Artificial intelligence helps to find new natural substances

Date:
October 14, 2021
Source:
Friedrich-Schiller-Universitaet Jena
Summary:
More than a third of all medicines available today are based on
active substances from nature and a research team has developed a
procedure to identify small active substance molecules much more
quickly and easily.



FULL STORY ========================================================================== Secondary natural substances that occur in numerous plants, bacteria and
fungi can be anti-inflammatory, can ward off pathogens or even prevent
the growth of cancer cells. However, making use of the riches provided
by nature's medicine cabinet and identifying new natural substances is time-consuming, costly and labour-intensive. A team of bioinformaticians
at Friedrich Schiller University Jena has now developed a method that
enables much faster and easier identification of small active substance molecules.


========================================================================== Millions of structural data items not yet deciphered To find out which substances are contained in a biological sample such as a plant extract,
a researcher analyses the sample using mass spectrometry. In this
process, the molecules are broken down into fragments and their mass
is determined. "The CSI:FingerID molecule search engine we developed
allows us to search specifically for molecular structures that match
these fragments," says Prof. Sebastian Bo"cker of the University of
Jena. "Whether this search is successful -- i.e., whether the search
result represents the correct structure -- is not something we can
distinguish in this way." There are currently huge collections of
data with billions of mass spectrometry data items from millions of
analyses of biological samples, the vast majority of which have not
been identified as to their structure. This is where COSMIC is now
coming into play, enabling structures to be deciphered automatically
for a large proportion of these as yet unidentified molecules. "To this
end, we use machine-learning methods," explains Martin Hoffmann, lead
author of the new publication. "First, the mass spectrum of the sample
under examination is compared with the available structural data." As
a result, you get -- as in a Google search -- a more or less extensive
list of possible hits. "Our method now indicates how confident one can
be that the hit found in the first place is actually the structure we
are looking for," Hoffmann adds. To do this, COSMIC determines a score
that evaluates the quality of the suggested hit and deduces whether it
is correct or incorrect.

New bile acids discovered Bo"cker and his team have been able to
demonstrate how well their method really works, in cooperation with
colleagues from the University of California, San Diego. They studied
mass spectrometry data from the digestive system of mice, searching
for as yet unknown bile acids. For this purpose, more than 28,000
theoretically possible bile acid structures were constructed and compared
with the measurement data from the microbiome of the mice. The subsequent analysis with COSMIC yielded a total of 11 new, previously completely
unknown bile acid structures. Two of these have since been confirmed
using specifically synthesised reference samples.

"This shows, firstly, that our method works reliably," emphasises
Sebastian Bo"cker. Secondly, COSMIC makes it possible to accelerate substantially the search for new and interesting substances, because the screening can be performed completely automatically, without any manual
effort and in a very short time. Bo"cker expects that in the coming years,
it will be possible to clarify thousands of new molecular structures in
this way.

========================================================================== Story Source: Materials provided by
Friedrich-Schiller-Universitaet_Jena. Original written by Ute
Scho"nfelder. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Martin A. Hoffmann, Louis-Fe'lix Nothias, Marcus Ludwig, Markus
Fleischauer, Emily C. Gentry, Michael Witting, Pieter C. Dorrestein,
Kai Du"hrkop, Sebastian Bo"cker. High-confidence structural
annotation of metabolites absent from spectral libraries. Nature
Biotechnology, 2021; DOI: 10.1038/s41587-021-01045-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/10/211014131201.htm

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