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Machine Learning Reveals Recipe for Building Artificial Proteins

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A ribbon model of a protein molecule.

A team lead by researchers in the Pritzker School of Molecular Engineering at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins.


Researchers at the University of Chicago's Pritzker School of Molecular Engineering (PME) have created a process that uses big data to design new proteins.

These artificial proteins could be used to treat disease, capture carbon, and harvest energy, among other tasks.

Said PME's Rama Ranganathan, "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."

The researchers developed mathematical models based on this data, then used machine learning methods to uncover new information about the basic design rules for proteins.

Said Ranganathan, "We believe similar approaches could help us search for models for design in other complex systems in biology, like ecosystems or the brain. ... The studies in proteins might even help teach us how the deep neural networks behind modern machine learning actually work."

From University of Chicago Pritzker School of Molecular Engineering
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Abstracts Copyright © 2020 SmithBucklin, Washington, DC, USA


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