Platanosides from Platanus × acerifolia: New molecules, SAR, and target validation of a strong lead for drug-resistant bacterial infections and the associated sepsis.

Three undescribed (1-3) and nine known (4-12) platanosides were isolated and characterized from a bioactive extract of the May leaves of Platanus × acerifolia that initially showed inhibition against Staphylococcus aureus. Targeted compound mining was guided by an LC-MS/MS-based molecular ion networking (MoIN) strategy combined with conventional isolation procedures from a unique geographic location. The novel structures were mainly determined by 2D NMR and computational (NMR/ECD calculations) methods. Compound 1 is a rare acylated kaempferol rhamnoside possessing a truxinate unit. 6 (Z,E-platanoside) and 7 (E,E-platanoside) were confirmed to have remarkable inhibitory effects against both methicillin-resistant S. aureus (MIC: ≤ 16 µg/mL) and glycopeptide-resistant Enterococcus faecium (MIC: ≤ 1 µg/mL). These platanosides were subjected to docking analyses against FabI (enoyl-ACP reductase) and PBP1/2 (penicillin binding protein), both of which are pivotal enzymes governing bacterial growth but not found in the human host. The results showed that 6 and 7 displayed superior binding affinities towards FabI and PBP2. Moreover, surface plasmon resonance studies on the interaction of 1/7 and FabI revealed that 7 has a higher affinity (KD = 1.72 µM), which further supports the above in vitro data and is thus expected to be a novel anti-antibacterial drug lead.

Informatics and Computational Approaches for the Discovery and Optimization of Natural Product-Inspired Inhibitors of the SARS-CoV-2 2'-O-Methyltransferase.

The urgent need for new classes of orally available, safe, and effective antivirals─covering a breadth of emerging viruses─is evidenced by the loss of life and economic challenges created by the HIV-1 and SARS-CoV-2 pandemics. As frontline interventions, small-molecule antivirals can be deployed prophylactically or postinfection to control the initial spread of outbreaks by reducing transmissibility and symptom severity. Natural products have an impressive track record of success as prototypic antivirals and continue to provide new drugs through synthesis, medicinal chemistry, and optimization decades after discovery. Here, we demonstrate an approach using computational analysis typically used for rational drug design to identify and develop natural product-inspired antivirals. This was done with the goal of identifying natural product prototypes to aid the effort of progressing toward safe, effective, and affordable broad-spectrum inhibitors of Betacoronavirus replication by targeting the highly conserved RNA 2'-O-methyltransferase (2'-O-MTase). Machaeriols RS-1 (7) and RS-2 (8) were identified using a previously outlined informatics approach to first screen for natural product prototypes, followed by in silico-guided synthesis. Both molecules are based on a rare natural product group. The machaeriols (3-6), isolated from the genus Machaerium, endemic to Amazonia, inhibited the SARS-CoV-2 2'-O-MTase more potently than the positive control, Sinefungin (2), and in silico modeling suggests distinct molecular interactions. This report highlights the potential of computationally driven screening to leverage natural product libraries and improve the efficiency of isolation or synthetic analog development.

Establishment of a Halophilic Bloom in a Sterile and Isolated Hypersaline Mesocosm.

Extreme environments, including hypersaline pools, often serve as biogeographical islands. Putative colonizers would need to survive transport across potentially vast distances of inhospitable terrain. Hyperhalophiles, in particular, are often highly sensitive to osmotic pressure. Here, we assessed whether hyperhalophiles are capable of rapidly colonizing an isolated and sterile hypersaline pool and the order of succession of the ensuing colonizers. A sterile and isolated 1 m3 hypersaline mesocosm pool was constructed on a rooftop in Charleston, SC. Within months, numerous halophilic lineages successfully navigated the 20 m elevation and the greater than 1 km distance from the ocean shore, and a vibrant halophilic community was established. All told, in a nine-month period, greater than a dozen halophilic genera colonized the pool. The first to arrive were members of the Haloarchaeal genus Haloarcula. Like a weed, the Haloarcula rapidly colonized and dominated the mesocosm community but were later supplanted by other hyperhalophilic genera. As a possible source of long-distance inoculum, both aerosol and water column samples were obtained from the Great Salt Lake and its immediate vicinity. Members of the same genus, Haloarcula, were preferentially enriched in the aerosol sample relative to the water column samples. Therefore, it appears that a diverse array of hyperhalophiles are capable of surviving aeolian long-distance transport and that some lineages, in particular, have possibly adapted to that strategy.

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs.

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug-drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.