using a combination of coarse grained and atomic potentials. The docking algorithm including the refinement and re-ranking, is compared favorably to other leading docking packages like ZDOCK, Cluspro, and PATCHDOCK, on the ZLAB 3.0 Benchmark and a test set of 30 novel complexes. A detailed analysis shows that coarse grained potentials perform better than atomic potentials for realistic unbound docking (where the exact structures of the individual bound proteins are unknown), probably because atomic potentials are more sensitive to local errors. Nevertheless, the atomic potential captures a different signal from the residue potential and as a result a combination of the two scores provides a significantly better prediction than each of the approaches alone. Proteins 2013. © 2012 Wiley Periodicals, Inc.

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