Algorithm-Aided Synthesis of Superior Antimicrobials
Hospital-acquired infections are a major global health concern and represent the sixth leading cause of death in the United States, with an estimated cost of ~$10 billion annually.
Infections caused by Gram-negative bacteria have been associated with more than 60% of pneumonia cases and more than 70% of urinary tract infections in intensive care units. Besides, such bacteria are highly efficient in generating mutants and sharing genes that encode for mechanisms of antibiotic resistance.
It has been recently estimated that 30 million sepsis cases occur worldwide each year, and potentially 5 million deaths occur as a result of antibiotic-resistant infections. Unfortunately, in the past two decades only two classes of antibiotics have reached the market, oxazolidinones and cyclic lipopeptides, and both of these drugs are limited as they only target Gram-positive bacteria.
Now, scientists at the MIT and Catholic University of Brasilia have come up with a streamlined approach to developing antimicrobial peptides as potential drugs to fight antibiotic-resistant bacteria. “We can use computers to do a lot of the work for us, as a discovery tool of new antimicrobial peptide sequences,” says Cesar de la Fuente-Nunez, an MIT postdoc and Areces Foundation Fellow. “This computational approach is much more cost-effective and much more time-effective
.”The researchers applied Charles Darwin’s theory of natural selection to their algorithm, which then generated thousands of variants from a peptide sequence and tested the variants for desired traits specified by the researchers.
The team began with an antimicrobial peptide found in the seeds of a guava plant, known as Pg-AMP1, and asked the algorithm to produce peptide sequences which tend to form alpha helices and have a particular level of hydrophobicity, both features that help peptides penetrate bacterial membranes.
The algorithm can start with any peptide sequence, generate thousands of variants, and test them for the desired traits that the researchers have specified. “By using this approach, we were able to explore many, many more peptides than if we had done this manually. Then we only had to screen a tiny fraction of the entirety of the sequences that the computer was able to browse through,” De la Fuente-Nunez says.
The algorithm then generated and tested tens of thousands of peptide sequences, and human scientists tested the top 100 candidates against bacteria grown in lab dishes. The best performer, guavanin 2, contains 20 amino acids and is rich in arginine, features that make the peptide much more potent, especially against Gram-negative bacteria, a category which includes species responsible for pneumonia and urinary tract infections.
The team then tested this out by employing animal models- testing guavanin 2 in mice with a skin infection caused by a type of Gram-negative bacteria known as Pseudomonas aeruginosa, and found that it cleared the infections much more effectively than the original Pg-AMP1 peptide.
De la Fuente-Nunez and his colleagues now plan to further develop guavanin 2 for potential human use, and they also plan to use their algorithm to seek other potent antimicrobial peptides. There are currently no artificial antimicrobial peptides approved for use in human patients.
