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Bacteria have become resistant to existing antibiotics, to help the health problem, scientists are exploring antimicrobial peptides — naturally occurring peptides found in organisms to replace the drugs. Most of these are not powerful to fight off infections in humans.
Researchers at MIT and the Catholic University of Brasilia have developed a streamlined approach to developing such drugs. Their new strategy relies on a computer algorithm that mimics the natural process of evolution, has yielded one potential drug candidate that successfully killed bacteria in mice.
Computers can be 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.” Antimicrobial peptides kill microbes in many different ways-they enter microbial cells by damaging their membranes, and once inside, they can disrupt cellular targets such as DNA, RNA, and proteins.
Scientists synthesize hundreds of new variants and test them against different types of bacteria. They created a computer algorithm that incorporates the same principles as Darwin’s theory of natural selection. The algorithm can start with any peptide sequence, generate thousands of variants, and test them for the desired traits that the researchers have specified.
Using computers leads to exploration of many peptides, researchers started with an antimicrobial peptide in the seeds of the guava plant. This peptide, known as Pg-AMP1, has only weak antimicrobial activity. The researchers told the algorithm to come up with peptide sequences with two features that help peptides to penetrate bacterial membranes: a tendency to form alpha helices and a certain level of hydrophobicity.
After the algorithm generated and evaluated tens of thousands of peptide sequences, the researchers synthesized the most promising 100 candidates to test against bacteria grown in lab dishes. The top performer, known as guavanin 2, contains 20 amino acids. Unlike the original Pg-AMP1 peptide, which is rich in the amino acid glycine, guavanin is rich in arginine but has only one glycine molecule.
These differences make guavanin 2 much more potent, especially against a type of bacteria known as Gram-negative. Gram-negative bacteria include many species responsible for the most common hospital-acquired infections, like pneumonia and urinary tract infections.
The researchers tested guavanin 2 in mice with a skin infection caused by a type of Gram-negative bacteria known as Pseudomonas aeruginosa, and discovered that it cleared the infections much more effectively than the original Pg-AMP1 peptide. This shows that designing antimicrobial peptides computationally using an ‘in silico’ evolutionary algorithm is useful for treating drug-resistance bacteria because peptides have the properties needed to serve as antibiotics.
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