Evaluation of Antibacterial Activity of Magainin and Mastoparan and Its Novel Hybrid Against MDR E. coli Isolates of Neonatal Calves

Industrial application of antimicrobial peptides based on their biological activity and structure-activity relationship

Last several years, a rapid increase in drug resistance to traditional antibiotics has driven the emergence and development of antimicrobial peptides (AMPs). AMPs have also gained considerable attention from scientists due to their high potency in combatting infectious pathogens. A subset of analogues and their derivatives with specific targets have been successfully designed based on natural peptide patterns. In this review, scientific knowledge on the mechanisms of action related to biological activity and structure-activity relationship (SAR) of AMPs are summarized, and the biological applications in several important fields are critically discussed. SAR shows that the positive charge, secondary structure, special amino acid residues, hydrophobicity, and helicity of AMPs are closely related to their biological activities. The combination of nanotechnology, bioinformatics, and genetic engineering can accelerate to achieve the application of AMPs as effective, safe, economical, and nonresistant antimicrobial agents in medicine, the food and feed industries, and agriculture in coming years. Given the intense interest in AMPs, further investigations are needed in the future to evaluate the specific structure and function that make their use favorable in several industries. This review may provide a comprehensive reference for future studies on chemical modifications, mechanistic exploration, and applications of AMPs.

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.

Functional characterization, antimicrobial effects, and potential antibacterial mechanisms of new mastoparan peptides from hornet venom (Vespa ducalis, Vespa mandarinia, and Vespa affinis)

Antibiotic-resistant bacteria are a major threat to global public health, and there is an urgent need to find effective, antimicrobial treatments that can be well tolerated by humans. Hornet venom is known to have antimicrobial properties, and contains peptides with similarity to known antimicrobial eptides (AMPs), mastoparans. We identified multiple new AMPs from the venom glands of Vespa ducalis (U-VVTX-Vm1a, U-VVTX-Vm1b, and U-VVTX-Vm1c), Vespa mandarinia (U-VVTX-Vm1d), and Vespa affinis (U-VVTX-Vm1e). All of these AMPs have highly similar sequences and are related to the toxic peptide, mastoparan. Our newly identified AMPs have α-helical structures, are amphiphilic, and have antimicrobial properties. Both U-VVTX-Vm1b and U-VVTX-Vm1e killed bacteria, Staphylococcus aureus ATCC25923 and Escherichia coli ATCC25922, at the concentrations of 16 μg/mL and 32 μg/mL, respectively. None of the five AMPs exhibited strong toxicity as measured via their hemolytic activity on red blood cells. U-VVTX-Vm1b was able to increase the permeability of E. coli ATCC25922 and degrade its genomic DNA. These results are promising, demonstrate the value of investigating hornet venom as an antimicrobial treatment, and add to the growing arsenal of such naturally derived treatments.