Communications Chemistry最新文献

Serine proteases in ribosomally synthesized and post-translationally modified peptides (RiPPs) catalyze the cleavage on the precursor peptides in the biosynthesis of RiPP natural products. Here, we identified an uncharacterized serine protease WprP₂ from Streptomyces venezuelae NPDC049867, encoded next to the radical S-adenosyl-L-methionine (SAM) enzyme WprB₂ involved in the biosynthesis of cyclophane natural products. In vitro characterization of S9 protease WprP₂ revealed that the precursor peptide WprA₂ is uniformly cleaved. The cleavage activity of WprP₂ has not been seen in any serine proteases and expands the S9 protease in RiPP biosynthesis.

Potency (IC₅₀) prediction of small molecules is pivotal for anticancer drug development. This study benchmarked five deep learning (DL) models for IC₅₀ prediction-DeepCDR, DrugCell, PaccMann, Precily, and tCNN-against a simple mean-based Baseline using standardized GDSC datasets and recently published anticancer compounds. To ensure practicality, conventional error metrics were supplemented with percentage error, log error, three-sigma limit, and a newly proposed Experimental Variability-Aware Prediction Accuracy statistic. The models performed well on randomly split data and unseen cell lines but showed sharply reduced accuracy for unseen compounds. Though all DL models exhibited similar performance trends, DeepCDR, DrugCell, and tCNN held a slight edge in most testing scenarios. Interestingly, several DL algorithms could not significantly outperform the Baseline model in many tests. Assessing prediction error against physicochemical and biological properties of compounds and cell lines revealed weak correlation, highlighting an underexplored aspect of model performance. A user-friendly web server (https://nlplab1.isical.ac.in/ic50.php) was also developed for IC₅₀ prediction of new compounds against cancer cell lines.

Protein conformational changes drive signal transduction to regulate cellular activities, yet monitoring of these changes in living cells remains challenging. Here, we introduce BIOSCE (BIOprobe based on Steric Confinement-induced Emission), a technique that enables tracking of individual protein conformations in living cells across millisecond-to-minute timescales. BIOSCE reports protein conformational changes via steric confinement-induced luminescence switching from non-luminescent to luminescent states. We demonstrate that BIOSCE rapidly senses calmodulin conformational changes triggered by intracellular calcium fluctuations. The BIOSCE platform achieved millisecond-resolution monitoring of single-protein conformations within cellular signaling pathways, as evidenced by its sensitive detection of rapamycin-dependent FKBP (FK506-binding protein)-FRB (FKBP-rapamycin binding) interactions regardless of the labeled partner. Furthermore, we applied BIOSCE to track the spatial distribution of SNAP25 (25 kDa synaptosomal nerve-associated protein) during botulinum neurotoxin A (BoNT/A) intoxication, revealing differential catalytic processing of its cleavage fragments. This generalizable approach provides a robust platform for investigating single-molecule conformational changes with high spatiotemporal resolution and enables direct evaluation of transient cellular events.

Deuterated alcohols are valuable synthetic targets due to their roles in pharmaceuticals, materials, and mechanistic studies. Conventional homogeneous strategies for their synthesis, while effective, often require expensive ligands and offer limited catalyst recovery. Heterogeneous catalysis, by contrast, provides a robust and recyclable alternative with enhanced scalability. Recent advances in supported metal nanoparticles and single-atom catalysts (SACs) have enabled high-efficiency and site-selective deuteration of alcohols. This Perspective presents heterogeneous catalytic systems as evolving into scalable and efficient platforms for deuterated alcohol synthesis, opening new directions for sustainable isotope incorporation.

In pursuit of the reductive conversion of NO in organic synthesis, this study presents the direct diazotization of indoles using 2-methoxyethyl nitrite (MOE-ONO). Diazo compounds are invaluable intermediates in organic synthesis, serving as carbene precursors for constructing diverse carbon frameworks. However, traditional C-H diazotization methods often require azide compounds or excessive amounts of acid/base, posing significant environmental and safety challenges. Herein, we introduce a sustainable and efficient diazotization protocol that overcomes these problems. The use of 2-methoxyethyl nitrite (MOE-ONO), a stable and highly reactive NO donor developed by our group, in combination with TEMPO and a catalytic amount of Sc(OTf)₃, played a significant role in the direct diazotization of 2-substituted indoles. MOE-ONO showed higher reactivity and selectivity than conventional NO donors, even including tert-butyl nitrite or NaNO₂/AcOH. Since MOE-ONO is synthesized from NO gas, oxygen, and 2-methoxyethanol, this diazotization serves as an effective utilization of NO. Furthermore, the diazotization proceeds efficiently even in water, which is important for designing clean chemical processes. The synthesized 3-diazoindoles exhibit broad reactivity, such as Grignard reaction at the C2-position and rhodium-catalyzed cyclopropanation at the C3-position. Such reactions can be practical solutions for synthesizing highly functionalized indoles and indolines, which are important for the design and discovery of pharmacologically active compounds.

Remote boronate rearrangement of boronic acids to C═N bonds is a valuable in synthetic chemistry. Conventional approaches are constrained by the need to pre-install specialized directing groups onto the starting materials. Here, we report a lactam-driven dynamic directing strategy, achieving 1,5- and 1,4-boronate rearrangements. The strategy circumvents the need for substrate pre-activation procedures, successfully overcoming a challenge in the functionalization of inactive C = N bonds to N-alkyl anilines and 3-aryl quinoxalinones. Comprehensive mechanistic investigations unveil three transformative insights: (i) Lactam leverages boron activation to C = N bonds through tetracoordinate boron species; (ii) the 1,5-boronate rearrangement to N-alkyl anilines is favored via an eight-membered boronate complex, as supported by density functional theory (DFT) studies; (iii) a catalyst-free 1,4-boronate rearrangement pathway operates through HFIP-stabilized tetracoordinate boron intermediates. This lactam-enabled boronate rearrangements offers a methodology with transformative potential.

Structural characterization of powder materials, including those synthesized by mechanochemical methods, remains challenging due to the lack of single crystals suitable for X-ray diffraction. Microcrystal-Electron Diffraction (MicroED) enables structure determination from sub-micrometer crystallites but faces limitations, particularly in locating hydrogen atoms and distinguishing light atoms (C, N, O). We present a general workflow that integrates MicroED with high-resolution mass spectrometry, database mining, solution and solid-state NMR, and DFT-D/GIPAW calculations to resolve atomic structures of complex powders, even with unknown composition. The approach is demonstrated on a pyridoxine-N-acetyl-L-cysteine salt, a mechanochemically synthesized adduct for which large single crystals could not be obtained, and on N-formyl-methionyl-leucyl-phenylalanine (fMLF), a bacterial chemoattractant peptide. This strategy enables comprehensive structure resolution, including identification of molecular components, crystal packing, atom assignments and hydrogen positions. Its modularity and scalability make it suitable for a wide range of powder materials, e.g., pigments, pharmaceutical compounds, etc., especially when conventional crystallography fails.

While it has been long supposed that asteroids played a role in the delivery of important prebiotic compounds to early Earth, the exact nature of the interactions between asteroidal material and metabolism remains largely unquantified. Pristine material from asteroid sample-return missions provides an unprecedented opportunity to evaluate the potential for the asteroids' chemistry to support the origin and persistence of life. Here we use metabolic network expansion to computationally test the viability of contrasted biochemical networks, including a group of acetogens and methanogens representing primitive metabolisms, on the known chemistry of three asteroids (Itokawa, Ryugu, Bennu) and two meteorites (Murchison, Murray). The chemistry of Murchison and Bennu appears to support the potential viability of the acetogenic and methanogenic metabolisms. In contrast, Murray, Ryugu and Itokawa samples lack critical substrates, particularly adenine and D-ribose needed for ATP production, suggesting that carbonaceous bodies vary in their compositional capacity to support the acetogenic and methanogenic metabolisms. This highlights the astrobiological relevance of asteroids rich in carbon, nitrogen, and phosphate such as Bennu, and hints at the habitability, past or present, of their parent bodies.

Chiral organic materials show great promise in optoelectronics, sensing and catalysis. Among those, macrocycles are of great interest due to their preorganisation and potential amplification of chiroptical properties. Understanding the effects of different sources of chirality on the resulting chiroptical properties of these molecules is key to unlocking tailored chiral materials. To this end, we have synthesised a family of bis-perylene diimide-based macrocycles containing multiple sources of chirality, specifically point chirality in the linker, helical chirality in the perylene diimide and supramolecular chirality in the macrocyclic dimer. We found a dominant effect from the helical chirality of the perylene diimide on the chiroptical properties, including the induction of chirality in an achiral guest molecule, which opens up new possibilities for hybrid chiroptical materials.

Pure shift NMR spectroscopy has found extensive applications in exploring the structure, function, and interactions of molecules in an ultrahigh-resolution manner. However, time-consuming data acquisition resulting from additional time dimension for pure shift evolution impedes its further applications. In this study, a general and robust AI-assisted NMR methodology combining non-uniform chunk sampling with physics-informed deep learning (DL) reconstruction is proposed for fast implementation of pure shift NMR spectroscopy. The proposed DL protocol enables the suppression on sparsely sampling artifacts, faithful recovery of weak signals, as well as high-fidelity reconstruction on peak intensities, thus implementing accelerated pure shift NMR while maintaining spectral quality. The well-trained model shows broad applicability across one-dimensional, two-dimensional, even multi-dimensional pure shift NMR. In addition, ablation experiments are further performed to provide mechanistic insights into deep learning reconstruction on sparse sampled pure shift NMR spectra. Moreover, its application potentials have been further demonstrated through in-situ monitoring of 1-butanol electrooxidation on Pt/C and PtRu/C catalysts. As a result, this study establishes a robust AI-assisted NMR framework for disentangling molecular structure and dynamics information for complex sample systems with high temporal and spectral resolution, and could find wide applications across multiple chemistry disciplines.