Deep learning dreams up new protein structures
A neural network trained exclusively to predict protein shapes can also generate new ones.

Date:
December 1, 2021
Source:
University of Washington School of Medicine/UW Medicine
Summary:
Using artificial intelligence and deep learning, researchers have
developed a neural network that 'hallucinates' the structures of
new protein molecules. The scientists made up completely random
protein sequences and introduced mutations into them until the
neural network predicted they would fold into stable structures. The
software was not guided toward a particular outcome; the proteins
were just what the computer dreams up. Next step: using deep
learning to try to design proteins with particular functions,
such as enzymes or drugs.



FULL STORY ==========================================================================
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 two
thousand 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, National
Institutes of Health, Department of Energy, 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.

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

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


========================================================================== Journal Reference:
1. Ivan Anishchenko, Samuel J. Pellock, Tamuka M. Chidyausiku,
Theresa A.

Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna,
Christoffer Norn, Alex Kang, Asim K. Bera, Frank DiMaio, Lauren
Carter, Cameron M.

Chow, Gaetano T. Montelione, David Baker. De novo protein
design by deep network hallucination. Nature, 2021; DOI:
10.1038/s41586-021-04184-w ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/12/211201111930.htm

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