In the new papers, biologists at the
University of Washington School of Medicine show that machine learning can be
used to create protein molecules much more accurately and quickly than
previously possible. The scientists hope this advance will lead to many
new vaccines, treatments, tools for carbon capture, and sustainable
biomaterials.
“Proteins are fundamental across
biology, but we know that all the proteins found in every plant, animal, and
microbe make up far less than one percent of what is possible. With these new
software tools, researchers should be able to find solutions to long-standing
challenges in medicine, energy, and technology,” said senior author David
Baker, professor
of biochemistry at the University of Washington School of Medicine and
recipient of a 2021 Breakthrough
Prize in Life Sciences.
Proteins are often referred to as the
“building blocks of life” because they are essential for the structure and
function of all living things. They are involved in virtually every process
that takes place inside cells, including growth, division, and repair. Proteins
are made up of long chains of chemicals called amino acids. The sequence of
amino acids in a protein determines its three-dimensional shape. This intricate
shape is crucial for the protein to function.
AI-hallucinated
symmetric rings. Images courtesy of Ian
Haydon
Recently, powerful machine learning algorithms
including AlphaFold and RoseTTAFold have
been trained to predict the detailed shapes of natural proteins based solely on
their amino acid sequences. Machine learning is a type of artificial
intelligence that allows computers to learn from data without being explicitly
programmed. Machine learning can be used to model complex scientific problems
that are too difficult for humans to understand.
To go beyond the proteins found in
nature, Baker’s team members broke down the challenge of protein design into
three parts andused new software solutions for each.
First, a new protein shape must be generated. In
a paper published July 21 in the journal Science, the team
showed that artificial intelligence can generate new protein shapes in two
ways.
The first, dubbed “hallucination,” is
akin to DALL-E or other generative AI tools that produce output based on
simple prompts. The second, dubbed “inpainting,” is analogous to the
autocomplete feature found in modern search bars.
Second, to speed up the process, the team devised a
new algorithm for generating amino acid sequences. Described in the
Sept.15 issue of Science, this software tool, called ProteinMPNN, runs in
about one second. That’s more than 200 times faster than the previous best software.
Its results are superior to prior tools, and the software requires no expert
customization to run.
“Neural networks are easy to train if you have a ton
of data, but with proteins, we don’t have as many examples as we would like. We
had to go in and identify which features in these molecules are the most
important. It was a bit of trial and error,” said project scientist Justas Dauparas, a postdoctoral fellow at the Institute for Protein
Design
Third, the team used AlphaFold, a tool
developed by Alphabet’s DeepMind, to independently assess whether the amino
acid sequences they came up with were likely to fold into the intended shapes.
“Software for predicting protein
structures is part of the solution but it cannot come up with anything new on
its own,” explained Dauparas.
“ProteinMPNN is to protein design what
AlphaFold was to protein structure prediction,” added Baker.
In another paper appearing in Science Sept.
15, a team from the Baker lab confirmed that the combination of new machine
learning tools could reliably generate new proteins that functioned in the
laboratory.
“We found that proteins made using ProteinMPNN were
much more likely to fold up as intended, and we could create very complex
protein assemblies using these methods” said project scientist Basile Wicky, a postdoctoral fellow at the Institute for Protein
Design.
Detail of a
protein designed using ProteinMPNN.
Among the new proteins made were
nanoscale rings that the researchers believe could become parts for custom
nanomachines. Electron microscopes were used to observe the rings, which have
diameters roughly a billion times smaller than a poppy seed.
“This is the very beginning of machine
learning in protein design. In the coming months, we will be working to improve
these tools to create even more dynamic and functional proteins,” said Baker.
Computer resources for this work were donated by Microsoft and Amazon Web Services.
Source: https://newsroom.uw.edu/news/beyond-alphafold-ai-excels-creating-new-proteins
Journal article: https://www.science.org/doi/10.1126/science.add2187
Image: Proteins designed with an
ultra-rapid software tool called ProteinMPNN were much more likely to fold up
as intended. Credit – Ian Haydon
Source: Beyond
AlphaFold: A.I. excels at creating new proteins – Scents of Science
(myfusimotors.com)
No comments:
Post a Comment