Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides

Targeting collagen damage for sustained in situ antimicrobial activities

Antimicrobial peptides (AMPs) are promising anti-infective drugs, but their use is restricted by their short-term retention at the infection site, non-targeted uptake, and adverse effects on normal tissues. Since infection often follows an injury (e.g., in a wound bed), directly immobilizing AMPs to the damaged collagenous matrix of the injured tissues may help overcome these limitations by transforming the extracellular matrix microenvironment of the infection site into a natural reservoir of AMPs for sustained in situ release. Here, we developed and demonstrated an AMP-delivery strategy by conjugating a dimeric construct of AMP Feleucin-K3 (Flc) and a collagen hybridizing peptide (CHP), which enabled selective and prolonged anchoring of the Flc-CHP conjugate to the damaged and denatured collagen in the infected wounds in vitro and in vivo. We found that the dimeric Flc and CHP conjugate design preserved the potent and broad-spectrum antimicrobial activities of Flc while significantly enhancing and extending its antimicrobial efficacy in vivo and facilitating tissue repair in a rat wound healing model. Because collagen damage is ubiquitous in almost all injuries and infections, our strategy of targeting collagen damage may open up new avenues for antimicrobial treatments in a range of infected tissues.

Hydrophobic-hydrophilic Alternation: An effective Pattern to de novo Designed Antimicrobial Peptides

The antimicrobial peptide (AMP) is a class of molecules that are active against a variety of microorganisms, from bacterial and cancer cells to fungi. Most AMPs are natural products, as part of an organism's own defense system against harmful microbes. However, the growing prevalence of drug resistance has forced researchers to design more promising engineered antimicrobial agents. Inspired by the amphiphilic detergents, the hydrophobic-hydrophilic alternation pattern was considered to be a simple but effective way to de novo design AMPs. In this model, hydrophobic amino acids (leucine, isoleucine etc.) and hydrophilic amino acids (arginine, lysine etc.) were arranged in an alternating way in the peptide sequence. The majority of this type of peptides have a clear hydrophilic-hydrophobic interface, which allows the molecules to have good solubility in both water and organic solvents. When they come into contact with hydrophobic membranes, many peptides undergo a conformational transformation, facilitating themself to insert into the cellular envelope. Moreover, positive-charged peptide amphiphiles tended to have an affinity with negatively-charged membrane interfaces and further led to envelope damage and cell death. Herein, several typical design patterns have been reviewed. Though varying in amino acid sequence, they all basically follow the rule of alternating arrangement of hydrophilic and hydrophobic residues. Based on that, researchers synthesized some lead compounds with favorable antimicrobial activities and preliminarily investigated their possible mode of action. Besides membrane disruption, these AMPs are proven to kill microbes in multiple mechanisms. These results deepened our understanding of AMPs' design and provided a theoretical basis for constructing peptide candidates with better biocompatibility and therapeutic potential.

CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework

Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).