Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon, or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate.
In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins.
By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins.
“We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein,” said Rama Ranganathan.
“Not only can it teach us the physics of how proteins work and how they evolve, it can help us find solutions for issues like carbon capture and energy harvesting.
Even more generally, the studies in proteins might even help teach us how the deep neural networks behind modern machine learning actually work.”
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