Sidhartha Chaudhury and Aroop Sircar contributed equally to this work.
Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6–12
Article first published online: 25 SEP 2007
Copyright © 2007 Wiley-Liss, Inc.
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
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, 69: 793–800. doi: 10.1002/prot.21731
- Issue published online: 31 OCT 2007
- Article first published online: 25 SEP 2007
- Manuscript Accepted: 28 JUN 2007
- Manuscript Revised: 26 JUN 2007
- Manuscript Received: 31 MAY 2007
- National Institute of Health (NIH). Grant Numbers: R01 GM078221, T32 GM008403
- protein–protein docking;
- protein structure prediction;
- protein flexibility;
- induced fit binding
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.