Communications Chemistry最新文献

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.

The ability to conjugate multiple molecules to a protein is of great interest for pharmaceutical and vaccine development, especially if this can be achieved in a one-pot procedure. Multicomponent reactions are powerful procedures that allow the assembly of complex constructs incorporating at least three molecular fragments, but many of them use amino and carboxylic groups that are too abundant in proteins. Herein, we introduce the use of the Passerini 3-component reaction with isocyanoproteins for the assembly of multivalent protein (glyco)conjugates. Proteins were tagged with isocyanide handles and next derivatized to investigate the efficacy and limitations of the Passerini bioconjugation. The multicomponent conjugation enabled the simultaneous functionalization of proteins with two biologically relevant molecules such as carbohydrate antigens, lipids, and polymers. The efficient display of various antigens in a unimolecular multivalent construct is a notable result that paves the way towards new applications in preventive vaccines and therapeutics.

Highly-charged intrinsically disordered proteins (IDPs) underpin biomolecular condensate formation through liquid-liquid phase separation, yet the influence of charge sequences on the dynamics within the condensate phase remains poorly understood. Using extensive molecular dynamics simulations with explicit hydrodynamics and electrostatics, we study the dynamics in IDP condensates across different length and time scales, by systematically varying the charge sequences of the constituent IDPs. Contrary to the expectation that long-range interactions are heavily screened in dense semidilute polymer solutions, we find hydrodynamics and electrostatics significantly influence the dynamics in IDP condensates and their effects are strongly coupled to the charge sequence of the constituent IDPs. For condensates of low to intermediate-κ IDPs, where κ is a measure of the charge blockiness of the charge sequence, we find hydrodynamics dominates the dynamics up to the length scale of the chain and beyond. On the sub-chain level, segmental relaxation is highly coupled to intra-chain electrostatic correlations due to local charge patterns, where sections with more charge-balanced blocks have faster relaxation. Furthermore, the viscosity in IDP condensates is significantly length-scale-dependent, with condensates of high-κ IDPs exhibiting large difference between microscopic and macroscopic viscosity. Such length-scale-dependent viscosity may be the key to understanding the experimentally observed extremely fast molecule-level dynamics in biocondensates of highly-charged IDPs. Our findings highlight the intricate relationship between charge sequences, hydrodynamics, and electrostatics in shaping the dynamics in IDP condensates at different length and time scales.

While photocatalysis has emerged as a transformative tool in modern synthesis, AI-assisted reaction prediction faces significant challenges due to data limitations. We present PhotoCatDB - a curated, open-source database containing 26.7 K photocatalytic reactions with detailed mechanistic annotations, including 9.2 K multicomponent transformations. Leveraging this resource alongside 100 million molecular data points, we developed PhotoCat, a Transformer-based platform that achieves unprecedented accuracy in photocatalytic reaction prediction (82.6%), retrosynthesis (77.1%), and condition recommendation (88.5%). The platform's capabilities were experimentally validated through the discovery of four novel photocatalytic reactions with yields up to 75.3%. This integrated approach establishes a new paradigm for data-driven innovation in photocatalysis, bridging computational prediction with experimental validation to accelerate discovery in sustainable chemistry.

Cancer-prone alleles exhibit single hotspot mutations. However, the combination of a cancer hotspot and a weak or moderate mutation (the 'one-two punch' hypothesis) produces same-allele double variants with a significantly different and potentially graded clinical phenotypic spectrum. Oncogenic PI3Kα variants, which are also associated with benign tumors and neurodevelopmental disorders, offer statistical support for this model. Using atomistic molecular dynamics (MD) simulations, we revealed that PI3Kα variants with single and double mutations exhibit expanded conformational profiles. Double mutations significantly shift the conformational ensembles toward the active form-a more pronounced effect than a single mutation. These double mutants facilitate nSH2 release, iSH2 shift, and A-loop protrusion in solution, promoting PIP₂ substrate recruitment at the membrane. Our simulations revealed cryptic pockets within PI3Kα. These pockets are potential drug targets and may exhibit mutation-specific characteristics. A key challenge is that a single drug is often ineffective against PI3Kα variants due to their diverse conformational spectra. To address this, we propose a conformational selection strategy involving a combination of allosteric drugs for variants with graded conformational spectra, particularly those with strong double mutations; we identified such potentially targetable cryptic pockets in double mutants conformers.

Water's polarity and hydrogen-bond network give rise to its unique chemical and biochemical behaviour. Its vibrational motions, occurring on a few-femtosecond timescale, govern ultrafast energy transfer within the hydrogen-bond network. However, direct real-time observation of these motions has remained elusive due to the extreme temporal resolution required. Here, we investigate the ground-state vibrational dynamics of liquid water initiated by a sub-5 fs near-infrared (NIR) pump pulse via Impulsive Stimulated Raman Scattering (ISRS). Using few-fs ultraviolet (UV) probe pulses transmitted through a 5 µm-thick liquid jet, we monitor the coherent vibrational wave packet dominated by the OH stretch mode, exhibiting a 10 fs oscillation period and a 25 fs damping time. These results reveal the rapid dephasing of the OH stretch mode preceding its relaxation through coupling to the bending vibrations, highlighting the importance of intermolecular couplings of liquid water in the high frequency vibrational dynamics.

Alzheimer's disease (AD) is marked by the abnormal aggregation of amyloid-beta peptides within the central nervous system. The formation of amyloid fibrils from amyloid-beta peptides is a hallmark of AD Here, we demonstrate that the aggregation of amyloid-beta 42 spreads both spatially and temporally. By measuring the spatial propagation of amyloid-beta in macroscopic capillaries and performing Monte Carlo simulations, we show that this spreading occurs through a diffusion mechanism involving oligomers in solution. These species, catalytically produced through spontaneous secondary nucleation, significantly accelerate the propagation velocity of the reaction wavefront. Our findings suggest that, in addition to their potential role in toxicity, these oligomers in solution are key drivers of the spatial spreading of aggregation and can therefore be considered key targets for therapeutic intervention.

Cryogenic electron microscopy (cryo-EM) single-particle analysis (SPA) has become a powerful technique for macromolecular structure determination. However, its effectiveness is often constrained by limited particle numbers or missing views. To address these challenges, we present CoCoFold, a fine-tuned framework that integrates raw cryo-EM particle images into AlphaFold to directly guide atomic model prediction. CoCoFold adopts a memory-efficient tuning strategy by introducing a fused attention mechanism into AlphaFold's structure module. Moreover, a differentiable network links predicted structures with cryo-EM observations, enabling end-to-end refinement against experimental data. Benchmark experiments with the escalating quantity insufficiency and view-missing of cryo-EM observations, demonstrate that CoCoFold consistently outperforms state-of-the-art methods across multiple evaluation metrics.

DNAzymes conventionally require dissolved metal ions for catalytic functions. Herein, we report that metal surfaces directly activate a self-cleaving DNAzyme (PL) at solid-liquid interfaces. PL exhibits activities on copper, vanadium and tantalum surfaces, within a minimal reaction system comprising only the metal surface, PL and double-distilled water. This interfacial activation is highly material-specific, showing complete absence of activity on plastics, glass or wood etc. Mechanistic studies reveal that dissolved oxygen could react with metal surfaces to generate superoxide anions, which serve as triggers for DNA-cleavage. The reaction shows modulatable characteristics, with inhibition by ethylenediaminetetraacetic acid, catalase, nitroblue tetrazolium and cytochrome c, versus enhancement by vitamin C, glutathione and catechol. Furthermore, metal surface-mediated activation was also observed in F-8, Ag10c and I-R3 DNAzymes, indicating that this phenomenon is not an isolated occurrence. This work establishes macroscopic metals as DNAzyme's cofactors, extending DNAzyme catalysis beyond conventional homogeneous systems to heterogeneous interfacial environments.

The TEAD transcription factors (TEAD1-4) are critical effectors of the Hippo pathway, forming active nuclear complexes with transcriptional co-activators YAP/TAZ to regulate cell growth/apoptosis pathways and control fundamental processes such as organ size. Frequent dysregulation of the Hippo pathway in cancer and the presence of druggable binding sites on TEADs make them attractive targets for development of small molecule inhibitors and degraders. Here, we identify and mechanistically characterize three unique series of bifunctional degraders that target TEAD1 via a lipid pocket and recruit different members of the Inhibitor of Apoptosis proteins (IAPs) family to effect degradation of TEAD1. We provide a detailed toolkit for structural, biophysical and cellular profiling, including the development of a cellular target engagement assay for the lipid pocket of TEAD1 and an IAP/TEAD1 ternary complex formation assay. Our study therefore provides essential resources for detailed characterization of IAP-recruiting degraders and important tools and learnings for bifunctional degraders targeted to the lipid pocket of TEADs.