Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains

Abstract

Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, represent an untapped source of promising antibiotic alternatives. However, bacteriocins display diverse mechanisms of action, a narrow spectrum of activity, and inherent challenges in natural product isolation making in vitro verification of putative bacteriocins difficult. A subset of bacteriocins exert their antimicrobial effects through favorable biophysical interactions with the bacterial membrane mediated by the charge, hydrophobicity, and conformation of the peptide. We have developed a pipeline for bacteriocin‐derived compound design and testing that combines sequence‐free prediction of bacteriocins using machine learning and a simple biophysical trait filter to generate 20 amino acid peptides that can be synthesized and evaluated for activity. We generated 28,895 total 20‐mer candidate peptides and scored them for charge, α‐helicity, and hydrophobic moment. Of those, we selected 16 sequences for synthesis and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against Escherichia coli and Pseudomonas aeruginosa. Our combined method incorporates machine learning and biophysical‐based minimal region determination to create an original approach to swiftly discover bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.

Exploring Amyloidogenicity of Peptides From Ribosomal S1 Protein to Develop Novel AMPs

“…Despite the wide variety of approaches to assessing antibacterial activity, it is difficult to create a universal template that could distinguish between antimicrobial and nonantimicrobial peptides, which is a significant limitation in the development of new AMPs. Obviously, there is a need for laboratory testing of the effectiveness of predicted AMPs, including for further refinement and improvement of the results of the predictive programs (Fields et al, 2020). We have proposed a new mechanism of AMP action, a mechanism of directed co-aggregation, which is based on the interaction of a peptide capable of forming fibrils with a target protein.…”

Bacteriocins: An Overview of Antimicrobial, Toxicity, and Biosafety Assessment by in vivo Models

“…Moreover, new strategies are being introduced in the design of bacteriocins. Fields et al (2020a) were the first to design the very first fully de novo bacteriocin by using a machine-learning approach. Fields et al (2020b) also constructed a library of the linear peptides from the membrane-interacting region of circular bacteriocin with pore-formation dynamics by selective aminoacidic substitution.…”

Physicochemical Features and Peculiarities of Interaction of AMP with the Membrane

“…Many attempts have been made to design compounds against microbes relying on the features of AMP, which are responsible for the mode of action [ 174 , 175 ]. Due to difficulties with determining features responsible for the particular mode of action, as well as the absence of quantitative description of these mechanisms, designing has been mainly performed experimentally and has relied on the known active peptide sequences and on reasonable substitutions of the amino acids at the certain positions of the parent peptide [ 176 , 177 , 178 ].…”

“…Here the question arises: what is an optimal set of physicochemical descriptors that should be used? The authors of [ 174 , 180 ] have used many different descriptors to classify AMP such as charge, hydrophobicity, hydrophobic moment, a propensity to disordered structure, etc. We have to emphasize that proper attention has not been paid to very important physical interactions, such as the cation–π interactions.…”