Chemical Space for Peptide-based Antimicrobials.

Multidrug-resistant (MDR) bacteria represent a global public health threat, and antimicrobial peptides (AMPs), derived from naturally occurring linear or cyclic peptides, can provide the solution. However, most AMPs are sensitive to proteases and have poor pharmacokinetics. The EU-funded ERC Advanced Grant SPACE4AMPS aims to identify new AMPs by applying the concepts of chemical space and ligand-based virtual screening, which are well known for small molecule drug discovery, to the world of peptides.

Stereorandomized Oncocins with Preserved Ribosome Binding and Antibacterial Activity.

We recently showed that solid-phase peptide synthesis using racemic amino acids yields stereorandomized peptides comprising all possible diastereomers as homogeneous, single-mass products that can be purified by HPLC and that stereorandomization modulates activity, toxicity, and stability of membrane-disruptive cyclic and linear antimicrobial peptides (AMPs) and dendrimers. Here, we tested if stereorandomization might be compatible with target binding peptides with the example of the proline-rich AMP oncocin, which inhibits the bacterial ribosome. Stereorandomization of up to nine -terminal residues preserved ribosome binding and antibacterial effects including activities against drug-resistant bacteria and protected against serum degradation.

Antimicrobial Peptide-Peptoid Hybrids with and without Membrane Disruption.

Among synthetic analogues of antimicrobial peptides (AMPs) under investigation to address antimicrobial resistance, peptoids (-alkylated oligoglycines) have been reported to act both by membrane disruption and on intracellular targets. Here we gradually introduced peptoid units into the membrane-disruptive undecapeptide KKLLKLLKLLL to test a possible transition toward intracellular targeting. We found that selected hybrids containing up to five peptoid units retained the parent AMP's α-helical folding, membrane disruption, and antimicrobial effects against Gram-negative bacteria including multidrug-resistant (MDR) strains of and while showing reduced hemolysis and cell toxicities.

To Fold or Not to Fold: Diastereomeric Optimization of an α-Helical Antimicrobial Peptide.

Membrane disruptive α-helical antimicrobial peptides (AMPs) offer an opportunity to address multidrug resistance; however, most AMPs are toxic and unstable in serum. These limitations can be partly overcome by introducing D-residues, which often confers protease resistance and reduces toxicity without affecting antibacterial activity, presumably due to lowered α-helicity. Here, we investigated 31 diastereomers of the α-helical AMP KKLLKLLKLLL.

Dipropylamine for 9-Fluorenylmethyloxycarbonyl (Fmoc) Deprotection with Reduced Aspartimide Formation in Solid-Phase Peptide Synthesis.

Herein, we report dipropylamine (DPA) as a fluorenylmethyloxycarbonyl (Fmoc) deprotection reagent to strongly reduce aspartimide formation compared to piperidine (PPR) in high-temperature (60 °C) solid-phase peptide synthesis (SPPS). In contrast to PPR, DPA is readily available, inexpensive, low toxicity, and nonstench. DPA also provides good yields in SPPS of non-aspartimide-prone peptides and peptide dendrimers.

A mixed chirality α-helix in a stapled bicyclic and a linear antimicrobial peptide revealed by X-ray crystallography.

The peptide α-helix is right-handed when containing amino acids with l-chirality, and left-handed with d-chirality, however mixed chirality peptides generally do not form α-helices unless a helix inducer such as the non-natural residue amino-isobutyric acid is used. Herein we report the first X-ray crystal structures of mixed chirality α-helices in short peptides comprising only natural residues as the example of a stapled bicyclic and a linear membrane disruptive amphiphilic antimicrobial peptide (AMP) containing seven l- and four d-residues, as complexes of fucosylated analogs with the bacterial lectin LecB. The mixed chirality α-helices are superimposable onto the homochiral α-helices and form under similar conditions as shown by CD spectra and MD simulations but non-hemolytic and resistant to proteolysis.

Machine learning designs non-hemolytic antimicrobial peptides.

Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells.