Unique Technique Paves Way for Creation of Protein Nanomachines and Engineering of New Cell Functions.

Institute for Protein Design

The findings were published Dec. 19 in the journal Nature. Here is the paper indexed under synthetic biology. The research was performed at the Institute for Protein Design, directed by David Baker, UW professor of biochemistry and a Howard Hughes Medical Institute investigator.

In the past, researchers interested in designing biomolecular nanomachines have often used DNA as a major component. This is because DNA strands come together and form hydrogen bonds to create DNA’s double helix, but only if their sequences are complementary.

The team developed new protein design algorithms that produce complementary proteins that precisely pair with each other using the same chemical language of DNA.

“This is a first-of-its-kind breakthrough,” Chen said. “What we’re doing is computationally designing these hydrogen-bond networks so that each protein pair has a unique complementary sequence. There is only one way for them to come together and they do not cross-react with proteins from other pairs.”

“Engineering cells to do new tasks is the future of medicine and biotechnology, whether that’s engineering bacteria to make energy or clean up toxic waste or creating immune cells that attack cancers,” said Scott Boyken, another author of the paper and a UW postdoctoral researcher. “This technique provides scientists a precise, programmable way to control how protein machines interact, a key step towards achieving these new tasks. We have opened a major door to protein nanomaterial design.”

In their study, researchers used a computer program developed in the Baker lab, called Rosetta. The program takes advantage of the fact that the shape an amino acid chain will assume is driven by the forces of attraction and repulsion between the amino acids of the chain and the fluid in which the chain is immersed. By calculating the shape that best balances out these forces so that the chain achieves its lowest overall energy level, the program can predict the shape a given amino acid chain will likely take.

This work was done in collaboration with researchers led by Vicki Wysocki at Ohio State University and by Nikolaos Sgourakis at the University of California, Santa Cruz. The work used support from the Structurally Integrated Biology for Life Sciences program with small angle X-ray scattering and the Advanced Light Source resources at the Lawrence Berkeley National Laboratory, as well as the Argonne Leadership Computing Facility at the Argonne National Laboratory.