ZRANK: Reranking protein docking predictions with an optimized energy function

Authors

  • Brian Pierce,

    1. Bioinformatics Program, Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
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  • Zhiping Weng

    Corresponding author
    1. Bioinformatics Program, Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
    • Bioinformatics Program, Department of Biomedical Engineering, Boston University, Boston, MA 02215
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Abstract

Protein–protein docking requires fast and effective methods to quickly discriminate correct from incorrect predictions generated by initial-stage docking. We have developed and tested a scoring function that utilizes detailed electrostatics, van der Waals, and desolvation to rescore initial-stage docking predictions. Weights for the scoring terms were optimized for a set of test cases, and this optimized function was then tested on an independent set of nonredundant cases. This program, named ZRANK, is shown to significantly improve the success rate over the initial ZDOCK rankings across a large benchmark. The amount of test cases with No. 1 ranked hits increased from 2 to 11 and from 6 to 12 when predictions from two ZDOCK versions were considered. ZRANK can be applied either as a refinement protocol in itself or as a preprocessing stage to enrich the well-ranked hits prior to further refinement. Proteins 2007. © 2007 Wiley-Liss, Inc.

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