CMU Tackles Homology Analysis for Proteins

A new homology prediction method developed by researchers at Carnegie Mellon University has raised questions about the applicability of commonly used sequence similarity tools such as Blast for analyzing the evolution of multidomain proteins.

The method, called Neighborhood Correlation, was developed specifically to deal with the challenge of multidomain proteins — proteins comprised of multiple sequence segments. While these proteins represent around 40 percent of the proteome in metazoans, they present a hurdle for current homology analysis tools because it is difficult to determine whether a common sequence is the result of shared ancestry or of domain insertion.

Neighborhood Correlation relies on a sequence similarity network that is weighted to give gene duplication and domain insertion very "neighborhood structures," which enables the method to distinguish true homologs from domain-only matches, according to the authors.

Dannie Durand, a computational biologist at Carnegie Mellon, and colleagues demonstrated that the method outperformed sequence similarity methods like Blast and Psi-Blast against a curated benchmark data set of sequences known to share common ancestry.

"The paper really tackles a fundamental problem that people have been hoping to avoid," says David Haussler, director of biomolecular engineering at the University of California, Santa Cruz. "No one has really faced it head-on before so I commend [Durand] for [that]. … She puts her method up against some others and demonstrates better performance."

Neighborhood Correlation takes a geographical view, looking at the genomic neighborhood, Durand says. "Basically we make a network … in which every dot or node is a sequence … a line between two dots means there is a meaningful Blast score," she says. "If the neighborhoods are similar, the genes are related; if they are not similar, we say the genes aren't related."

— Vivien Marx

Bioinformatics Notes

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