Membrane permeabilization can be crucially biased by a fusogenic lipid composition - leaky fusion caused by antimicrobial peptides in model membranes.

Induced membrane permeabilization or leakage is often taken as an indication for activity of membrane-active molecules, such as antimicrobial peptides (AMPs). The exact leakage mechanism is often unknown, but important, because certain mechanisms might actually contribute to microbial killing, while others are unselective, or potentially irrelevant in an in vivo situation. Using an antimicrobial example peptide (cR3W3), we illustrate one of the potentially misleading leakage mechanisms: leaky fusion, where leakage is coupled to membrane fusion. Like many others, we examine peptide-induced leakage in model vesicles consisting of binary mixtures of anionic and zwitterionic phospholipids. In fact, phosphatidylglycerol and phosphatidylethanolamine (PG/PE) are supposed to reflect bacterial membranes, but exhibit a high propensity for vesicle aggregation and fusion. We describe the implications of this vesicle fusion and aggregation for the reliability of model studies. The ambiguous role of the relatively fusogenic PE-lipids becomes clear as leakage decreases significantly when aggregation and fusion are prevented by sterical shielding. Furthermore, the mechanism of leakage changes if PE is exchanged for phosphatidylcholine (PC). We thus point out that the lipid composition of model membranes can be biased towards leaky fusion. This can lead to discrepancies between model studies and activity in true microbes, because leaky fusion is likely prevented by bacterial peptidoglycan layers. In conclusion, choosing the model membrane might implicate the type of effect (here leakage mechanism) that is observed. In the worst case, as with leaky fusion of PG/PE vesicles, this is not directly relevant for the intended antimicrobial application.

finDr: A web server for in silico D-peptide ligand identification.

In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure (https://findr.biologie.uni-freiburg.de/). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.