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New technique is quantum leap forward in understanding proteins
Date:12/23/2008

interactions is a statistical method known as covariance, and the Scripps Research-UCSD team's new method relies on this as a foundation. Covariance analysis of proteins involves studying the amino acids found at specific locations on various protein sequences culled from genomics data. Covariance analysis between two proteins identifies residue positions that vary together from residue positions that vary at random.

Covariance has proven quite effective at identifying critical residues that bind directly with other proteins or other spots on the same protein, which is the goal. But, unfortunately, the method also identifies a high percentage of residues that turn out to not be involved in these direct interactions. Research groups have developed various techniques to winnow out such indirect interactions, but with only limited successuntil now.

Outside Help

Years ago, frustrated by the inadequacy of available techniques, Hoch and his colleagues set out to find some means beyond the normal bounds of biology to solve the problem of identifying the directly interacting protein residues without crystallography. The search eventually brought them to Professor Terry Hwa at UCSD and Martin Weigt, an expert in a computational technique known as message passing, in Turin, Italy. This method, used mainly in an area known as spin glass physics, is a computer-intensive means of finding patterns in certain types of data.

For the first test of message passing with proteins, the group focused on the proteins involved in the well-studied two-component signaling system, which is responsible for a range of critical functions in bacteria. The first step of the work was to analyze the countless proteins involved in this system applying standard covariance techniques to available genomics data. The full analysis included about 2,500 different protein pairings and considered the potential interactions between about 100 residues on each protein
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Contact: Keith McKeown
kmckeown@scripps.edu
858-784-8134
Scripps Research Institute
Source:Eurekalert

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