Antibiotic resistance is one of the major problems threatening human health. As a result, many studies has been done on this issue, and many scientists around the world are working towards ending the crisis that is killing more than a million people worldwide.
Rockefeller University scientists have synthesized a new antibiotic using computer models of bacterial gene products. It turns out that it even kills bacteria that are resistant to other antibiotics. The molecule called cilagicin has been tested in mice and uses a new mechanism to attack MRSA, C. diff and several other deadly pathogens.
“This is not only an amazing new molecule, but it is also the validation of a new approach to drug discovery,” said Sean F. Brady, Professor Evnin and corresponding author of the study in a press release published by institution. “This study is an example of computational biology, genetic sequencing & synthetic chemistry coming together to unlock the secrets of bacterial evolution.”
Bacterias killing each other
Since the growth of bacteria is about them inventing new ways to kill each other, it is not surprising that most antibiotics are based on bacteria. However, bacteria gaining resistance also leads to the emergence of problems such as antibiotic-resistant bacteria, leading to the need for new active compounds.
However, countless antibiotics probably hidden inside stubborn bacterial genomes that are difficult or impossible to test in the lab. “Many antibiotics come from bacteria, but most bacteria can’t be grown in lab,” says Brady. “We’re probably missing-out most of the antibiotics.”
For the past 15 years, Brady’s lab has taken an alternative approach to finding anti-bacterial genes in soil and growing them inside more lab friendly bacteria. But this approach also has its own limitations. The gene sequences contained in so-called biosynthetic gene clusters, groups of genes that work together to code together for many proteins, are where most anti-biotics originate. But with current technology, these clusters are often inaccessible.
Unable-to-unlock many clusters bacterial genes, Brady and his colleagues turned to algorithms. Modern algorithms can predict the structure of antibiotic-like compounds that bacteria with these sequences would create by teasing apart genetic instructions in DNA sequence. Organic chemists can then use the data to synthesize predicted structures in the lab.
A promising compound
Zonggiang Wang and Bimal Koirala, postdoctoral colleagues at Brady’s lab, began studying the huge database of gene sequences with the goal of finding potential bacterial genes thought to be important in killing other bacteria and has not been studied before.
The “cil” gene cluster, which has not been studied in this context, stands out because it closely resembles other genes used in the production of antibiotics. Next, researchers fed its related sequences into an algorithm that proposed a handful of compounds that “cil” can produce. One compound, named cilagicine, has been shown to be an effective antibiotic.
Cilagicin was found to work by binding two molecules, C55-P & C55-PP, both of which support the bacterial cell walls. Bacteria frequently develop resistance to existing antibiotics by combining the cell wall with the rest. Drugs like bacitracin bind one of those 2 molecules but never both. The team therefore suspects that cilagicine’s capacity to shut-down both molecules may actually be an insurmountable barrier to prevents resistance.
Although cilagicin has not been tested in humans, the Brady lab will perform more synthesis to improve this compound in further studies and test it in animal models against a wide range of infections to identify the most useful diseases to treat.
The results of the study have been published in the journal Science.
