Using a type of
artificial intelligence known as deep learning, MIT researchers have discovered a class of compounds
that can kill a drug-resistant bacterium that causes more than 10,000 deaths in
the United States every year.
In a study appearing in Nature, the researchers showed that these compounds could
kill methicillin-resistant Staphylococcus aureus (MRSA) grown in a lab dish and in two mouse models of MRSA
infection. The compounds also show very low toxicity against human cells,
making them particularly good drug candidates.
A key innovation of the new study is
that the researchers were also able to figure out what kinds of information the
deep-learning model was using to make its antibiotic potency predictions. This
knowledge could help researchers to design additional drugs that might work
even better than the ones identified by the model.
“The insight here was that we could see
what was being learned by the models to make their predictions that certain
molecules would make for good antibiotics. Our work provides a framework that
is time-efficient, resource-efficient, and mechanistically insightful, from a
chemical-structure standpoint, in ways that we haven’t had to date,” says James
Collins, the Termeer Professor of Medical Engineering and Science in MIT’s
Institute for Medical Engineering and Science (IMES) and Department of
Biological Engineering.
Felix Wong, a postdoc at IMES and the
Broad Institute of MIT and Harvard, and Erica Zheng, a former Harvard Medical
School graduate student who was advised by Collins, are the lead authors of the
study, which is part of the Antibiotics-AI Project at MIT. The mission of this project, led by
Collins, is to discover new classes of antibiotics against seven types of
deadly bacteria, over seven years.
Explainable
predictions
MRSA, which infects more than 80,000
people in the United States every year, often causes skin infections or
pneumonia. Severe cases can lead to sepsis, a potentially fatal bloodstream
infection.
Over the past several years, Collins and
his colleagues in MIT’s Abdul Latif Jameel Clinic for Machine Learning in
Health (Jameel Clinic) have begun using deep learning to try to find new
antibiotics. Their work has yielded potential drugs against Acinetobacter
baumannii, a bacterium
that is often found in hospitals, and many other drug-resistant
bacteria.
These compounds were identified using
deep learning models that can learn to identify chemical structures that are
associated with antimicrobial activity. These models then sift through millions
of other compounds, generating predictions of which ones may have strong
antimicrobial activity.
These types of searches have proven
fruitful, but one limitation to this approach is that the models are “black
boxes,” meaning that there is no way of knowing what features the model based
its predictions on. If scientists knew how the models were making their
predictions, it could be easier for them to identify or design additional
antibiotics.
“What we set out to do in this study was
to open the black box,” Wong says. “These models consist of very large numbers
of calculations that mimic neural connections, and no one really knows what’s
going on underneath the hood.”
First, the researchers trained a deep
learning model using substantially expanded datasets. They generated this
training data by testing about 39,000 compounds for antibiotic activity against
MRSA, and then fed this data, plus information on the chemical structures of
the compounds, into the model.
“You can represent basically any
molecule as a chemical structure, and also you tell the model if that chemical
structure is antibacterial or not,” Wong says. “The model is trained on many
examples like this. If you then give it any new molecule, a new arrangement of
atoms and bonds, it can tell you a probability that that compound is predicted
to be antibacterial.”
To
figure out how the model was making its predictions, the researchers adapted an
algorithm known as Monte Carlo tree search, which has been used to help make
other deep learning models, such as AlphaGo, more explainable. This search
algorithm allows the model to generate not only an estimate of each molecule’s
antimicrobial activity, but also a prediction for which substructures of the
molecule likely account for that activity.
Potent activity
To further narrow down the pool of
candidate drugs, the researchers trained three additional deep learning models
to predict whether the compounds were toxic to three different types of human
cells. By combining this information with the predictions of antimicrobial
activity, the researchers discovered compounds that could kill microbes while
having minimal adverse effects on the human body.
Using this collection of models, the
researchers screened about 12 million compounds, all of which are commercially
available. From this collection, the models identified compounds from five
different classes, based on chemical substructures within the molecules, that
were predicted to be active against MRSA.
The researchers purchased about 280
compounds and tested them against MRSA grown in a lab dish, allowing them to
identify two, from the same class, that appeared to be very promising
antibiotic candidates. In tests in two mouse models, one of MRSA skin infection and one
of MRSA systemic infection, each of those compounds reduced the MRSA population
by a factor of 10.
Experiments
revealed that the compounds appear to kill bacteria by disrupting their ability
to maintain an electrochemical gradient across their cell membranes. This
gradient is needed for many critical cell functions, including the ability to
produce ATP (molecules that cells use to store energy). An antibiotic candidate
that Collins’ lab discovered in 2020, halicin, appears to work by a similar
mechanism but is specific to Gram-negative bacteria (bacteria with thin cell
walls). MRSA is a Gram-positive bacterium, with thicker cell walls.
“We
have pretty strong evidence that this new structural class is active against
Gram-positive pathogens by selectively dissipating the proton motive force in
bacteria,” Wong says. “The molecules are attacking bacterial cell membranes
selectively, in a way that does not incur substantial damage in human cell
membranes. Our substantially augmented deep learning approach allowed us to
predict this new structural class of antibiotics and enabled the finding that
it is not toxic against human cells.”
The
researchers have shared their findings with Phare Bio, a nonprofit started by Collins and
others as part of the Antibiotics-AI Project. The nonprofit now plans to do
more detailed analysis of the chemical properties and potential clinical use of
these compounds. Meanwhile, Collins’ lab is working on designing additional
drug candidates based on the findings of the new study, as well as using the
models to seek compounds that can kill other types of bacteria.
“We
are already leveraging similar approaches based on chemical substructures to
design compounds de novo, and of course, we can readily adopt this approach out
of the box to discover new classes of antibiotics against different pathogens,”
Wong says.
Source: https://news.mit.edu/2023/using-ai-mit-researchers-identify-antibiotic-candidates-1220
No comments:
Post a Comment