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NATURE COMMUNICATIONS, May 28, 2021, ¡°Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery,¡± by Bahar Behsaz, et al. © 2021 Springer Nature Communications. All rights reserved.

To view or purchase this article, please visit: 
https://www.nature.com/articles/s41467-021-23502-4
Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery

Researchers from Carnegie Mellon University¡¯s Computational Biology Department in the School of Computer Science have developed a new process to search for natural product drugs to treat cancers, viral infections and other ailments.

The machine learning algorithms match the signals of a microbe¡¯s metabolites with its genomic signals and identify which likely correspond to a natural product. Knowing that, researchers are better equipped to isolate the natural product and develop it for a possible drug.

In a new study published in Nature Communications, the team was able to scan the metabolomics and genomic data for about 200 strains of microbes. Their new algorithm not only identified the hundreds of natural product drugs the researchers expected to find, but it also discovered four novel natural products that appear promising for future drug development.

The publication outlines the team¡¯s development of NRP-miner, an artificial intelligence tool to aid in discovering non-ribosomal peptides (or NRPs). NRPs are an important type of natural product and are used to make many antibiotics, anticancer drugs and other clinically used medications. They are, however, difficult to detect and even more difficult to identify as potentially useful.

What is unique about this approach is that the technology is very sensitive. It can detect molecules with nanograms of abundance. Therefore, it can discover things that are hid- den under the grass.

That¡¯s important because, uncovering natural products is time and labor intensive, often taking years and millions of dollars. Consequently, major pharmaceutical companies have mostly abandoned the search for new natural products in the past decades.

By applying machine learning algorithms to the study of genomics, however, researchers have created new opportunities to identify and isolate natural products that could be beneficial.

The team¡¯s objective is to push this forward and discover other natural drug candidates and then develop those into a phase that would be attractive to pharmaceutical companies.  To do so they are expanding the NPR-miner¡¯s discovery methods to different classes of natural products at a scale suitable for commercialization. The team is already investigating the four new natural products discovered during their initial study and two have been found to have potential antimalarial properties.

Reference:
- NATURE COMMUNICATIONS, May 28, 2021, ¡°Integrating genomics and metabolomics for scalable non-ribosomal peptide discovery,¡± by Bahar Behsaz, et al. © 2021 Springer Nature Communications. All rights reserved.

To view or purchase this article, please visit: 
https://www.nature.com/articles/s41467-021-23502-4