Plants are extensively used in traditional medicine, and several plant antimicrobial peptides have been described as potential alternatives to conventional antibiotics. However, after more than four decades of research no plant antimicrobial peptide is currently used for treating bacterial infections, due to their length, post-translational modifications or high dose requirement for a therapeutic effect . Here we report the design of antimicrobial peptides derived from a guava glycine-rich peptide using a genetic algorithm. This approach yields guavanin peptides, arginine-rich α-helical peptides that possess an unusual hydrophobic counterpart mainly composed of tyrosine residues. Guavanin 2 is characterized as a prototype peptide in terms of structure and activity. Nuclear magnetic resonance analysis indicates that the peptide adopts an α-helical structure in hydrophobic environments. Guavanin 2 is bactericidal at low concentrations, causing membrane disruption and triggering hyperpolarization. This computational approach for the exploration of natural products could be used to design effective peptide antibiotics.

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.

Antimicrobial peptides (AMPs) constitute promising alternatives to classical antibiotics for the treatment of drug-resistant infections, which are a rapidly emerging global health challenge. However, our understanding of the structure-function relationships of AMPs is limited, and we are just beginning to rationally engineer peptides in order to develop them as therapeutics. Here, we leverage a physicochemical-guided peptide design strategy to identify specific functional hotspots in the wasp-derived AMP polybia-CP and turn this toxic peptide into a viable antimicrobial. Helical fraction, hydrophobicity, and hydrophobic moment are identified as key structural and physicochemical determinants of antimicrobial activity, utilized in combination with rational engineering to generate synthetic AMPs with therapeutic activity in a mouse model. We demonstrate that, by tuning these physicochemical parameters, it is possible to design nontoxic synthetic peptides with enhanced sub-micromolar antimicrobial potency in vitro and anti-infective activity in vivo. We present a physicochemical-guided rational design strategy to generate peptide antibiotics.

Antibiotics started to be used almost 90 years ago to eradicate life-threatening infections. The urgency of the problem required rapid, broad-spectrum elimination of infectious agents. Since their initial discovery, these antimicrobials have saved millions of lives. However, they are not exempt from side effects, which include the indiscriminate disruption of the beneficial microbiota. Recent technological advances have enabled the development of antimicrobials that can selectively target a gene, a cellular process, or a microbe of choice. These strategies bring us a step closer to developing personalized therapies that exclusively remove disease-causing infectious agents. Here, we advocate the preservation of our beneficial microbes and provide an overview of promising alternatives to broad-spectrum antimicrobials. Specifically, we emphasize nucleic acid and peptide-based systems as a foundation for next-generation alternatives to antibiotics that do not challenge our microbiota and may help to mitigate the spread of resistance.

During the development of antimicrobial peptides (AMP) as potential therapeutics, antimicrobial susceptibility testing (AST) stands as an essential part of the process in identification and optimisation of candidate AMP. Standard methods for AST, developed almost 60 years ago for testing conventional antibiotics, are not necessarily fit for purpose when it comes to determining the susceptibility of microorganisms to AMP. Without careful consideration of the parameters comprising AST there is a risk of failing to identify novel antimicrobials at a time when antimicrobial resistance (AMR) is leading the planet toward a post-antibiotic era. More physiologically/clinically relevant AST will allow better determination of the preclinical activity of drug candidates and allow the identification of lead compounds. An important consideration is the efficacy of AMP in biological matrices replicating sites of infection, e.g., blood/plasma/serum, lung bronchiolar lavage fluid/sputum, urine, biofilms, etc., as this will likely be more predictive of clinical efficacy. Additionally, specific AST for different target microorganisms may help to better predict efficacy of AMP in specific infections. In this manuscript, we describe what we believe are the key considerations for AST of AMP and hope that this information can better guide the preclinical development of AMP toward becoming a new generation of urgently needed antimicrobials.