Using AI to analyze large amounts of biological data

Date:
May 5, 2022
Source:
University of Missouri-Columbia
Summary:
Researchers are applying a form of artificial intelligence (AI) -
- previously used to analyze how National Basketball Association
(NBA) players move their bodies -- to now help scientists develop
new drug therapies for medical treatments targeting cancers and
other diseases.



FULL STORY ========================================================================== Researchers at the University of Missouri are applying a form of
artificial intelligence (AI) -- previously used to analyze how National Basketball Association (NBA) players move their bodies -- to now help scientists develop new drug therapies for medical treatments targeting
cancers and other diseases.


==========================================================================
The type of AI, called a graph neural network, can help scientists with speeding up the time it takes to sift through large amounts of data
generated by studying protein dynamics. This approach can provide new
ways to identify target sites on proteins for drugs to work effectively,
said Dong Xu, a Curators' Distinguished Professor in the Department
of Electrical Engineering and Computer Science at the MU College of
Engineering and one of the study's authors.

"Previously, drug designers may have known about a couple places on a
protein's structure to target with their therapies," said Xu, who is
also the Paul K. and Dianne Shumaker Professor in bioinformatics. "A
novel outcome of this method is that we identified a pathway between
different areas of the protein structure, which could potentially allow scientists who are designing drugs to see additional possible target
sites for delivering their targeted therapies. This can increase the
chances that the therapy may be successful." Xu said they can also
simulate how proteins can change in relation to different conditions,
such as the development of cancer, and then use that information to
infer their relationships with other bodily functions.

"With machine learning we can really study what are the important
interactions within different areas of the protein structure," Xu
said. "Our method provides a systematic review of the data involved
when studying proteins, as well as a protein's energy state, which could
help when identifying any possible mutation's effect. This is important
because protein mutations can enhance the possibility of cancers and
other diseases developing in the body." "Neural relational inference
to learn long-range allosteric interactions in proteins from molecular
dynamics simulations" was published in Nature Communications. Juexin Wang
at MU; and Jingxuan Zhu and Weiwei Han at Jilin University in China, also contributed to this study. Funding was provided by the China Scholarship Council and the Overseas Cooperation Project of Jilin Province, which
were used to support Jingxuan Zhu to conduct this research at MU, as well
as the National Institute of General Medical Sciences of the National Institutes of Health. The content is solely the responsibility of the
authors and does not necessarily represent the official views of the
funding agencies.


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


========================================================================== Journal Reference:
1. Jingxuan Zhu, Juexin Wang, Weiwei Han, Dong Xu. Neural relational
inference to learn long-range allosteric interactions in proteins
from molecular dynamics simulations. Nature Communications, 2022;
13 (1) DOI: 10.1038/s41467-022-29331-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/05/220505143820.htm

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