Published online 31 May 2006
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Protein complex compositions predicted by structural similarity
1 Department of Biopharmaceutical Sciences, California Institute for Quantitative Biomedical Research, University of California San Francisco, 1700 4th Street, Byers Hall, San Francisco, CA 94143-2552, USA 2 Department of Pharmaceutical Chemistry, California Institute for Quantitative Biomedical Research, University of California San Francisco, 1700 4th Street, Byers Hall, San Francisco, CA 94143-2552, USA
*To whom correspondence should be addressed. Tel: + 1 415 514 4232; Fax: +1 415 514 4231; Email: madhu{at}salilab.org
*Correspondence may also be addressed to A. Sali. Tel: +1 415 514 4227; Fax: +1 415 514 4231; Email: sali{at}salilab.org
Received March 15, 2006. Revised April 1, 2006. Accepted April 20, 2006.
Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (http://salilab.org/pibase). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain—porcine 
–amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE (http://salilab.org/modbase).
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