LOS ANGELES, Oct. 16 (Xinhua) -- Using a machine-learning approach, researchers at the California Institute of Technology (Caltech) have developed a new tool for optogenetics that eliminates the need for invasive surgeries.
Modulating brain circuits in live, normally-behaving animals noninvasively will be a game changer for both basic science and therapy, according to a latest release of Caltech.
Optogenetics has been applied to study many different areas of the brain and types of behavior, such as how neurons regulate sleep behavior, but this technique often requires invasive surgical procedures.
Led by former Caltech graduate student Claire Bedbrook, a team of Caltech researchers used machine learning to design channelrhodopsin variants with optimal combinations of properties.
The goal was to find a light-sensitive variant that could open widely to strongly activate a neuron, and that would be triggered to open by just a few photons of light. When coupled with non-invasive gene delivery vectors developed by the Gradinaru laboratory, such variants would enable non-invasive optogenetics, according to the release.
The team's machine-learning algorithm learned from more than 100 different channelrhodopsins' functional properties, amino acid sequences, and structural properties to predict the sequence of a new variant that would have the desired light-sensitivity properties.
Although this work was focused on optimizing proteins for optogenetics, a machine-learning approach like this one is promising for protein engineering in general, said the research team.
"I think this machine-learning approach and framework is the future of protein engineering," said Bedbrook. "With these models, we are able to optimize proteins to our engineering specifications."
