Connecting membrane fluidity and surface charge to pore-forming antimicrobial peptides resistance by an ANN-based predictive model

Abstract

Efficiency of antibacterial chemotherapy is gradually more challenged by the emergence of pathogenic strains exhibiting high levels of antibiotic resistance. Pore-forming antimicrobial peptides (PF-AMPs) such as alamethicin (Alm) are therefore in the focus of extensive research efforts. In the present study, an artificial neural network (ANN)-based quantitative structure-activity relationship (SAR) modeling of membrane phospholipids vs. PF-AMPs, in context to membrane fluidity and surface charge, was carried out. We observed that the potency of PF-AMPs depends on the fatty acyl chain and polar head group of phospholipids. Alm showed surface interactions with zwitterionic phospholipids however could penetrate deeper inside the hydrophobic core of anionic membranes. Here, the resistance developed in bacterial cells was coupled to membrane fluidity and surface charge, and simultaneously, these principles could be applied for combating resistance against PF-AMPs. The correlation coefficient between observed CR and predicted CR using ANN was found to be 0.757. Thus, ANN could be used as a reliable modeling method for predicting CR, given the structure of the biomimetic membrane in terms of membrane fluidity and surface charge. Fully explored mechanisms of resistance, a forward modeling step in the design cycle of AMPs, can be cross-linked to the inward modeling using ANN to complete the peptide design cycle. The SAR between membrane phospholipids and PF-AMPs could furnish valuable information regarding their design to provide us efficacious peptides against premier pathogens. So far, this is the only report available to predict and quantify interactions of PF-AMPs with membrane phospholipids.