Antibiotic resistance is a growing issue in which harmful bacteria in the body are no longer receptive to the effects of antibiotics. Because of this issue, more and more patients struggle with everything from common illnesses to much more severe bacterial infections that could cause life-threatening harm.
One technique that could combat antibiotic resistance is finding variants of known antibiotics, or peptidic natural products (PNPs). Unfortunately, finding these variants has been an arduous and time-consuming process. Until now: a group of American and Russian computer scientists has created an antibiotic algorithm that, by rapidly sorting through databases, can discover 10 times more new variants of PNPs than all previous efforts combined.
The algorithm, known as VarQuest, is described in the latest issue of the journal Nature Microbiology. Hosein Mohimani, an assistant professor in Carnegie Mellon University’s Computational Biology Departmen, said in a press release that VarQuest completed a search that could have taken hundreds of years of computations traditionally.
Mohimani also said that the study expanded their understanding of the microbial world. Not only does finding more variants quickly increase researchers’ ability to formulate alternative antibiotics; it can also provide vital information to microbiologists.
“Our results show that the antibiotics produced by microbes are much more diverse than had been assumed,” said Mohimani in a press release.
Mohimani noted that, because VarQuest was able to find over one thousand variants and in such a short amount of time, it could give microbiologists a larger perspective, perhaps alerting them to trends or patterns that wouldn’t otherwise be noticeable.
VarQuest’s success stands on the shoulders of computing progress made within the past few years. High-throughput methods have advanced, allowing samples to be processed in batches instead of one at a time, making the process much faster. Additionally, the effort has been supported by the Global Natural Products Social (GNPS) molecular network, launched in 2016. This is a database in which researchers from around the world collect data on natural products based on their mass spectra, the chemical analysis of how charge is distributed through a substance’s mass. Using this database alongside VarQuest could drastically enhance drug discovery abilities.
“Natural product discovery is turning into a Big Data territory, and the field has to prepare for this transformation in terms of collecting, storing and making sense of Big Data,” Mohimani said of this growing data and scientists’ ability to access it. “VarQuest is the first step toward digesting the Big Data already collected by the community.”
Every time a person consumes an antibiotic, evolution pushes bacterial species to develop resistance and multiply. Children and elderly adults have the highest rates of antibiotic use, but are also higher-risk groups to begin with, making this a particularly concerning issue with them. Many partially attribute the drastic growth of the resistance problem to the needless prescription of antibiotics to patients with viral illnesses, for whom antibiotics would have no effect but to create resistance.
This issue is only going to get worse if steps are not taken to prevent resistance in the first place. However, as solutions are crafted, working antibiotics are still needed for those facing both resistance and infection. This antibiotic algorithm will be a critical tool in mitigating the effects of resistance while also giving microbiologists a big-picture view, hopefully propelling research forward.