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Big Data in 3 Dimensions

Viewing oncogenic mutations in 3-D showed that they cluster together on folded proteins.

May 1, 2018
Jim Daley

MAPMAKING: A tool for visualizing data on metabolic pathways reveals new relationships among cancer-causing mutations.© ISTOCK.COM/SHULZ

EDITOR'S CHOICE IN TECHNIQUES

The paper
E. Brunk et al., “Recon3D enables a three-dimensional view of gene variation in human metabolism,” Nat Biotechnol, 36:272-81, 2018.

DISPARATE DATA
To make better sense of the accumulated knowledge about human metabolic pathways gathered by different research groups, researchers led by Elizabeth Brunk, a structural systems biologist at the University of California, San Diego, constructed a database that displays aggregated protein structure, pharmacogenomic associations, and phenotypic data in 3D.

FILLING THE GAPS
Recon3D’s inclusion of protein structural information from thousands of labs in a massive searchable map is the model’s “biggest step forward from other metabolic reconstructions,” says Brunk. Often, the data may be incomplete or may contain experimental artifacts; the team filled in those gaps using homology modeling, a technique in which researchers construct a model of a protein using its amino acid sequence and hints from a structurally related protein.

BIG PICTURE
The 3-D maps of protein structures gave the researchers a new perspective on cancer-causing mutations in the human proteome, revealing that many oncogenic mutations cluster near each other when proteins are folded. “We would have missed that had we not mapped them to the 3-D structure,” says Brunk. Other potential applications of the tool include determining metabolic responses to medications and studying the connections between disease, genes, and drug action.

PASSING IT ON
“This is potentially very exciting, because it’s really the integration of . . . big data resources into something that is even larger than the parts,” says John Van Horn, a neuroscientist at the University of Southern California’s Institute of Neuroimaging and Informatics. By demonstrating that deleterious mutations cluster in functional hotspots, he says, Recon3D could potentially allow researchers “to identify things that are cancer-causing.”

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