Deep learning takes on synthetic biology
Computational algorithms enable identification and optimization of RNA-
based tools for myriad applications

Date:
October 7, 2020
Source:
Wyss Institute for Biologically Inspired Engineering at Harvard
Summary:
Machine learning is helping biologists solve hard problems,
including designing effective synthetic biology tools. Scientists
have now created a set of algorithms that can effectively predict
the efficacy of thousands of RNA-based sensors called toehold
switches, allowing the rapid identification and optimization of
sequences that can act as biological sensors for medicine and
other applications.



FULL STORY ==========================================================================
DNA and RNA have been compared to "instruction manuals" containing the information needed for living "machines" to operate. But while electronic machines like computers and robots are designed from the ground up to
serve a specific purpose, biological organisms are governed by a much
messier, more complex set of functions that lack the predictability of
binary code. Inventing new solutions to biological problems requires
teasing apart seemingly intractable variables -- a task that is daunting
to even the most intrepid human brains.


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Two teams of scientists from the Wyss Institute at Harvard University
and the Massachusetts Institute of Technology have devised pathways
around this roadblock by going beyond human brains; they developed a
set of machine learning algorithms that can analyze reams of RNA-based "toehold" sequences and predict which ones will be most effective at
sensing and responding to a desired target sequence. As reported in
two papers published concurrently today in Nature Communications, the algorithms could be generalizable to other problems in synthetic biology
as well, and could accelerate the development of biotechnology tools to
improve science and medicine and help save lives.

"These achievements are exciting because they mark the starting point
of our ability to ask better questions about the fundamental principles
of RNA folding, which we need to know in order to achieve meaningful discoveries and build useful biological technologies," said Luis Soenksen, Ph.D., a Postdoctoral Fellow at the Wyss Institute and Venture Builder
at MIT's Jameel Clinic who is a co-first author of the first of the
two papers.

Getting ahold of toehold switches The collaboration between data
scientists from the Wyss Institute's Predictive BioAnalytics Initiative
and synthetic biologists in Wyss Core Faculty member Jim Collins' lab
at MIT was created to apply the computational power of machine learning,
neural networks, and other algorithmic architectures to complex problems
in biology that have so far defied resolution. As a proving ground for
their approach, the two teams focused on a specific class of engineered
RNA molecules: toehold switches, which are folded into a hairpin-like
shape in their "off" state. When a complementary RNA strand binds to a "trigger" sequence trailing from one end of the hairpin, the toehold
switch unfolds into its "on" state and exposes sequences that were
previously hidden within the hairpin, allowing ribosomes to bind to and translate a downstream gene into protein molecules. This precise control
over the expression of genes in response to the presence of a given
molecule makes toehold switches very powerful components for sensing
substances in the environment, detecting disease, and other purposes.

However, many toehold switches do not work very well when tested experimentally, even though they have been engineered to produce a desired output in response to a given input based on known RNA folding rules.

Recognizing this problem, the teams decided to use machine learning to
analyze a large volume of toehold switch sequences and use insights from
that analysis to more accurately predict which toeholds reliably perform
their intended tasks, which would allow researchers to quickly identify high-quality toeholds for various experiments.



==========================================================================
The first hurdle they faced was that there was no dataset of toehold
switch sequences large enough for deep learning techniques to analyze effectively. The authors took it upon themselves to generate a dataset
that would be useful to train such models. "We designed and synthesized
a massive library of toehold switches, nearly 100,000 in total, by systematically sampling short trigger regions along the entire genomes
of 23 viruses and 906 human transcription factors," said Alex Garruss, a Harvard graduate student working at the Wyss Institute who is a co-first
author of the first paper. "The unprecedented scale of this dataset
enables the use of advanced machine learning techniques for identifying
and understanding useful switches for immediate downstream applications
and future design." Armed with enough data, the teams first employed
tools traditionally used for analyzing synthetic RNA molecules to see if
they could accurately predict the behavior of toehold switches now that
there were manifold more examples available. However, none of the methods
they tried -- including mechanistic modeling based on thermodynamics
and physical features -- were able to predict with sufficient accuracy
which toeholds functioned better.

A picture is worth a thousand base pairs The researchers then explored
various machine learning techniques to see if they could create
models with better predictive abilities. The authors of the first
paper decided to analyze toehold switches not as sequences of bases,
but rather as two-dimensional "images" of base-pair possibilities. "We
know the baseline rules for how an RNA molecule's base pairs bond with
each other, but molecules are wiggly -- they never have a single perfect
shape, but rather a probability of different shapes they could be in,"
said Nicolaas Angenent-Mari, a MIT graduate student working at the Wyss Institute and co-first author of the first paper. "Computer vision
algorithms have become very good at analyzing images, so we created
a picture-like representation of all the possible folding states of
each toehold switch, and trained a machine learning algorithm on those
pictures so it could recognize the subtle patterns indicating whether a
given picture would be a good or a bad toehold." Another benefit of their visually-based approach is that the team was able to "see" which parts of
a toehold switch sequence the algorithm "paid attention" to the most when determining whether a given sequence was "good" or "bad." They named this interpretation approach Visualizing Secondary Structure Saliency Maps,
or VIS4Map, and applied it to their entire toehold switch dataset.

VIS4Map successfully identified physical elements of the toehold switches
that influenced their performance, and allowed the researchers to conclude
that toeholds with more potentially competing internal structures were "leakier" and thus of lower quality than those with fewer such structures, providing insight into RNA folding mechanisms that had not been discovered using traditional analysis techniques.



========================================================================== "Being able to understand and explain why certain tools work or don't work
has been a secondary goal within the artificial intelligence community
for some time, but interpretability needs to be at the forefront of
our concerns when studying biology because the underlying reasons for
those systems' behaviors often cannot be intuited," said Jim Collins,
Ph.D., the senior author of the first paper. "Meaningful discoveries and disruptions are the result of deep understanding of how nature works,
and this project demonstrates that machine learning, when properly
designed and applied, can greatly enhance our ability to gain important insights about biological systems." Collins is also the Termeer Professor
of Medical Engineering and Science at MIT.

Now you're speaking my language While the first team analyzed toehold
switch sequences as 2D images to predict their quality, the second team
created two different deep learning architectures that approached the
challenge using orthogonal techniques. They then went beyond predicting
toehold quality and used their models to optimize and redesign poorly performing toehold switches for different purposes, which they report
in the second paper.

The first model, based on a convolutional neural network (CNN) and
multi-layer perceptron (MLP), treats toehold sequences as 1D images, or
lines of nucleotide bases, and identifies patterns of bases and potential interactions between those bases to predict good and bad toeholds. The
team used this model to create an optimization method called STORM (Sequence-based Toehold Optimization and Redesign Model), which allows
for complete redesign of a toehold sequence from the ground up. This
"blank slate" tool is optimal for generating novel toehold switches
to perform a specific function as part of a synthetic genetic circuit,
enabling the creation of complex biological tools.

"The really cool part about STORM and the model underlying it is that
after seeding it with input data from the first paper, we were able to fine-tune the model with only 168 samples and use the improved model
to optimize toehold switches. That calls into question the prevailing assumption that you need to generate massive datasets every time you want
to apply a machine learning algorithm to a new problem, and suggests that
deep learning is potentially more applicable for synthetic biologists
than we thought," said co-first author Jackie Valeri, a graduate student
at MIT and the Wyss Institute.

The second model is based on natural language processing (NLP), and
treats each toehold sequence as a "phrase" consisting of patterns of
"words," eventually learning how certain words are put together to make
a coherent phrase. "I like to think of each toehold switch as a haiku
poem: like a haiku, it's a very specific arrangement of phrases within
its parent language -- in this case, RNA. We are essentially training
this model to learn how to write a good haiku by feeding it lots and
lots of examples," said co-first author Pradeep Ramesh, Ph.D., a Visiting Postdoctoral Fellow at the Wyss Institute and Machine Learning Scientist
at Sherlock Biosciences.

Ramesh and his co-authors integrated this NLP-based model with the
CNN-based model to create NuSpeak (Nucleic Acid Speech), an optimization approach that allowed them to redesign the last 9 nucleotides of a given toehold switch while keeping the remaining 21 nucleotides intact. This technique allows for the creation of toeholds that are designed to detect
the presence of specific pathogenic RNA sequences, and could be used to
develop new diagnostic tests.

The team experimentally validated both of these platforms by optimizing
toehold switches designed to sense fragments from the SARS-CoV-2 viral
genome. NuSpeak improved the sensors' performances by an average of 160%,
while STORM created better versions of four "bad" SARS-CoV-2 viral RNA
sensors whose performances improved by up to 28 times.

"A real benefit of the STORM and NuSpeak platforms is that they enable
you to rapidly design and optimize synthetic biology components, as we
showed with the development of toehold sensors for a COVID-19 diagnostic,"
said co-first author Katie Collins, an undergraduate MIT student at the
Wyss Institute who worked with MIT Associate Professor Timothy Lu, M.D.,
Ph.D., a corresponding author of the second paper.

"The data-driven approaches enabled by machine learning open the door to
really valuable synergies between computer science and synthetic biology,
and we're just beginning to scratch the surface," said Diogo Camacho,
Ph.D., a corresponding author of the second paper who is a Senior Bioinformatics Scientist and co-lead of the Predictive BioAnalytics
Initiative at the Wyss Institute. "Perhaps the most important aspect of
the tools we developed in these papers is that they are generalizable
to other types of RNA-based sequences such as inducible promoters and
naturally occurring riboswitches, and therefore can be applied to a wide
range of problems and opportunities in biotechnology and medicine."
Additional authors of the papers include Wyss Core Faculty member and
Professor of Genetics at HMS George Church, Ph.D.; and Wyss and MIT
Graduate Students Miguel Alcantar and Bianca Lepe.

"Artificial intelligence is wave that is just beginning to impact
science and industry, and has incredible potential for helping to solve intractable problems. The breakthroughs described in these studies
demonstrate the power of melding computation with synthetic biology at
the bench to develop new and more powerful bioinspired technologies,
in addition to leading to new insights into fundamental mechanisms of biological control," said Don Ingber, M.D., Ph.D., the Wyss Institute's Founding Director. Ingber is also the Judah Folkman Professor of Vascular Biology at Harvard Medical School and the Vascular Biology Program at
Boston Children's Hospital, as well as Professor of Bioengineering at
Harvard's John A. Paulson School of Engineering and Applied Sciences.

This work was supported by the DARPA Synergistic Discovery and Design
program, the Defense Threat Reduction Agency, the Paul G. Allen Frontiers Group, the Wyss Institute for Biologically Inspired Engineering, Harvard University, the Institute for Medical Engineering and Science, the Massachusetts Institute of Technology, the National Science Foundation,
the National Human Genome Research Institute, the Department of Energy,
the National Institutes of Health, and a CONACyT grant.


========================================================================== Story Source: Materials provided
by Wyss_Institute_for_Biologically_Inspired_Engineering_at
Harvard. Original written by Lindsay Brownell. Note: Content may be
edited for style and length.


==========================================================================


Link to news story: https://www.sciencedaily.com/releases/2020/10/201007085615.htm

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