Applying a form of AI to sift through large amounts of biological data

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. 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."

Dong Xu, the Paul K. and Dianne Shumaker Professor in bioinformatics

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."

Source:
Journal reference:

Zhu, J., et al. (2022) Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations. Nature Communications. doi.org/10.1038/s41467-022-29331-3.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
PIONEER software breaks down barriers in protein-protein interaction research