Cryptic Host Defense Peptides: Multifaceted Activity and Prospects for Medicinal Chemistry

Diversity and Mechanisms of Action of Plant, Animal, and Human Antimicrobial Peptides

Antimicrobial peptides (AMPs) are usually made up of fewer than 100 amino acid residues. They are found in many living organisms and are an important factor in those organisms' innate immune systems. AMPs can be extracted from various living sources, including bacteria, plants, animals, and even humans. They are usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi, and other pathogens. They can act against both Gram-negative and Gram-positive pathogens and have various modes of action against them. Some attack the pathogens' membranes, while others target their intracellular organelles, as well as their nucleic acids, proteins, and metabolic pathways. A crucial area of AMP use is related to their ability to help with emerging antibiotic resistance: some AMPs are active against resistant strains and are susceptible to peptide engineering. This review considers AMPs from three key sources-plants, animals, and humans-as well as their modes of action and some AMP sequences.

Evolutionary and in silico guided development of novel peptide analogues for antibacterial activity against ESKAPE pathogens

According to WHO, to combat the resistant strains, new effective anti-microbial agents are needed on an urgent basis and global researchers should focus their efforts and discovery programs on developing them against antibiotic-resistant pathogens or priority pathogens like ESKAPE. In this context, Cationic antimicrobial peptides (AMPs) are being explored extensively as promising next-generation antimicrobials due to their broad range, fast kinetics and multifunctional role. Despite recent advances, it is still a daunting challenge to identify and design a potent AMP with no cytotoxicity, but with broad specific antimicrobial activity, stability and efficacy under in vivo conditions in a cost-effective and robust manner. In this work, as a proof of concept, we designed novel potent AMPs using artificial intelligence based in silico programs. Shortlisted peptide sequences were synthesized using the fmoc chemistry approach, assessed their antimicrobial activity, cell selectivity, mode of action and in vivo efficacy using a series of experiments. The synthesized peptide analogues demonstrated their antimicrobial activity (MIC in the range of 2.5-80 μM) against bacteria. The identified potential lead molecules showed antibacterial activity in physiological conditions with no signs of cytotoxicity. We further tested the antimicrobial activity of peptide analogues for treating wounds infected with Pseudomonas aeruginosa in the mice burn wound model. In drug-development programs, the identification of lead antimicrobial agents is always challenging and involves screening a large number of molecules which is time-consuming and expensive. This work demonstrates the utility of artificial intelligence based in silico analysis programs in discovering novel antimicrobial agents in an economical, robust way.

Simple Electric Device to Isolate Nucleic Acids from Whole Blood Optimized for Point of Care Testing of Brain Damage

BACKGROUND

Detection or monitoring of brain damage is a clinically crucial issue. Nucleic acids in the whole blood can be used as biomarkers for brain injury. Polymerase chain reaction (PCR) which is one of the most commonly used molecular diagnostic assays requires isolated nucleic acids to initiate amplification. Currently used nucleic acid isolation procedures are complicated and require laboratory equipments.

OBJECTIVE

In this study, we tried to develop a simple and convenient method to isolate nucleic acids from the whole blood sample using a tiny battery-powered electric device. The quality of the isolated nucleic acids should be suitable for PCR assay without extra preparation.

METHODS

A plastic device with separation chamber was designed and printed with a 3D printer. Two platinum electrodes were placed on both sides and a battery was used to supply the electricity. To choose the optimal nucleic acid isolation condition, diverse lysis buffers and separation buffers were evaluated, and the duration and voltage of the electricity were tested. Western blot analysis and PCR assay were used to determine the quality of the separated nucleic acids.

RESULTS

2ul of whole blood was applied to the cathode side of the separation chamber containing 78 ul of normal saline. When the electricity at 5 V was applied for 5 min, nucleic acids were separated from segment 1 to 3 of the separation chamber. The concentration of nucleic acids peaked around 7~8 mm from cathode side. PCR assay using the separation buffer as the template was performed successfully both in conventional and realtime PCR methods. The hemoglobin in the whole blood did not show the inhibitory effect in our separation system and it may be due to structural modification of hemoglobin during electric separation.

CONCLUSION

Our simple electric device can separate nucleic acids from the whole blood sample by applying electricity at 5 V for 5 min. The separation buffer solution taken from the device can be used for PCR assay successfully.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.