Machine learning uncovers potential new TB drugs

Date:
October 15, 2020
Source:
Massachusetts Institute of Technology
Summary:
Using a machine-learning approach that incorporates uncertainty,
researchers identified several promising compounds that target
a protein required for the survival of the bacteria that cause
tuberculosis.



FULL STORY ========================================================================== Machine learning is a computational tool used by many biologists to
analyze huge amounts of data, helping them to identify potential new
drugs. MIT researchers have now incorporated a new feature into these
types of machine- learning algorithms, improving their prediction-making ability.


========================================================================== Using this new approach, which allows computer models to account for uncertainty in the data they're analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that
cause tuberculosis.

This method, which has previously been used by computer scientists but has
not taken off in biology, could also prove useful in protein design and
many other fields of biology, says Bonnie Berger, the Simons Professor
of Mathematics and head of the Computation and Biology group in MIT's
Computer Science and Artificial Intelligence Laboratory (CSAIL).

"This technique is part of a known subfield of machine learning, but
people have not brought it to biology," Berger says. "This is a paradigm
shift, and is absolutely how biological exploration should be done."
Berger and Bryan Bryson, an assistant professor of biological engineering
at MIT and a member of the Ragon Institute of MGH, MIT, and Harvard, are
the senior authors of the study, which appears today in Cell Systems. MIT graduate student Brian Hie is the paper's lead author.

Better predictions Machine learning is a type of computer modeling in
which an algorithm learns to make predictions based on data that it
has already seen. In recent years, biologists have begun using machine
learning to scour huge databases of potential drug compounds to find
molecules that interact with particular targets.



==========================================================================
One limitation of this method is that while the algorithms perform well
when the data they're analyzing are similar to the data they were trained
on, they're not very good at evaluating molecules that are very different
from the ones they have already seen.

To overcome that, the researchers used a technique called Gaussian
process to assign uncertainty values to the data that the algorithms are trained on. That way, when the models are analyzing the training data,
they also take into account how reliable those predictions are.

For example, if the data going into the model predict how strongly
a particular molecule binds to a target protein, as well as the
uncertainty of those predictions, the model can use that information
to make predictions for protein-target interactions that it hasn't
seen before. The model also estimates the certainty of its own
predictions. When analyzing new data, the model's predictions may have
lower certainty for molecules that are very different from the training
data. Researchers can use that information to help them decide which
molecules to test experimentally.

Another advantage of this approach is that the algorithm requires only
a small amount of training data. In this study, the MIT team trained
the model with a dataset of 72 small molecules and their interactions
with more than 400 proteins called protein kinases. They were then able
to use this algorithm to analyze nearly 11,000 small molecules, which
they took from the ZINC database, a publicly available repository that
contains millions of chemical compounds.

Many of these molecules were very different from those in the training
data.

Using this approach, the researchers were able to identify molecules
with very strong predicted binding affinities for the protein kinases
they put into the model. These included three human kinases, as well
as one kinase found in Mycobacterium tuberculosis. That kinase, PknB,
is critical for the bacteria to survive, but is not targeted by any
frontline TB antibiotics.



==========================================================================
The researchers then experimentally tested some of their top hits to
see how well they actually bind to their targets, and found that the
model's predictions were very accurate. Among the molecules that the
model assigned the highest certainty, about 90 percent proved to be
true hits -- much higher than the 30 to 40 percent hit rate of existing
machine learning models used for drug screens.

The researchers also used the same training data to train a traditional machine-learning algorithm, which does not incorporate uncertainty,
and then had it analyze the same 11,000 molecule library. "Without
uncertainty, the model just gets horribly confused and it proposes very
weird chemical structures as interacting with the kinases," Hie says.

The researchers then took some of their most promising PknB inhibitors
and tested them against Mycobacterium tuberculosis grown in bacterial
culture media, and found that they inhibited bacterial growth. The
inhibitors also worked in human immune cells infected with the bacterium.

A good starting point Another important element of this approach is that
once the researchers get additional experimental data, they can add it
to the model and retrain it, further improving the predictions. Even a
small amount of data can help the model get better, the researchers say.

"You don't really need very large data sets on each iteration,"
Hie says. "You can just retrain the model with maybe 10 new examples,
which is something that a biologist can easily generate." This study is
the first in many years to propose new molecules that can target PknB,
and should give drug developers a good starting point to try to develop
drugs that target the kinase, Bryson says. "We've now provided them with
some new leads beyond what has been already published," he says.

The researchers also showed that they could use this same type of machine learning to boost the fluorescent output of a green fluorescent protein,
which is commonly used to label molecules inside living cells. It could
also be applied to many other types of biological studies, says Berger,
who is now using it to analyze mutations that drive tumor development.

The research was funded by the U.S. Department of Defense through
the National Defense Science and Engineering Graduate Fellowship;
the National Institutes of Health; the Ragon Institute of MGH, MIT,
and Harvard' and MIT's Department of Biological Engineering.


========================================================================== Story Source: Materials provided by
Massachusetts_Institute_of_Technology. Original written by Anne
Trafton. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Brian Hie, Bryan D. Bryson, Bonnie A. Berger. Leveraging
Uncertainty in
Machine Learning Accelerates Biological Discovery and Design. Cell
Systems, 2020; DOI: 10.1016/j.cels.2020.09.007 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201015111731.htm

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