Antimicrobial Peptides: Effect on Bacterial Cells

A review on antimicrobial peptides databases and the computational tools

Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.

Insights into the mode of action of the two-peptide lantibiotic lichenicidin

Lantibiotics are promising candidates to address the worldwide problem of antibiotic resistance. They belong to a class of natural compounds exhibiting strong activity against clinically relevant Gram-positive bacterial strains, including methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE). Lichenicidin is a class II two-peptide lantibiotic. The presence of the two mature peptides, Bliα and Bliβ, is necessary for full activity against target bacteria. This work aims at clarifying the synergistic activity of both peptides in their interaction with the target membranes. The effect of lichenicidin was tested against S. aureus cells and large unilamellar vesicles. Lichenicidin increases the net surface charge of S. aureus, as shown by zeta-potential measurements, without reaching electroneutralization. In addition, lichenicidin causes cell surface perturbations that culminate in the leakage of its internal contents, as observed by atomic force microscopy. Bliα seems to have low affinity for S. aureus, however, it contributes to increase the affinity of Bliβ, because together they present higher affinity than separately. In contrast, Bliα seems to provide an anchoring site for lichenicidin in lipid II-containing membranes. Interestingly, Bliβ alone can induce high levels of membrane leakage, but this effect appears to be faster in the presence of Bliα. Based on this information, we propose a mechanism of action of lichenicidin.

CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model

BACKGROUND

Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduce the anticancer peptide secondary structures as additional features and propose an effective computational model, CL-ACP, that uses a combined network and attention mechanism to predict anticancer peptides.

RESULTS

The CL-ACP model uses secondary structures and original sequences of anticancer peptides to construct the feature space. The long short-term memory and convolutional neural network are used to extract the contextual dependence and local correlations of the feature space. Furthermore, a multi-head self-attention mechanism is used to strengthen the anticancer peptide sequences. Finally, three categories of feature information are classified by cascading. CL-ACP was validated using two types of datasets, anticancer peptide datasets and antimicrobial peptide datasets, on which it achieved good results compared to previous methods. CL-ACP achieved the highest AUC values of 0.935 and 0.972 on the anticancer peptide and antimicrobial peptide datasets, respectively.

CONCLUSIONS

CL-ACP can effectively recognize antimicrobial peptides, especially anticancer peptides, and the parallel combined neural network structure of CL-ACP does not require complex feature design and high time cost. It is suitable for application as a useful tool in antimicrobial peptide design.

Polyalanine peptide variations may have different mechanisms of action against multidrug-resistant bacterial pathogens

OBJECTIVES

The number of bacterial pathogens resistant to the currently available antibiotics has dramatically increased, with antimicrobial peptides (AMPs) being among the most promising potential new drugs. In this study, the applicability and mechanisms of action of Pa-MAP 2 and Pa-MAP 1.9, two AMPs synthetically designed based on a natural AMP template, were evaluated.

METHODS

Pa-MAP 2 and Pa-MAP 1.9 were tested against a clinically isolated multidrug-resistant (MDR) Escherichia coli strain. Biophysical approaches were used to evaluate the preference of both peptides for specific lipid membranes, and bacterial surface changes imaged by atomic force microscopy (AFM). The efficacy of both peptides was assessed both in vitro and in vivo.

RESULTS

Experimental results showed that both peptides have antimicrobial activity against the E. coli MDR strain. Zeta potential and surface plasmon resonance assays showed that they interact extensively with negatively charged membranes, changing from a random coil structure, when free in solution, to an α-helical structure after membrane interaction. The antibacterial efficacy was evaluated in vitro, by several techniques, and in vivo, using a wound infection model, showing a concentration-dependent antibacterial effect. Different membrane properties were evaluated to understand the mechanism underlying peptide action, showing that both promote destabilization of the bacterial surface, as imaged by AFM, and change properties such as membrane surface and dipole potential.

CONCLUSIONS

Despite their similarity, data indicate that the mechanisms of action of the peptides are different, with Pa-MAP 1.9 being more effective than Pa-MAP 2. These results highlight their potential use as antimicrobial agents against MDR bacteria.

Cell biomechanics and mechanobiology in bacteria: Challenges and opportunities

Physical forces play a profound role in the survival and function of all known forms of life. Advances in cell biomechanics and mechanobiology have provided key insights into the physiology of eukaryotic organisms, but much less is known about the roles of physical forces in bacterial physiology. This review is an introduction to bacterial mechanics intended for persons familiar with cells and biomechanics in mammalian cells. Bacteria play a major role in human health, either as pathogens or as beneficial commensal organisms within the microbiome. Although bacteria have long been known to be sensitive to their mechanical environment, understanding the effects of physical forces on bacterial physiology has been limited by their small size (∼1 μm). However, advancements in micro- and nano-scale technologies over the past few years have increasingly made it possible to rigorously examine the mechanical stress and strain within individual bacteria. Here, we review the methods currently used to examine bacteria from a mechanical perspective, including the subcellular structures in bacteria and how they differ from those in mammalian cells, as well as micro- and nanomechanical approaches to studying bacteria, and studies showing the effects of physical forces on bacterial physiology. Recent findings indicate a large range in mechanical properties of bacteria and show that physical forces can have a profound effect on bacterial survival, growth, biofilm formation, and resistance to toxins and antibiotics. Advances in the field of bacterial biomechanics have the potential to lead to novel antibacterial strategies, biotechnology approaches, and applications in synthetic biology.

Using fluorescence microscopy to shed light on the mechanisms of antimicrobial peptides

Antimicrobial peptides (AMPs) are promising in the fight against increasing bacterial resistance, but the development of AMPs with enhanced activity requires a thorough understanding of their mechanisms of action. Fluorescence microscopy is one of the most flexible and effective tools to characterize AMPs, particularly in its ability to measure the membrane interactions and cellular localization of peptides. Recent advances have increased the scope of research questions that can be addressed via microscopy through improving spatial and temporal resolution. Unique combinations of fluorescent labels and dyes can simultaneously consider different aspects of peptide-membrane interaction mechanisms. This review emphasizes the central role that fluorescence microscopy will continue to play in the interrogation of AMP structure-function relationships and the engineering of more potent peptides.