Binding sites for protein-making machinery

Date:
August 27, 2020
Source:
ETH Zurich
Summary:
Researchers can predict how tightly a cell's protein synthesis
machinery will bind to RNA sequences - even when dealing with
many billions of different RNA sequences. This binding plays a key
role in determining how much of a specific protein is produced. The
scientists are developing their prediction model using a combination
of synthetic biology experiments and machine learning algorithms.



FULL STORY ========================================================================== Genome sequencing of bacteria, plants and even humans has become a routine process, yet the genome still poses many unanswered questions. One of
these concerns the sites on messenger RNAs (mRNAs) that ribosomes --
the cellular structures responsible for protein synthesis -- bind to
in order to translate genetic information. Currently, the function of
these ribosome binding sites is only partly understood.


==========================================================================
An interdisciplinary team of researchers from the Department of Biosystems Science and Engineering (D-BSSE) at ETH Zurich in Basel has now developed
a new approach that, for the first time, makes it possible to obtain
detailed information on an incredibly large number of these binding
sites in bacteria.

The new approach combines experimental methods of synthetic biology with machine learning.

Precise control over protein production Ribosome binding sites are
short RNA sequences upstream of a gene's coding sequence. In the past, biotechnologists also developed synthetic binding sites.

The ribosomes bind extremely well to some of these, and less well
to others.

The tighter ribosomes are able to bind to a specific variant, the more
often they translate the respective gene and the greater the amount of
the corresponding protein they produce.

Biotechnologists who use bacteria to produce chemicals of interest such as pharmaceuticals can influence the amount of involved proteins in the cell through their choice of ribosome binding sites. "Exerting this kind of
control is particularly important and helpful when incorporating complex
gene networks comprising multiple proteins at the same time. The key
here is to establish an optimal balance amongst the different proteins,"
says Markus Jeschek, senior scientist and group leader at D-BSSE.

An experiment with 300,000 sequences Together with ETH professors Yaakov Benenson and Karsten Borgwardt and members of the respective groups,
Jeschek has now developed a method to determine how tightly ribosomes
bind to hundreds of thousands or more RNA sequences in a single
experiment. Previously this was only possible for a few hundred sequences.

The ETH researchers' approach harnesses deep sequencing, the latest
technology used to sequence DNA and RNA. In the laboratory, the scientists produced over 300,000 different synthetic ribosome binding sites and fused
each of these with a gene for an enzyme that modifies a piece of target
DNA. They introduced the resulting gene constructs into bacteria in order
to see how tightly the ribosomes bind to RNA in each individual case. The better the function of the binding site, the more enzyme is produced in
the cell and the more rapidly the target DNA will be changed. At the
end of the experiment, the researchers can read this change together
with the binding site's RNA sequence using deep sequencing.

Universally applicable approach Since 300,000 represents only a small
fraction of the many billions of theoretically possible ribosome binding
sites, the scientists analysed their data using machine learning
algorithms. "These algorithms can detect complex patterns in large
datasets. With their help, we can predict how tightly ribosomes will
bind to a specific RNA sequence," says Karsten Borgwardt, Professor of
Data Mining. The ETH researchers have made this prediction model freely available as software so that other scientists can make use of it,
and they will soon be introducing an easy-to-use online service as well.

The approach developed by the scientists is universally applicable,
Benenson and Jeschek emphasise, and the team is planning to extend it to
other organisms including human cells. "We're also keen to find out how
genetic information influences the amount of protein that is produced
in a human cell," Benenson says. "This could be particularly useful for
genetic diseases."

========================================================================== Story Source: Materials provided by ETH_Zurich. Original written by
Fabio Bergamin. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Simon Ho"llerer, Laetitia Papaxanthos, Anja Cathrin Gumpinger,
Katrin
Fischer, Christian Beisel, Karsten Borgwardt, Yaakov Benenson,
Markus Jeschek. Large-scale DNA-based phenotypic recording and deep
learning enable highly accurate sequence-function mapping. Nature
Communications, 2020; 11 (1) DOI: 10.1038/s41467-020-17222-4 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200827105913.htm

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