New research technique effectively identifies antibiotic compounds

If you are looking for a needle in a haystack, it's best to know what hay looks like. An international team of researchers has applied this idea to research new pharmaceutical products, developing a technique that reduces the chances of simply rediscovering known compounds.

In an article published today in the journal Nature Communications, researchers from Carnegie Mellon University; the University of California San Diego; and St Petersburg State University in Russia describe a new way to search for vast repositories of compounds produced by microbes. By analyzing the mass spectra of the compounds, they were able to identify known compounds in the repository and eliminate them from further analysis, focusing instead on unknown variants – needles in the haystack – which could potentially be antibiotics better or more effective. , anticancer drugs or other pharmaceutical products.

In just one week, running on 100 computers, the algorithm called Dereplicator +, sorted out of a billion mass spectra in UC San Diego's Global Natural Products Social molecular network, has identified more than 5,000 Promising and unknown compounds that deserved further study, Hosein said. Mohimani, assistant professor in the department of computational biology of CMU and first author of the article.

The algorithm that powers this molecular search engine is now available to any investigator to investigate additional repositories.

In the past, mass spectrometry data repositories were underutilized due to the difficulty of navigating them and the efforts made to date by high levels of rediscovery of known compounds.

"It's amazing how many times people have rediscovered penicillin," Mohimani said.

The analysis of the mass spectra of the compounds – essentially a measurement of the masses in an ionized sample – constitutes a relatively inexpensive way of identifying new pharmaceuticals. But existing techniques were largely limited to peptides, which have simple structures such as chains and loops.

"We were only looking at the tip of the iceberg," Mohimani said.

To analyze the largest number of complex compounds with entangled structures and many loops and branches, researchers have developed a method to predict how a mass spectrometer would divide molecules. Starting with the weakest rings, the method simulated what would happen when the molecules separated. Using 5,000 known compounds and their mass spectra, they formed a computer model that could then be used to predict the degradation of other compounds.

Mohimani said that Dereplicator + can not only identify known compounds that it is not necessary to study further, but that it can also look for less common variants of known compounds that would probably not go through a sample.