Research Article

Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6–12

  1. Sidhartha Chaudhury1,†,
  2. Aroop Sircar2,†,
  3. Arvind Sivasubramanian2,
  4. Monica Berrondo2,
  5. Jeffrey J. Gray1,2,*

Article first published online: 25 SEP 2007

DOI: 10.1002/prot.21731

Issue

Proteins: Structure, Function, and Bioinformatics

Proteins: Structure, Function, and Bioinformatics

Special Issue: Third Meeting on the Critical Assessment of PRedicted Interactions

Volume 69, Issue 4, pages 793–800, December 2007

Additional Information

How to Cite

Chaudhury, S., Sircar, A., Sivasubramanian, A., Berrondo, M. and Gray, J. J. (2007), Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6–12. Proteins: Structure, Function, and Bioinformatics, 69: 793–800. doi: 10.1002/prot.21731

Author Information

  1. 1

    Program in Molecular and Computational Biophysics, Johns Hopkins University, Baltimore, Maryland 21218

  2. 2

    Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218

  1. †

    Sidhartha Chaudhury and Aroop Sircar contributed equally to this work.

*Department of Chemical and Biomolecular Engineering, Johns Hopkins University, 3400 N. Charles St., Baltimore, MD 21218

Publication History

  1. Issue published online: 31 OCT 2007
  2. Article first published online: 25 SEP 2007
  3. Manuscript Accepted: 28 JUN 2007
  4. Manuscript Revised: 26 JUN 2007
  5. Manuscript Received: 31 MAY 2007

Funded by

  • National Institute of Health (NIH). Grant Numbers: R01 GM078221, T32 GM008403

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Keywords:

  • protein–protein docking;
  • RosettaDock;
  • Rosetta;
  • CAPRI;
  • protein structure prediction;
  • protein flexibility;
  • induced fit binding

Abstract

In CAPRI rounds 6–12, RosettaDock successfully predicted 2 of 5 unbound–unbound targets to medium accuracy. Improvement over the previous method was achieved with computational mutagenesis to select decoys that match the energetics of experimentally determined hot spots. In the case of Target 21, Orc1/Sir1, this resulted in a successful docking prediction where RosettaDock alone or with simple site constraints failed. Experimental information also helped limit the interacting region of TolB/Pal, producing a successful prediction of Target 26. In addition, we docked multiple loop conformations for Target 20, and we developed a novel flexible docking algorithm to simultaneously optimize backbone conformation and rigid-body orientation to generate a wide diversity of conformations for Target 24. Continued challenges included docking of homology targets that differ substantially from their template (sequence identity <50%) and accounting for large conformational changes upon binding. Despite a larger number of unbound–unbound and homology model binding targets, Rounds 6–12 reinforced that RosettaDock is a powerful algorithm for predicting bound complex structures, especially when combined with experimental data Proteins 2007. © 2007 Wiley-Liss, Inc.

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