Creating a global map of the protein shape universe

Now, Purdue University researchers have come up with a novel way to classify proteins and their shapes, which lays the foundation of how we understand protein structures and functions. The shapes are important because they determine the role and effectiveness of the proteins. The research is published in the April edition of PLOS Computational Biology.

"We developed a new way to view and classify protein 3D shapes, which provides our basic understanding of protein shapes and will become a foundation of artificial protein design and other applications of proteins," said Daisuke Kihara, a professor of biological sciences and computer science in Purdue's College of Science, who leads the research team.

Kihara said proteins were conventionally classified by conformations of protein chains, a rather detailed level of structural features. They currently classify proteins by their overall surface shapes, which would be more directly relevant to how they interact with other proteins and compounds in a cell.

The Purdue team maps and classifies 3D surface shapes of proteins within a certain space inside the body. Surface shapes of proteins are represented with 3D Zernike descriptors, mathematical moment-based invariants, which have previously been demonstrated effective for biomolecular structure similarity search.

They also have analyzed the shape space occupied by protein complexes. The mapping provides data into the relationship between shapes, main-chain folds and complex formation.

"This work is in the area of bioinformatics," Kihara said. "Bioinformatics is not only useful for processing biological data or developing computational tools, but it is also helpful for providing a unique view and framework that can change the way people view and understand the biomolecular world."

Kihara has worked with the Purdue Research Foundation Office of Technology Commercialization on some of his research and technology.