Over the past two years, machine learning has revolutionized protein structure prediction. Now,
three papers in the journal Science describe a similar revolution in protein design.
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.
The researcher David Baker says that 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.
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.
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 and used 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 A.I.
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.
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. ProteinMPNN is to
protein design what AlphaFold was to protein structure prediction.
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.
The researchers found that proteins made using ProteinMPNN were much more likely to
fold up as intended, and they could create very complex protein assemblies using these methods.
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.