New machine learning method to analyze complex scientific data of
proteins
Method allows faster and more accurate data analysis from NMR
spectrometers

Date:
September 21, 2021
Source:
Ohio State University
Summary:
Scientists have developed a method using machine learning to better
analyze data from a powerful scientific tool: nuclear magnetic
resonance (NMR). One way NMR data can be used is to understand
proteins and chemical reactions in the human body. NMR is closely
related to magnetic resonance imaging (MRI) for medical diagnosis.



FULL STORY ========================================================================== Scientists have developed a method using machine learning to better
analyze data from a powerful scientific tool: nuclear magnetic resonance
(NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis.


==========================================================================
NMR spectrometers allow scientists to characterize the structure of
molecules, such as proteins, but it can take highly skilled human
experts a significant amount of time to analyze that data. This new
machine learning method can analyze the data much more quickly and just
as accurately.

In a study recently published in Nature Communications, the scientists described their process, which essentially teaches computers to untangle complex data about atomic-scale properties of proteins, parsing them
into individual, readable images.

"To be able to use these data, we need to separate them into features from different parts of the molecule and quantify their specific properties,"
said Rafael Bru"schweiler, senior author of the study, Ohio Research
Scholar and a professor of chemistry and biochemistry at The Ohio State University. "And before this, it was very difficult to use computers to identify these individual features when they overlapped." The process, developed by Dawei Li, lead author of the study and a research scientist
at Ohio State's Campus Chemical Instrument Center, teaches computers to
scan images from NMR spectrometers. Those images, known as spectra, appear
as hundreds and thousands of peaks and valleys, which, for example, can
show changes to proteins or complex metabolite mixtures in a biological
sample, such as blood or urine, at the atomic level. The NMR data give important information about a protein's function and important clues
about what is happening in a person's body.

But deconstructing the spectra into readable peaks can be difficult
because often, the peaks overlap. The effect is almost like a mountain
range, where closer, larger peaks obscure smaller ones that may also
carry important information.



========================================================================== "Think of the QR code readers on your phone: NMR spectra are like a QR
code of a molecule -- every protein has its own specific 'QR code,'" Bru"schweiler said. "However, the individual pixels of these 'QR codes'
can overlap with each other to a significant degree. Your phone would
not be able to decipher them.

And that is the problem we have had with NMR spectroscopy and that we were
able to solve by teaching a computer to accurately read these spectra."
The process involves creating an artificial deep neural network, a
multi- layered network of nodes that the computer uses to separate and
analyze data.

The researchers created that network, then taught it to analyze NMR
spectra by feeding spectra that had already been analyzed by a person
into the computer and telling the computer the previously known correct
result. The process of teaching a computer to analyze spectra is almost
like teaching a child to read -- the researchers started with very simple spectra. Once the computer understood that, the researchers moved on
to more complex sets. Eventually, they fed highly complex spectra of
different proteins and from a mouse urine sample into the computer.

The computer, using the deep neural network that had been taught to
analyze spectra, was able to parse out the peaks in the highly complex
sample with the same accuracy as a human expert, the researchers
found. And more, the computer did it faster and highly reproducibly.

Using machine learning as a tool to analyze NMR spectra is just one
key step in the lengthy scientific process of NMR data interpretation, Bru"schweiler said.

But this research enhances the capabilities of NMR spectroscopists,
including the users of Ohio State's new National Gateway Ultrahigh
Field NMR Center, a $17.5 million center funded by the National Science Foundation. The center is expected be commissioned in 2022 and will have
the first 1.2 gigahertz NMR spectrometer in North America.

This work was supported by the National Science Foundation and the
National Institutes of Health.

Other research scientists involved in this study include Alexandar Hansen, Chunhua Yuan and Lei Bruschweiler-Li, all of Ohio State's Campus Chemical Instrument Center.

========================================================================== Story Source: Materials provided by Ohio_State_University. Original
written by Laura Arenschield. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Da-Wei Li, Alexandar L. Hansen, Chunhua Yuan, Lei Bruschweiler-Li,
Rafael
Bru"schweiler. DEEP picker is a deep neural network for accurate
deconvolution of complex two-dimensional NMR spectra. Nature
Communications, 2021; 12 (1) DOI: 10.1038/s41467-021-25496-5 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210921172716.htm

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