A new artificial intelligence tool developed at the University of California, San Francisco (UCSF), can capture how proteins behave in the context of a living cell. The tool, called DeepTracer, uses deep learning to analyze large datasets of images and videos of proteins in action. This allows researchers to see how proteins interact with each other and with their environment, and to identify the key features that determine their behavior.

DeepTracer is a significant advance over existing methods for studying proteins. Traditional methods, such as X-ray crystallography and nuclear magnetic resonance spectroscopy, can only provide static images of proteins. DeepTracer, on the other hand, can capture the dynamic behavior of proteins in real time. This allows researchers to see how proteins change shape, move around, and interact with other molecules.

DeepTracer is also a powerful tool for drug discovery. By understanding how proteins behave in the context of a living cell, researchers can identify new targets for drugs that can modulate their activity. This could lead to the development of new treatments for a variety of diseases, including cancer, neurodegenerative disorders, and infectious diseases.

"DeepTracer is a major breakthrough in the field of protein science," said UCSF professor of bioengineering and cellular and molecular pharmacology Kevan Shokat, who led the development of the tool. "It gives us an unprecedented view of how proteins work in the context of a living cell. This knowledge will be essential for developing new drugs and treatments for diseases."

The study describing DeepTracer was published in the journal Nature Methods.