Professor Luis Pedro Coelho, QUT Centre for Microbiome Research.

Research published in the journal Cell,1 by a team including Queensland University of Technology computational biologist Associate Professor Luis Pedro Coelho, has used machine learning to identify 863,498 promising antimicrobial peptides (AMPs) – small molecules that can kill or inhibit the growth of infectious microbes, some of which may impact ocular health.

The findings of the study come with a renewed global focus on combatting antimicrobial resistance (AMR) as humanity contends with the growing number of superbugs resistant to current drugs.

“There is an urgent need for new methods for antibiotic discovery,” Prof Coelho, a researcher at the QUT Centre for Microbiome Research, said. The centre studies the structure and function of microbial communities from around the globe.

“It is one of the top public health threats, killing 1.27 million people each year.”

Without intervention, it is estimated that AMR could cause up to 10 million deaths per year by 2050.

“Using artificial intelligence to understand and harness the power of the global microbiome will hopefully drive innovative research for better public health outcomes,” Prof Coelho said.

USING MACHINE LEARNING

The team verified the machine predictions by testing 100 laboratory-made peptides against clinically significant pathogens. They found 79 disrupted bacterial membranes and 63 specifically targeted antibiotic-resistant bacteria such as Staphylococcus aureus, a major pathogen that can infect the tear duct, eyelid, conjunctiva, cornea, anterior and posterior chambers, and the vitreous chamber.2

“Moreover, some peptides helped to eliminate infections in mice; two in particular reduced bacteria by up to four orders of magnitude,” Prof Coelho said.

In a preclinical model, tested on infected mice, treatment with these peptides produced results similar to the effects of polymyxin B – a commercially available antibiotic that is used to treat meningitis, pneumonia, sepsis, and urinary tract infections.

More than 60,000 metagenomes (a collection of genomes within a specific environment), which together contained the genetic makeup of over one million organisms, were analysed to get these results. They came from sources across the globe including marine and soil environments, and human and animal guts.

The resulting AMPSphere – a comprehensive database comprising these novel peptides – has been published as a publicly available, openaccess resource for new antibiotic discovery.

Prof Coelho’s research was conducted as part of his ARC Future Fellowship through the QUT School of Biomedical Science, in collaboration with the Cesar de la Fuente laboratory at the University of Pennsylvania, Fudan University, the European Molecular Biology Laboratory, and APC Microbiome Ireland.

Reference
1. Santos Junior., C.D., Torres, M.D.T., and Coelho, L.P., Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 2024. DOI: 10.1016/j.cell.2024.05.013.
2. O'Callaghan, R.J., The pathogenesis of staphylococcus aureus eye infections. pathogens. 2018 Jan 10;7(1):9. DOI: 10.3390/pathogens7010009.