Novel approaches are needed to combat antibiotic resistance. Here, we describe a computational-experimental framework for the discovery of novel cryptic antimicrobial peptides (AMPs). The computational platform, based on previously validated antimicrobial scoring functions, indicated the activation peptide of pepsin A, the main human stomach protease, and its N- and C-terminal halves as antimicrobial peptides. The three peptides from pepsinogen A3 isoform were prepared in a recombinant form using a fusion carrier specifically developed to express toxic peptides in Escherichia coli. Recombinant pepsinogen A3-derived peptides proved to be wide-spectrum antimicrobial agents with MIC values in the range 1.56-50 μM (1.56-12.5 μM for the whole activation peptide). Moreover, the activation peptide was bactericidal at pH 3.5 for relevant foodborne pathogens, suggesting that this new class of previously unexplored AMPs may contribute to microbial surveillance within the human stomach. The peptides showed no toxicity toward human cells and exhibited anti-infective activity in vivo, reducing by up to 4 orders of magnitude the bacterial load in a mouse skin infection model. These peptides thus represent a promising new class of antibiotics. We envision that computationally guided data mining approaches such as the one described here will lead to the discovery of antibiotics from previously unexplored sources.
Novel approaches are needed to combat antibiotic resistance. Here, we describe a computational-experimental framework for the discovery of novel cryptic antimicrobial peptides (AMPs). The computational platform, based on previously validated antimicrobial scoring functions, indicated the activation peptide of pepsin A, the main human stomach protease, and its N- and C-terminal halves as antimicrobial peptides. The three peptides from pepsinogen A3 isoform were prepared in a recombinant form using a fusion carrier specifically developed to express toxic peptides in Escherichia coli. Recombinant pepsinogen A3-derived peptides proved to be wide-spectrum antimicrobial agents with MIC values in the range 1.56-50 μM (1.56-12.5 μM for the whole activation peptide). Moreover, the activation peptide was bactericidal at pH 3.5 for relevant foodborne pathogens, suggesting that this new class of previously unexplored AMPs may contribute to microbial surveillance within the human stomach. The peptides showed no toxicity toward human cells and exhibited anti-infective activity in vivo, reducing by up to 4 orders of magnitude the bacterial load in a mouse skin infection model. These peptides thus represent a promising new class of antibiotics. We envision that computationally guided data mining approaches such as the one described here will lead to the discovery of antibiotics from previously unexplored sources.
Identification of Novel Cryptic Multifunctional Antimicrobial Peptides from the Human Stomach Enabled by a Computational-Experimental Platform
Di Maro, Antimo;
2018
Abstract
Novel approaches are needed to combat antibiotic resistance. Here, we describe a computational-experimental framework for the discovery of novel cryptic antimicrobial peptides (AMPs). The computational platform, based on previously validated antimicrobial scoring functions, indicated the activation peptide of pepsin A, the main human stomach protease, and its N- and C-terminal halves as antimicrobial peptides. The three peptides from pepsinogen A3 isoform were prepared in a recombinant form using a fusion carrier specifically developed to express toxic peptides in Escherichia coli. Recombinant pepsinogen A3-derived peptides proved to be wide-spectrum antimicrobial agents with MIC values in the range 1.56-50 μM (1.56-12.5 μM for the whole activation peptide). Moreover, the activation peptide was bactericidal at pH 3.5 for relevant foodborne pathogens, suggesting that this new class of previously unexplored AMPs may contribute to microbial surveillance within the human stomach. The peptides showed no toxicity toward human cells and exhibited anti-infective activity in vivo, reducing by up to 4 orders of magnitude the bacterial load in a mouse skin infection model. These peptides thus represent a promising new class of antibiotics. We envision that computationally guided data mining approaches such as the one described here will lead to the discovery of antibiotics from previously unexplored sources.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
