Medical Design Briefs - August 2023 - 30

Drug Development
structures of the proteins requires a great deal of time and computing
power.
An additional obstacle is that these kinds of models don't
have a good track record for eliminating compounds known as
decoys, which are very similar to a successful drug but don't
actually interact well with the target.
" One of the longstanding challenges in the field has been
that these methods are fragile, in the sense that if I gave the
model a drug or a small molecule that looked almost like the
true thing, but it was slightly different in some subtle way, the
model might still predict that they will interact, even though it
should not, " Singh says.
Researchers have designed models that can overcome this
kind of fragility, but they are usually tailored to just one class of
drug molecules, and they aren't well-suited to large-scale
screens because the computations take too long.
The MIT team decided to take an alternative approach,
based on a protein model they first developed in 2019. Working
with a database of more than 20,000 proteins, the language
model encodes this information into meaningful numerical
representations of each amino-acid sequence that capture associations
between sequence and structure.
" With these language models, even proteins that have very
different sequences but potentially have similar structures or
similar functions can be represented in a similar way in this
language space, and we're able to take advantage of that to
make our predictions, " Sledzieski says.
In their new study, the researchers applied the protein model
to the task of figuring out which protein sequences will interact
with specific drug molecules, both of which have numerical representations
that are transformed into a common, shared space
by a neural network. They trained the network on known
protein- drug interactions, which allowed it to learn to associate
specific features of the proteins with drug-binding ability, without
having to calculate the 3D structure of any of the molecules.
" With this high-quality numerical representation, the model
can short-circuit the atomic representation entirely, and from
these numbers predict whether or not this drug will bind, "
Singh says. " The advantage of this is that you avoid the need to
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go through an atomic representation, but the numbers still
have all of the information that you need. "
Another advantage of this approach is that it takes into account
the flexibility of protein structures, which can be " wiggly "
and take on slightly different shapes when interacting with
a drug molecule.
High Affinity
To make their model less likely to be fooled by decoy drug
molecules, the researchers also incorporated a training stage
based on the concept of contrastive learning. Under this approach,
the researchers give the model examples of " real "
drugs and imposters and teach it to distinguish between them.
The researchers then tested their model by screening a library
of about 4,700 candidate drug molecules for their ability
to bind to a set of 51 enzymes known as protein kinases.
From the top hits, the researchers chose 19 drug-protein
pairs to test experimentally. The experiments revealed that of
the 19 hits, 12 had strong binding affinity (in the nanomolar
range), whereas nearly all of the many other possible drug-protein
pairs would have no affinity. Four of these pairs bound
with extremely high, sub-nanomolar affinity (so strong that a
tiny drug concentration, on the order of parts per billion, will
inhibit the protein). While the researchers focused mainly on
screening small- molecule drugs in this study, they are now
working on applying this approach to other types of drugs,
such as therapeutic antibodies. This kind of modeling could
also prove useful for running toxicity screens of potential drug
compounds, to make sure they don't have any unwanted side
effects before testing them in animal models.
" Part of the reason why drug discovery is so expensive is because
it has high failure rates. If we can reduce those failure rates by
saying upfront that this drug is not likely to work out, that could go
a long way in lowering the cost of drug discovery, " Singh says.
This new approach " represents a significant breakthrough
in drug-target interaction prediction and opens up additional
opportunities for future research to further enhance its capabilities, "
says Eytan Ruppin, chief of the Cancer Data Science
Laboratory at the National Cancer Institute, who was not involved
in the study. " For example, incorporating structural
information into the latent space or exploring molecular generation
methods for generating decoys could further improve
predictions. "
The research was funded by the National Institutes of Health,
the National Science Foundation, and the Phillip and Susan
Ragon Foundation.
Reference
1. " Contrastive learning in protein language space predicts interactions between drugs
and protein targets, " PNAS, Vol. 120, No 24, June 8, 2023, https://www.pnas.org/
doi/10.1073/pnas.2220778120.
This article was written by Anne Trafton, MIT. For more information,
contact Bonnie Berger at bab@mit.edu or visit
https://news.mit.edu.
Read more and get the code for ConPLex,
a computational approach based on a
artificial intelligence.
www.medicaldesignbriefs.com
Medical Design Briefs, August 2023
https://www.pnas.org/doi/10.1073/pnas.2220778120 https://www.pnas.org/doi/10.1073/pnas.2220778120 http://info.hotims.com/84482-811 https://news.mit.edu http://www.medicaldesignbriefs.com
Medical Design Briefs - August 2023

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