Artificial intelligence conjures proteins that speed up chemical
reactions
A team has devised machine-learning algorithms that created light-
emitting enzymes called luciferases

Date:
February 22, 2023
Source:
University of Washington School of Medicine/UW Medicine
Summary:
Scientists have used machine learning to create brand-new enzymes,
which are proteins that accelerate chemical reactions. This
is an important step in the field of protein design as new
enzymes could have many uses across medicine and industrial
manufacturing. The research team devised deep-learning, artificial
intelligence algorithms that created light- emitting enzymes called
luciferases. Laboratory testing confirmed that the new enzymes
can recognize specific chemicals and emit light very efficiently.


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FULL STORY ==========================================================================
For the first time, scientists have used machine learning to
create brand-new enzymes, which are proteins that accelerate chemical reactions. This is an important step in the field of protein design as new enzymes could have many uses across medicine and industrial manufacturing.


========================================================================== "Living organisms are remarkable chemists. Rather than relying on toxic compounds or extreme heat, they use enzymes to break down or build
up whatever they need under gentle conditions. New enzymes could put
renewable chemicals and biofuels within reach," said senior author David
Baker, professor of biochemistry at the University of Washington School
of Medicine and recipient of the 2021 Breakthrough Prize in Life Sciences.

As reported Feb, 22 in the journal Nature, a team based at the Institute
for Protein Design at UW Medicine devised machine-learning algorithms
that can create light-emitting enzymes called luciferases. Laboratory
testing confirmed that the new enzymes can recognize specific chemicals
and emit light very efficiently. This project was led by two postdoctoral scholars in the Baker Lab, Andy Hsien-Wei Yeh and Christoffer Norn.

The Naturepaper is titled De novo design of luciferases using deep
learning.

To create new luciferase enzymes, the team first selected chemicals
called luciferins that they wanted the proteins to act upon. They then
used software to generate thousands of possible protein structures that
might react with those chemicals.

During laboratory testing, the researchers identified an efficient
enzyme, dubbed LuxSit (Let there be light). The enzyme performed the
desired chemical reaction. Refinement of the enzyme led to dramatic improvements in performance.

An optimized enzyme, dubbed LuxSit-i, generated enough light to be
visible to the naked eye. It was found to be brighter than the natural luciferase enzyme found in the glowing sea pansy Renilla reniformis.

"We were able to design very efficient enzymes from scratch on the
computer, as opposed to relying on enzymes found in nature. This
breakthrough means that custom enzymes for almost any chemical reaction
could, in principle, be designed," said Yeh.

New enzymes could benefit biotechnology, medicine, environmental
remediation, and manufacturing. For example, in biotechnology, enzymes
can improve biofuel production, food processing, and pharmaceutical manufacturing. In medicine, enzymes can serve as therapeutic and
diagnostic tools. Enzyme design can improve the environment by breaking
down pollutants or cleaning up contaminated sites. And enzymes may also
aid in the production of new materials such as biodegradable plastics
and adhesives.

This research was led by UW School of Medicine scientists and included collaborators at the University of California, Los Angeles.

This work was supported by the Howard Hughes Medical Institute, National Institutes of Health (K99EB031913), United World Antiviral Research
Network, National Institute of Allergy and Infectious Disease (1 U01 AI151698-01), Audacious Project at the Institute for Protein Design,
Open Philanthropy Project Improving Protein Design Fund, Novo Nordisk Foundation (NNF18OC0030446), National Science Foundation (CHE-1764328, OCI-1053575), and Eric and Wendy Schmidt by recommendation of the Schmidt Futures program.

National Natural Science Foundation of China (22103060) provided partial computational resources.

* RELATED_TOPICS
o Matter_&_Energy
# Biochemistry # Organic_Chemistry # Optics # Chemistry
o Computers_&_Math
# Software # Computer_Modeling # Artificial_Intelligence
# Video_Games
* RELATED_TERMS
o Protein o Artificial_intelligence o Industrial_robot
o Denaturation_(biochemistry) o Bioinformatics o
Electromagnetic_spectrum o Computer_vision o Electroluminescence

========================================================================== Story Source: Materials provided by University_of_Washington_School_of_Medicine/UW_Medicine.

Original written by Ian Haydon. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Andy Hsien-Wei Yeh, Christoffer Norn, Yakov Kipnis, Doug Tischer,
Samuel
J. Pellock, Declan Evans, Pengchen Ma, Gyu Rie Lee, Jason Z. Zhang,
Ivan Anishchenko, Brian Coventry, Longxing Cao, Justas Dauparas,
Samer Halabiya, Michelle DeWitt, Lauren Carter, K. N. Houk, David
Baker. De novo design of luciferases using deep learning. Nature,
2023; 614 (7949): 774 DOI: 10.1038/s41586-023-05696-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/02/230222141133.htm

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