Computer algorithm helps develop ultra-powerful peptides

Researchers at MIT and the Catholic University of Brasilia have now developed a streamlined approach to developing such drugs. Their new strategy, which relies on a computer algorithm that mimics the natural process of evolution, has already yielded one potential drug candidate that successfully killed bacteria in mice.

“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.”

De la Fuente-Nunez and Octavio Franco of the Catholic University of Brasilia and the Dom Bosco Catholic University are the corresponding authors of the paper, which appears in Nature Communications. Timothy Lu, an MIT associate professor of electrical engineering and computer science, and of biological engineering, is also an author.

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.

In their search for more powerful, artificial antimicrobial peptides, scientists typically synthesise hundreds of new variants, which is a laborious and time-consuming process, and then test them against different types of bacteria.

De la Fuente-Nunez and his colleagues wanted to find a way to make computers do most of the design work. To achieve that, the researchers 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.

“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.

In this study, the researchers began with an antimicrobial peptide found 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 synthesised 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.

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Image credit: MIT.