A
new artificial intelligence-powered tool can help researchers determine how
well an enzyme fits with a desired target, helping them find the best enzyme
and substrate combination for applications from catalysis to medicine to
manufacturing.
Led by Huimin Zhao, a professor of
chemical and biomolecular engineering at the University of Illinois
Urbana-Champaign, the researchers developed EZSpecificity using new
enzyme-substrate pair data and a new machine learning algorithm. They have made
the tool freely available online and published their results in the journal Nature.
"If we want a certain product using
an enzyme, we want to use the best enzyme and substrate combination," said
Zhao, who also is the director of the NSF Molecule Maker Lab Institute and of
the NSF iBioFoundry at the U. of I. "EZSpecificity is an AI model that can
analyze an enzyme sequence and then predict which substrate can best fit into
that enzyme. It is highly complementary to the CLEAN AI model that we developed
to predict an enzyme's function from its sequence more than two years ago."
Enzymes are large proteins that catalyze
molecular reactions. They have pocket-like regions that target molecules, called
substrates, fit into. How well an enzyme and substrate fit is called
specificity. The typical analogy for enzyme-substrate interaction is a lock and
key: Only the right key will open the lock. However, enzyme function is not
that simple, Zhao said.
"It
is challenging to figure out the best combination because the pocket is not
static," he said. "The enzyme actually changes conformation when it
interacts with the substrate. It is more of an induced fit. And some enzymes
are promiscuous and can catalyze different types of reactions. That makes it
very hard to predict. That's why we need a machine learning model and experimental data that really prove which
pairing will work best."
While
other enzyme specificity models have been introduced, they are limited in
accuracy and in the types of enzymatic reactions they can predict.
Zhao's
group realized that to improve AI's ability to predict specificity, they needed
to improve and expand the dataset that the machine learning model drew from.
They partnered with the group led by Diwakar Shukla, a U. of I. professor of
chemical and biomolecular
engineering. Shukla's group performed docking studies for different
classes of enzymes to create a large database containing information about not
only an enzyme's sequence and structure, but also how enzymes of various
classes conform around different types of substrates.
"Experiments
that capture how enzymes interact with their substrates are often slow and
complex, so we ran extensive docking simulations to complement and expand on
the existing experimental data," Shukla said. "We zoomed in on the
atomic-level interactions between enzymes and their substrates. Millions of
docking calculations provided us this missing piece of the puzzle to build a
highly accurate enzyme specificity predictor."
The
researchers then tested EZSpecificity side-by-side with ESP, the current
leading model, in four scenarios designed to mimic real-world applications.
EZSpecificity outperformed ESP in all scenarios. Finally, the researchers
experimentally validated EZSpecificity by looking at eight halogenase enzymes,
a class that has not been well characterized but is increasingly used to make
bioactive molecules, and 78 substrates. EZSpecificity achieved 91.7% accuracy
for its top pairing predictions, while ESP only displayed 58.3% accuracy.
"I
cannot say it works for every enzyme, but for certain enzymes, we showed that
EZSpecificity works very well indeed," Zhao said. "We want to make
this tool available to others, so we developed a user interface. Researchers
now can enter the substrate and the protein sequence, and then they can use our tool
to predict whether that substrate can work well or not."
Next, the researchers plan to expand their AI tools to analyze enzyme selectivity, which indicates whether an enzyme has a preference for a certain site on a substrate, to help rule out enzymes with off-target effects. They also plan to continue to refine EZSpecificity with more experimental data.
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