POSTERS - BLAST X - University of Utah

BLAST X Mon. Morning Session
MESSAGE PASSING: PROTEIN STRUCTURE ASSEMBLY FROM SEQUENCE DATA FOR
TWO-COMPONENT SIGNALING PROTEINS
Hendrik Szurmant 1 , Martin Weigt 2,3 , Robert White 1,3 , Terry Hwa 3 and James A. Hoch 1
1
Division of Cellular Biology, Department of Molecular and Experimental Medicine, The Scripps
Research Institute, La Jolla, CA 92037
2
Institute for Scientific Interchange, I-10133 Torino, Italy
3
Center for Theoretical Biological Physics and Department of Physics, University of California at
San Diego, La Jolla, CA 92093
The crystal structure of the Bacillus subtilis response regulator Spo0F in complex with
the histidine kinase structural homologue Spo0B defined the active site of phosphotransfer and
the spatial interactions of two-component systems of microbes and plants. The limited
bioinformatic data available at the time was sufficient to deduce and understand the molecular
basis for recognition specificity between histidine kinases and response regulators (J. A. Hoch
and K. I. Varughese. 2001. J. Bacteriol. 183:4941-4949). Today, the availability of large protein
databases generated from sequences of hundreds of bacterial genomes enables more
sophisticated statistical approaches to extract interacting and specificity determining positions
between proteins from protein databases. The goal of such studies is to identify protein
interaction surfaces from sequencing data alone, without previous structural knowledge, i.e. cocrystal
data. A number of co-variance based approaches producing nearly identical results have
been applied to the highly amplified two-component systems as a means to verify mathematical
data with structural knowledge of sensor kinase/response regulator interaction; including one
presented by us at the BLAST IX in 2007 (R. A. White et al. 2007. Methods Enzymol. 422:75-
101). While producing potential specificity determining position information, these methods have
an important shortcoming in predicting spatial proximity. They cannot distinguish between
directly correlated (interacting) and indirectly correlated (non-interacting) residue positions. To
address this issue we developed a novel method that combines co-variance analysis with global
inference analysis, adopted from use in statistical physics.
When applied to a set of over 2500 representatives of the bacterial two-component
signal transduction system, the combination of covariance with global inference methods
successfully and robustly identified residue pairs that are proximal in space without resorting to
ad hoc tuning parameters, both for hetero-interactions between sensor kinase (SK) and
response regulator (RR) proteins and for homo-interactions between RR proteins. The
spectacular success of this approach illustrates the effectiveness of this combination approach
in identifying direct interaction positions based on sequence information alone. We expect this
method to be applicable for predicting interaction surfaces between proteins present in only one
copy per genome as the number of sequenced genomes continues to expand, and for
assembling multi-protein structures.
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