Among the manifold methods for the functionalization of peptides known today, radical chemistry has so far only rarely been exploited to introduce structural modifications on this challenging type of substrate. In this short review, we give an overview showing that, especially, radical arylation reactions can represent attractive and powerful tools to access functionalized peptides under mild and biocompatible conditions where no protecting group strategy is required. Besides offering new opportunities to directly synthesize functionalized peptides for diverse biological applications, including drug development, it is also our intention to give deeper insight into aryl radical generation and aryl radical reactivity, which can, in a wider sense, be highly useful for future developments in the field of the selective radical functionalization of biomacromolecules.
N ‐phenyl‐1‐naphthylamine (NPN) has been widely used to probe the integrity of the outer membrane of gram‐negative bacteria. NPN fluoresces in the hydrophobic environment of lipid bilayers, whereas its fluorescence is quenched in the aqueous extracellular milieu. Because NPN does not intrinsically accumulate in intact gram‐negative bacteria, it has been broadly applied as a reporter of outer membrane permeabilization. Despite its widespread utility, systematic derivatization of this probe has remained largely unexplored. Here, we investigated the structure–fluorescence relationships of NPN through the synthesis of a diverse library featuring varied linkages to the fluorogenic naphthylamine core. Subsequent library expansion was informed by density functional theory and time‐dependent density functional theory studies, which suggested possible origins of fluorescence quenching in aqueous environments. The final resulting library comprised 95 compounds, of which 49 exhibited a fluorogenic profile similar to that of NPN. A selected subset of analogs was then evaluated in a fluorescence‐based accumulation assay using varied efflux and permeation strains of Escherichia coli , revealing that installed structural motifs influenced bacterial permeation. Collectively, these probes provide a foundation for further studies examining how small structural changes affect accumulation in gram‐negative bacteria.
Heparan sulfate (HS) is a key host‐factor mediator of SARS‐CoV‐2 attachment and an attractive target for antiviral intervention, yet the synthesis of structurally defined HS oligosaccharides remains challenging. Here, we show that a defined HS hexasaccharide, heparin‐dp6, efficiently inhibits RBD‐ACE2 binding and exhibits superior antiviral activity compared with longer HS mimetics. Electronic‐structure analyses suggest that the strong recognition capacity of HS arises from sulfate and carboxylate groups, which create highly negative electrostatic surfaces and serve as dominant hydrogen‐bond acceptors, governing charge distribution and electronic properties along the glycan backbone to modulate binding specificity and affinity. Stepwise virtual screening of a comprehensive HS database further identified several high‐affinity oligosaccharides, with top candidates displaying favorable RBD‐binding properties. These findings indicate that HS activity is determined not only by chain length and sulfation pattern, but also by the spatial arrangement of charged functional groups, providing mechanistic insight into HS‐RBD recognition and guiding the rational design of HS‐based decoy inhibitors against SARS‐CoV‐2 entry.
The xanthine oxidase (XO) family of mononuclear molybdoenzymes consists of well‐studied enzymes like XO, xanthine dehydrogenase (XDH), and aldehyde oxidase (AO) in eukaryotes. The characteristic of this family is a terminal sulfido ligand bound to the active site Mo or W ion. Historically, these enzymes were among the first to be investigated in a purified form. Since its discovery in 1902 in cow's milk, the XO enzyme is among the best‐studied molybdoenzymes. Much pioneering work has been carried out including comprehensive spectroscopic and structural analyses to study the enzymes’ molybdenum center. This led to the discovery that the enzyme produces superoxide and thereby inactivates itself by removal of the essential sulfido ligand at the active site. This sulfido ligand has been later discovered also in enzymes of the dimethyl sulfoxide (DMSO) reductase family. In this review, we describe historical aspects on the discovery and role of the sulfido ligand in the two enzyme families, contextualize new discoveries, and provide perspectives for future investigations into still elusive research questions urgently requiring determination, which may or may not span yet another 125 years.
Machine learning has become an increasingly powerful tool in enzyme discovery, enabling the prediction of enzyme activity, substrate scope, and functional specificity across diverse protein families. However, many reported models rely on narrowly sampled datasets and task‐specific benchmarks, limiting their robustness and generalizability to distantly related enzymes or novel chemical substrates. In this concept article, we provide a practical, best‐practice–oriented perspective on developing enzyme family‐ or class‐specific machine learning (ML) predictors with an emphasis on data quality, representation, and model interpretability. We first discuss strategies for assembling biochemical datasets from literature, highlighting common sources of bias, redundancy, and uncertainty impacting downstream model performance. We then outline key considerations for generating new experimental data that are better suited for ML applications, including dataset design and coverage of enzyme and chemical space. Next, we summarize core computational steps involved in constructing enzyme and substrate representations, training and testing predictive models, and analyzing learned embedding spaces. Finally, we discuss outstanding technical challenges, such as mitigating taxonomic sampling bias, computing representations of enzymes with cofactors and post‐translational modifications, and integrating predictions into design–build–test–learn cycles for real‐world applications.
