Intramembrane Nanoaggregates of Antimicrobial Peptides Play a Vital Role in Bacterial Killing.

Recent developments in antimicrobial peptides (AMPs) have focused on the rational design of short sequences with less than 20 amino acids due to their relatively low synthesis costs and ease of correlation of the structure-function relationship. However, gaps remain in the understanding of how short cationic AMPs interact with the bacterial outer and inner membranes to affect their antimicrobial efficacy and dynamic killing. The membrane-lytic actions of two designed AMPs, G(IIKK)3 I-NH2 (G3 ) and G(IIKK)4 I-NH2 (G4 ), and previously-studied controls GLLDLLKLLLKAAG-NH2 (LDKA, biomimetic) and GIGAVLKVLTTGLPALISWIKRKR-NH2 (Melittin, natural) are examined. The mechanistic processes of membrane damage and the disruption strength of the four AMPs are characterized by molecular dynamics simulations and experimental measurements including neutron reflection and scattering. The results from the combined studies are characterized with distinctly different intramembrane nanoaggregates formed upon AMP-specific binding, reflecting clear influences of AMP sequence, charge and the chemistry of the inner and outer membranes. G3 and G4 display different nanoaggregation with the outer and inner membranes, and the smaller sizes and further extent of insertion of the intramembrane nanoaggregates into bacterial membranes correlate well with their greater antimicrobial efficacy and faster dynamic killing. This work demonstrates the crucial roles of intramembrane nanoaggregates in optimizing antimicrobial efficacy and dynamic killing.

Design of bovine lactoferricin-derived peptide and its expression and activity in Pichia pastoris.

Bovine lactoferrin peptide has been shown to be a broad-spectrum antimicrobial peptide. Based on the relationship between the structure and function of antimicrobial peptides, the antimicrobial peptide databases and protein analysis software were used to optimize the design of bovine lactoferricin peptide (LfcinB). The designed bovine lactoferricin-derived peptide (LfcinBD) gene fragment was inserted into a pPIC9K-His plasmid to construct a recombinant expression vector. After linearization of the Recombinant plasmid, Pichia pastoris GS115 cells were transfected with linearized recombinant plasmid by using electroporation and LfcinBD gene expression was induced with methanol. After the fermentation, supernatant was separated by low-temperature high-speed centrifugation. Ultrafiltration and freeze drying of the fermentation supernatant were performed, purified. Experimental results showed that the LfcinBD had stronger bacteriostatic activity against Staphylococcus aureus than the natural bovine lactoferrin peptide (LfcinB) produced under the same fermentation conditions. The effective expression of the optimized bovine lactoferricin-derived peptide was detected using SDS-PAGE electrophoresis. This study lays the foundation for further exploration to improve the biological activities of antimicrobial peptides.

Sequential rearrangement and stereochemical reorganization to design an antimicrobial peptide with enhanced stability.

Antimicrobial peptides (AMPs) are natural molecules that function within the innate immune system to counteract pathogenic invasion and minimize the detrimental consequences of infection. However, utilizing these molecules for medical applications has been challenging. In this study, we selected a model AMP with poor stability, Tilapia Piscidin 4 (TP4), and modified its sequence and chirality (TP4-γ) to improve its potential for clinical application. The strategy of chirality inversion was inspired by the cereulide peptide, which has a DDLL enantiomer pattern and exhibits exceptional stability. Sequential substitution of key residues and selective chirality inversion yielded a less toxic peptide with enhanced stability and notable antimicrobial activity. In addition to its superior stability profile and antimicrobial activity, TP4-γ treatment reduced the level of LPS-induced nitric oxide (NO) release in a macrophage cell line. This reduction in NO release may reflect anti-inflammatory properties, as NO is widely known to promote inflammatory processes. Hence, our heterochiral peptide construct shows a more suitable pharmacokinetic profile than its parental compound, and further studies are warranted to develop the molecule for potential clinical application.

Geometric deep learning as a potential tool for antimicrobial peptide prediction.

Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.