Researchers use artificial intelligence language tools to decode
molecular movements
Algorithms--like the ones that fill in words as people type--can learn to predict how and when proteins form different shapes.

Date:
October 9, 2020
Source:
University of Maryland
Summary:
Researchers used language processing AI to turn molecular movements
into stories that reveal what forms a protein can take and how and
when it changes form -- key information for understanding disease
and developing targeted therapeutics.



FULL STORY ==========================================================================
By applying natural language processing tools to the movements of protein molecules, University of Maryland scientists created an abstract language
that describes the multiple shapes a protein molecule can take and how
and when it transitions from one shape to another.


==========================================================================
A protein molecule's function is often determined by its shape
and structure, so understanding the dynamics that control shape and
structure can open a door to understanding everything from how a protein
works to the causes of disease and the best way to design targeted drug therapies. This is the first time a machine learning algorithm has been
applied to biomolecular dynamics in this way, and the method's success
provides insights that can also help advance artificial intelligence
(AI). A research paper on this work was published on October 9, 2020,
in the journal Nature Communications.

"Here we show the same AI architectures used to complete sentences
when writing emails can be used to uncover a language spoken by the
molecules of life," said the paper's senior author, Pratyush Tiwary, an assistant professor in UMD's Department of Chemistry and Biochemistry
and Institute for Physical Science and Technology. "We show that the
movement of these molecules can be mapped into an abstract language,
and that AI techniques can be used to generate biologically truthful
stories out of the resulting abstract words." Biological molecules are constantly in motion, jiggling around in their environment. Their shape
is determined by how they are folded and twisted. They may remain in
a given shape for seconds or days before suddenly springing open and
refolding into a different shape or structure. The transition from one
shape to another occurs much like the stretching of a tangled coil that
opens in stages. As different parts of the coil release and unfold,
the molecule assumes different intermediary conformations.

But the transition from one form to another occurs in picoseconds
(trillionths of a second) or faster, which makes it difficult for
experimental methods such as high-powered microscopes and spectroscopy
to capture exactly how the unfolding happens, what parameters affect the unfolding and what different shapes are possible. The answers to those questions form the biological story that Tiwary's new method can reveal.

Tiwary and his team applied Newton's laws of motion -- which can predict
the movement of atoms within a molecule -- with powerful supercomputers, including UMD's Deepthought2, to develop statistical physics models that simulate the shape, movement and trajectory of individual molecules.

Then they fed those models into a machine learning algorithm, like the one Gmail uses to automatically complete sentences as you type. The algorithm approached the simulations as a language in which each molecular movement
forms a letter that can be strung together with other movements to make
words and sentences. By learning the rules of syntax and grammar that
determine which shapes and movements follow one another and which don't,
the algorithm predicts how the protein untangles as it changes shape
and the variety of forms it takes along the way.

To demonstrate that their method works, the team applied it to a small biomolecule called riboswitch, which had been previously analyzed
using spectroscopy. The results, which revealed the various forms the riboswitch could take as it was stretched, matched the results of the spectroscopy studies.

"One of the most important uses of this, I hope, is to develop drugs that
are very targeted," Tiwary said. "You want to have potent drugs that bind
very strongly, but only to the thing that you want them to bind to. We
can achieve that if we can understand the different forms that a given biomolecule of interest can take, because we can make drugs that bind
only to one of those specific forms at the appropriate time and only
for as long as we want." An equally important part of this research is
the knowledge gained about the language processing system Tiwary and his
team used, which is generally called a recurrent neural network, and in
this specific instance a long short-term memory network. The researchers analyzed the mathematics underpinning the network as it learned the
language of molecular motion. They found that the network used a kind of
logic that was similar to an important concept from statistical physics
called path entropy. Understanding this opens opportunities for improving recurrent neural networks in the future.

"It is natural to ask if there are governing physical principles making
AI tools successful," Tiwary said. "Here we discover that, indeed,
it is because the AI is learning path entropy. Now that we know this,
it opens up more knobs and gears we can tune to do better AI for biology
and perhaps, ambitiously, even improve AI itself. Anytime you understand
a complex system such as AI, it becomes less of a black-box and gives
you new tools for using it more effectively and reliably."

========================================================================== Story Source: Materials provided by University_of_Maryland. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Sun-Ting Tsai, En-Jui Kuo, Pratyush Tiwary. Learning molecular
dynamics
with simple language model built upon long short-term memory
neural network. Nature Communications, 2020; 11 (1) DOI:
10.1038/s41467-020- 18959-8 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201009084940.htm

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