Computational biologists at Carnegie Mellon University (CMU) have developed a new tool for understanding the genetic history of multi-domain proteins, a class of proteins that play an important role in cancer development.
Researchers use the ancestry of proteins to predict how genes will function, to map chromosomal regions and to analyze how genes turn on and off. This information is critical to cancer research because proteins impact cell communication and binding so errors in their functioning can cause tumors.
While other computational biologists have already developed methods to identify genes sharing common ancestors, multi-domain proteins continue to present challenges. That’s because these proteins undergo a complex evolutionary process known as domain shuffling which makes their true lineage difficult to determine.
To improve scientists’ understanding of multi-domain proteins, Carnegie Mellon’s team developed a new computational method called Neighborhood Correlation. Neighborhood Correlation uses a statistically-weighted sequence similarity network to identify the ancestral relationships of protein families with greater accuracy.
CMU’s team tested Neighborhood Correlation on 20 protein families whose ancestry was already well established including kinases, the largest multi-domain protein family in the human body. The team was pleased to find the approach worked significantly better than other computational tools in use today. As one member of the CMU team said,
“We needed a completely new approach to determine which multi-domain proteins share a common ancestor and we are the first group to propose such a method. Ours is the first approach to define and analyze common ancestry in a traditional vertical way, even when domain shuffling occurs.”
Computational Biologist, CMU
If you’d like to learn more about Neighborhood Correlation, the findings have been published in the May 16, 2008, online edition of the Public Library of Science (PLoS) Computational Biology, an open-access, peer-reviewed journal of the International Society for Computational Biology.
Source: Carnegie Mellon University Press
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