A neural network trained exclusively to predict protein shapes can also generate new ones.
From:
UW Medicine Newsroom
December 1, 2021 -- Just as
convincing images of cats can be created using artificial intelligence, new
proteins can now be made using similar tools. In a report in Nature,
researchers describe the development of a neural network that “hallucinates”
proteins with new, stable structures.
Proteins, which are string-like
molecules found in every cell, spontaneously fold into intricate
three-dimensional shapes. These folded shapes are key to nearly every
biological process, including cellular development, DNA repair, and metabolism.
But the complexity of protein shapes makes them difficult to study. Biochemists
often use computers to predict how protein strings, or sequences, might fold.
In recent years, deep learning has revolutionized the accuracy of this work.
“For this project, we made up completely
random protein sequences and introduced mutations into them until our neural
network predicted that they would fold into stable structures,” said co-lead
author Ivan Anishchenko, He is an acting instructor of biochemisty
at the University of Washington School of Medicine and a researcher in David
Baker’s laboratory at the UW Medicine Institute
for Protein Design.
“At no point did we guide the software
toward a particular outcome,“ Anishchenko said, “ These new proteins are just
what a computer dreams up.”
In the future, the team believes it
should be possible to steer the artificial intelligence so that it generates
new proteins with useful features.
“We’d like to use deep learning to
design proteins with function, including protein-based drugs, enzymes, you name
it,” said co-lead author Sam Pellock, a postdoctoral scholar in the Baker lab.
The research team, which included
scientists from UW Medicine, Harvard University, and Rensselaer Polytechnic
Institute (RPI), generated 2,000 new protein sequences that were predicted
to fold. Over 100 of these were produced in the laboratory and studied.
Detailed analysis on three such proteins confirmed that the shapes predicted by
the computer were indeed realized in the lab.
“Our NMR [nuclear magnetic resonance]
studies, along with X-ray crystal structures determined by the University of
Washington team, demonstrate the remarkable accuracy of protein designs created
by the hallucination approach”, said co-author Theresa Ramelot, a senior
research scientist at RPI in Troy, New York.
Gaetano Montelione, a co-author and professor of chemistry
and chemical biology at RPI, noted. “The hallucination approach builds on
observations we made together with the Baker lab revealing that protein
structure prediction with deep learning can be quite accurate even for a single
protein sequence with no natural relatives. The potential to hallucinate brand
new proteins that bind particular biomolecules or form desired enzymatic active
sites is very exciting”.
“This approach greatly simplifies
protein design,” said senior author David Baker, a professor of biochemistry at
the UW School of Medicine who received a 2021 Breakthrough Prize in Life
Sciences. “Before, to create a new protein with a particular shape, people
first carefully studied related structures in nature to come up with a set of
rules that were then applied in the design process. New sets of rules were
needed for each new type of fold. Here, by using a deep-learning network that
already captures general principles of protein structure, we eliminate the need
for fold-specific rules and open up the possibility of focusing on just the
functional parts of a protein directly.”
“Exploring how to best use this strategy
for specific applications is now an active area of research, and this is where
I expect the next breakthroughs,” said Baker.
Funding was provided by the National
Science Foundation (1937533, MCB2032259), National Institutes of Health
(DP5OD026389, GM120574, P30GM124165, S10OD021527), Department of Energy
(DE-AC02-06CH11357) Open Philanthropy, Eric and Wendy Schmidt by recommendation
of the Schmidt Futures program, Audacious Project, Washington Research
Foundation, Novo Nordisk Foundation, and Howard Hughes Medical Institute. The
authors also acknowledge computing resources from the University of Washington
and Rosetta@Home volunteers.
https://newsroom.uw.edu/news/deep-learning-dreams-new-protein-structures
No comments:
Post a Comment