Machine-Learning Discovery And Design Of Membrane-Active Peptides For Biomedicine

There are approximately 1,100 known antimicrobial peptides (AMP) with diverse sequences that can permeate microbial membranes. To help discover the "blueprint" for natural AMP sequences, researchers from the University of Illinois at Urbana-Champaign and the University of California, Los Angeles, have developed a new machine learning approach to discover and design alpha-helical membrane active peptides based on their physicochemical properties. "In this work, we have trained a machine learning classifier--known as a support vector machine--to recognize membrane activity and experimentally calibrated the recognition metric by peptide synthesis and characterization," explained Andrew Ferguson, an assistant professor of materials science and engineering at Illinois. "We use machine learning to not only discover new membrane active peptides, but to also identify membrane activity in known peptides with previously defined functions leading us to discover membrane activity in diverse and unexpected peptide families. "Since getting cargo into a cell is important for many applications, we anticipate that this tool can have broad biomedical implications including in immunotherapy and in broad-spectrum membrane-active antimicrobial peptides to combat the rising incidence of drug resistance, design of cationic cell-penetrating peptides for nucleic acid transfection into cells, and in targeting and permeating anticancer therapeutics into tumors," added Ferguson, who was the senior computational investigator for the project. In this collaborative work, the Illinois researchers developed the computational innovations, with the experimental testing of the predictions accomplished at UCLA. The results, which highlight the difference between the efficacy of an antimicrobial and its recognizability as such, are surprising. "AMPs do not share a common core structure, but tend to be short, cationic, and amphiphilic," Ferguson said. "By training our machine learning classifier over a training set comprising peptides with known antimicrobial activity (hits) and decoy peptides with no activity (misses), the classifier learned the physical and chemical properties of a peptide that make for good membrane activity.