Dima Kozakov, David R. Hall, Dmitri Beglov, and Ryan Brenke are the joint first authors to this work.
Achieving reliability and high accuracy in automated protein docking: Cluspro, PIPER, SDU, and stability analysis in CAPRI rounds 13–19†
Article first published online: 23 JUL 2010
Copyright © 2010 Wiley-Liss, Inc.
Proteins: Structure, Function, and Bioinformatics
Special Issue: Fourth Meeting on the Critical Assessment of PRedicted Interactions
Volume 78, Issue 15, pages 3124–3130, 15 November 2010
How to Cite
Kozakov, D., Hall, D. R., Beglov, D., Brenke, R., Comeau, S. R., Shen, Y., Li, K., Zheng, J., Vakili, P., Paschalidis, I. Ch. and Vajda, S. (2010), Achieving reliability and high accuracy in automated protein docking: Cluspro, PIPER, SDU, and stability analysis in CAPRI rounds 13–19. Proteins, 78: 3124–3130. doi: 10.1002/prot.22835
The authors state no conflict of interest.
- Issue published online: 23 JUL 2010
- Article first published online: 23 JUL 2010
- Manuscript Accepted: 23 JUN 2010
- Manuscript Revised: 15 JUN 2010
- Manuscript Received: 19 APR 2010
- National Institute for Health. Grant Numbers: GM61867, GM93147
- National Science Foundation. Grant Number: MRI DBI-0116574
- fast Fourier transform;
- energy funnel;
- scoring function;
- global optimization;
- structure refinement;
- pairwise potential;
- docking server;
Our approach to protein—protein docking includes three main steps. First, we run PIPER, a rigid body docking program based on the Fast Fourier Transform (FFT) correlation approach, extended to use pairwise interactions potentials. Second, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the stability of the clusters is analyzed by short Monte Carlo simulations, and the structures are refined by the medium-range optimization method SDU. The first two steps of this approach are implemented in the ClusPro 2.0 protein–protein docking server. Despite being fully automated, the last step is computationally too expensive to be included in the server. When comparing the models obtained in CAPRI rounds 13–19 by ClusPro, by the refinement of the ClusPro predictions and by all predictor groups, we arrived at three conclusions. First, for the first time in the CAPRI history, our automated ClusPro server was able to compete with the best human predictor groups. Second, selecting the top ranked models, our current protocol reliably generates high-quality structures of protein–protein complexes from the structures of separately crystallized proteins, even in the absence of biological information, provided that there is limited backbone conformational change. Third, despite occasional successes, homology modeling requires further improvement to achieve reliable docking results. Proteins 2010. © 2010 Wiley-Liss, Inc.