A series of cationic conjugated oligoelectrolytes (COEs) was designed to understand how variations in molecular dimensions impact the relative activity against bacteria and mammalian cells.
New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large data sets and finding structural similarities to existing antibiotics. Challenges remain in modeling unconventional antibiotic classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure−activity relationship studies. Our approach enables us to predict the minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5305 descriptors. This predictive model achieves an R 2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the three-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.
We developed a series of transmembrane conjugated oligoelectrolytes (COEs) with tunable optical emissions from the UV to the near IR to address the false-positive problem when detecting nanometer-sized extracellular vesicles (EVs) by flow cytometry. The amphiphilic molecular framework of COEs is defined by a linear conjugated structure and cationic charged groups at each terminal site. Consequently, COEs have excellent water solubility and the absence of nanoaggregates at concentrations up to 50 μM, and unbound COE dyes can be readily removed through ultrafiltration. These properties enable unambiguous and simple detection of COE-labeled small EVs using flow cytometry with negligible background signals. We also demonstrated the time-lapsed tracking of small EV uptake into mammalian cells and the endogenous small EV labeling using COEs. Briefly, COEs provide a class of membrane-targeting dyes that behave as biomimetics of the lipid bilayer and a general and practical labeling strategy for nanosized EVs.
Cationic conjugated oligoelectrolytes (COEs) are a class of compounds that can be tailored to achieve relevant in vitro antimicrobial properties with relatively low cytotoxicity against mammalian cells. Three distyrylbenzene‐based COEs were designed containing amide functional groups on the side chains. Their properties were compared to two representative COEs with only quaternary ammonium groups. The optimal compound, COE2−3C−C3‐Apropyl, has an antimicrobial efficacy against Escherichia coli with an MIC=2 μg mL−1, even in the presence of human serum albumin low cytotoxicity (IC50=740 μg mL−1) and minimal hemolytic activity. Moreover, we find that amide groups increase interactions between COEs and a bacterial lipid mimic based on calcein leakage assay and allow COEs to readily permeabilize the cytoplasmic membrane of E. coli. These findings suggest that hydrogen bond forming moieties can be further applied in the molecular design of antimicrobial COEs to further improve their selectivity towards bacteria.
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