Algorithm-aided antibiotic hunt yields powerful new drug candidate

Algorithm-aided antibiotic hunt yields powerful new drug candidate
A new algorithm-aided technique to discover antibiotic compounds has borne its first fruit
A new algorithm-aided technique to discover antibiotic compounds has borne its first fruit
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A new algorithm-aided technique to discover antibiotic compounds has borne its first fruit
A new algorithm-aided technique to discover antibiotic compounds has borne its first fruit

Algorithms have helped uncover a new antibiotic candidate that shows promise against some particularly nasty bugs, using a novel mode of attack that should be hard for them to develop resistance to. Most importantly it could unlock a whole new arsenal of antibiotics.

We humans aren’t the only organisms that want to kill bacteria – nature has developed a wide range of antibacterial compounds, many of them used by bacteria themselves to gain the upper hand in the eons-long turf war against other bacteria.

Most of our antibiotics are sourced from this arsenal, originally grown from cultures of bacteria and later synthesized into more potent forms. The problem is, over time bacteria evolve resistance to these drugs, forcing us to make new ones, until they inevitably become resistant to those as well. Progress has slowed drastically in recent decades as we start to run out of the easiest bacteria to work with, leaving antibiotic-resistant bacteria looming as one of the world’s most pressing health threats.

“Many antibiotics come from bacteria, but most bacteria can’t be grown in the lab,” said Sean Brady, corresponding author of the study. “It follows that we’re probably missing out on most antibiotics.”

To help parse through the possibilities much faster, the Rockefeller researchers used algorithms to investigate what are known as biosynthetic gene clusters. These are groups of genes that code for a series of proteins – including some that may have antibacterial properties – but are just too numerous and fiddly for humans to sort through.

“Bacteria are complicated, and just because we can sequence a gene doesn’t mean we know how the bacteria would turn it on to produce proteins,” said Brady. “There are thousands and thousands of uncharacterized gene clusters, and we have only ever figured out how to activate a fraction of them.”

But the algorithms can sort through these gene clusters much faster, and pick out the most promising candidate compounds that could have antibacterial effects. From there, human chemists can then synthesize that much shorter list of compounds and test them.

And sure enough, this process turned out one particularly promising compound, which the team named cilagicin. It originated in a gene cluster called “cil,” which was selected because of its similarity to other antibiotic-producing genes.

In lab tests, cilagicin was able to kill bacteria reliably, including several strains that are resistant to existing antibiotics. Importantly, it didn’t harm human cells, and was able to treat bacterial infections in live mice. Most impressively however is that it even managed to kill bacteria that the researchers specifically engineered to resist the drug.

On closer inspection, the team discovered the molecular secrets behind its strength. When it attacks bacteria, cilagicin binds two molecules, called C55-P and C55-PP, which bacteria use to build their cell walls. Existing antibiotics are known to bind one molecule or the other, but resistant bacteria can still use the second to maintain a cell wall. By taking out both at once, cilagicin may prevent this kind of resistance.

As promising as the study seems, the team cautions that there is still plenty of work left to do before it could find use in humans. The researchers plan to optimize the compound and run more tests in animals against a range of other bacteria. Most excitingly though, cilagicin might just be the first of many new antibiotics discovered using this method.

“This work is a prime example of what could be found hidden within a gene cluster,” said Brady. “We think that we can now unlock large numbers of novel natural compounds with this strategy, which we hope will provide an exciting new pool of drug candidates.”

The research was published in the journal Science.

Source: Rockefeller University

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