JACS Au最新文献

Proteolysis-targeting chimeras (PROTACs) are promising next-generation therapeutics for the degradation of disease-associated proteins. However, optimizing the physicochemical properties of PROTACs, particularly their poor cell membrane permeability, remains challenging. Traditionally, PROTAC linkers have been manually designed to improve cell membrane permeability. Although recent machine learning-based approaches have enabled the rational design of PROTAC linkers, no linker design methods that explicitly address cell membrane permeability have been reported. In this study, we developed PROTAC-TS, a linker generative model that combines a chemical language model and reinforcement learning to control cell membrane permeability. We first constructed a prediction model of cell membrane permeability, which achieved high prediction performance (R ² = 0.710). By integrating this prediction model into the generative model, we successfully designed linkers of PROTACs with high predicted cell membrane permeability while considering PROTAC likeness. Our results highlight the potential of PROTAC-TS in accelerating PROTAC development with favorable cell membrane permeability.

The melting point is one of the most fundamental properties of crystalline materials and has been commonly used to determine the chemical and phase purity of organic compounds. Here, we report a significant depression in the melting point of about 1.4 K in naturally grown crystals with a circular shape obtained for one polymorph of the tetradecamorphic compound 5-methyl-2-((2-nitrophenyl)-amino)-3-thiophenecarbonitrile (ROY). Crystals of ROY grown by microspace sublimation have peculiar habits, with irregular, elongated, occasionally bent, or curled habits, and with one or both of their ends closed into loops. In contrast to the straight crystals, the Mueller matrix microscopic analysis suggested the continuous reorientation of electrical dipole moments in the curled crystals. When heated, these curly crystals often start to melt at the kink and exhibit a lower melting onset point and a broader endothermic peak in the thermal (DSC) fingerprint compared with the regular crystals. The decrease in the melting point was found to be proportional to the deformation expressed as the curvature of the crystals; it is also inversely proportional to the crystal width for narrow crystals, but it is independent of the width for wider crystals. The bent section of the crystals is mechanically softer than the straight part, and both the stiffness and hardness are inversely proportional to the degree of curling, presumably due to defects. In the three-dimensional reciprocal space, the curled crystals show diffuse diffraction or streaks due to lattice distortion. Nanoinfrared spectroscopic signatures indicate that the lattice distortion is related to the conformational changes of the molecule. The results highlight the dependence of the extent of crystal deformation on melting properties, which may have broad implications for modulating properties of pharmaceutical crystals.

Th1-selective natural killer T (NKT) cell agonists are promising immunotherapeutic agents due to their ability to promote cellular immunity against tumors and intracellular pathogens. However, the development of potent Th1-biased NKT cell agonists has remained slow despite decades of structural modification of the prototypical Th0-type agonist α-galactosylceramide (αGalCer). In this work, we used a distinct conformational restriction strategy to design a series of αGalCer branched analogs based on the spatial architecture of the CD1d binding groove, rather than through residue-focused modifications in previous αGalCer derivatizations. The linear acyl chain of αGalCer was replaced with branched motifs to restrict flexibility and enhance binding stability. Two optimized candidates GCB-27a and GCB-27b induced strong Th1-biased responses in vivo, with over 10-fold higher IFN-γ and limited IL-4 levels compared to αGalCer, establishing them among the most potent Th1-biased NKT cell agonists. They also demonstrated superior antitumor efficacy in mice. Importantly, these agonists retained significant activity in human NKT cells, highlighting their translational potential as promising immunotherapeutic agents.

Photocatalytic C-C coupling reactions mediated by molecular complexes represent an advancing field of research, in which Au-based catalysts have demonstrated significant potential. Herein, we employ an integrated computational strategy: incorporating MS-CASPT2, DFT, and kinetic analyses of fluorescence, phosphorescence, intersystem crossing, internal conversion, and electron transfer processes to unravel the mechanistic intricacies between aryldiazonium salts and alkynyl-silanes catalyzed by a naphthalene-di-imide-functionalized N-heterocyclic carbene Au-(I) complex, [(NDI-NHC)-Au-Cl]. Upon photoexcitation, a four-state model (S₀, S₁, T₂, and T₁) of the photophysical process is identified. The reaction proceeds via a triplet-state-initiated tandem comprising single electron transfer (SET), N₂ extrusion, radical addition, and intersystem crossing, culminating in reductive elimination to complete the catalytic cycle. Furthermore, introducing electron-withdrawing groups such as the cyano group into the aryldiazonium scaffold enhances SET efficiency by a certain cancellation of the reorganization energy by driving energy, thereby reducing the free energy barrier. The nonradiative decay dominates the reaction coordinates. Leveraging these mechanistic insights, the SET modulation strategy is theoretically extended to Ag and Cu complexes, which exhibit comparably high performance in C-C coupling reactions along with improved cost-effectiveness. This work not only establishes fundamental structure-reactivity relationships in Au-photocatalyzed cross-couplings but also provides a general framework for optimizing photoinduced electron transfer processes.

Chiral D ₂-symmetric figure-eight macrocycles with extended π-conjugated systems represent versatile scaffolds for developing advanced chiroptical materials. Here, we present a dynamic covalent platform that transforms a preorganized alkaloidal bispyrrolidinoindoline (BPI) scaffold into a series of chiroptically tunable figure-eight macrocycles through thermodynamically controlled imine formation. Strategic installation of aryl-ethynyl substituents precisely preorganized the BPI scaffold, enabling highly efficient macrocyclization (up to a 81% yield) under reversible imine-forming conditions. This modular approach allowed systematic assembly of diverse intercrossing chromophores, affording 44-90-membered macrocycles exhibiting predictable variations in both optical and chiroptical responses. Postsynthetic conversion of dynamic imine linkages into rigid π-extended B-N chelates via boranil formation further fixed the macrocyclic conformation, producing a bright orange emitter (Φ_fl = 0.54) with enhanced circularly polarized luminescence (CPL) activity (g _lum = 1.97 × 10^-3). This work establishes a general design principle integrating dynamic covalent chemistry and conformational preorganization to generate functional figure-eight macrocycles, translating natural-product-inspired molecular architecture into tunable chiroptical materials.

Light activates palladium catalysts to enable radical C-H (hetero)-arylation of heterocycles using simple (hetero)-aryl halides. Without the need of an external photocatalyst, the reaction proceeds under mild conditions with broad functional-group tolerance and high site selectivity, expanding photoactivated palladium catalysis for sustainable C-C bond formation in complex bi-(hetero)-aryl scaffolds.

The pikromycin polyketide synthase (PKS) catalyzes formation of 12-membered macrolactone 10-deoxymethynolide, and 14-membered macrolactone narbonolide. Herein, we show the efficient diversification of novel 14-membered macrolactones and identification of 6-membered δ-lactones from a series of unnatural pentaketides using the PikAIII/PikAIV PKS in vitro system. New macrocycles were further elaborated by the addition of D-desosamine and late-stage C-H hydroxylation. Molecular dynamics (MD) simulations and density functional theory (DFT) calculations were conducted to probe the reactivity and selectivity of this terminal catalytic step on the assembled unnatural macrolides. This approach highlights the flexibility of the PikAIII/PikAIV bimodule system in processing non-native substrates and demonstrates the utility of sequential biocatalytic steps for the chemoenzymatic synthesis of complex antibiotic scaffolds.

Dynamic effects represent a crucial mechanism governing the stereoselectivity of nitrogen extrusion from 1,1-diazenes. Elucidating this dynamic behavior provides a mechanistic basis for enhancing nitrogen deletion selectivity. Using pyrrolidine ring contraction as a model reaction, we demonstrate how nitrogen extrusion dynamics control reaction selectivity. Quasiclassical dynamics trajectory simulations reveal that reaction selectivity is dynamically controlled: retention products predominate (60.1% of trajectories) while inversion products are significantly disfavored (3.7%). Mechanistic analysis reveals dynamic mismatching between ring-expansion motion and retention bonding in nitrogen extrusion. Incorporation of the sterically demanding 1-adamantyl substituent effectively promotes retention bonding, attenuates dynamic mismatching, and increases retention products to 79.0%. These results provide mechanistic insight into 1,1-diazene nitrogen extrusion and establish a foundation for rational design of more selective nitrogen deletion reactions.

Oxidative protein folding, which is critical to proteins achieving their functional structures, is catalyzed in cells by protein disulfide isomerase (PDI)an enzyme that couples redox catalysis with the transient capture of folding intermediates to promote native disulfide formation while preventing aggregation. Although PDI improves oxidative folding in both chemically synthesized and recombinantly produced proteins, its use is restricted to homogeneous systems, limiting reusability and operational robustness. Artificial PDI mimics have advanced in vitro folding; however, no system has yet combined sufficient redox activity for native disulfide formation with a folding environment that suppresses aggregation, nor demonstrated true reusability. Here, we introduce a polymer-based "solid chaperone" that realizes PDI-like dual activity on an abiotic surface, achieving what natural PDI cannot: recyclable, HPLC-free oxidative folding without the stability and single-use limitations of enzymes. The covalent immobilization of cyclic diselenide onto polystyrene beads yields a redox-active and hydrophobic interface that transiently captures unfolded proteins, catalyzes both disulfide bond formation and isomerization, and suppresses aggregation even at high substrate concentrations. This solid-phase catalyst outperformed its homogeneous counterpart, producing native peptides and proteins in up to 99% yield and retaining full activity over multiple reuse cycles. These results demonstrate that complex biological folding functions, once confined to fragile enzymes, can be re-engineered into durable polymeric materials. This solid-phase strategy not only enables recyclable oxidative folding but also establishes a paradigm for translating enzymatic behavior into scalable synthetic systems with industrial potential.

Kinetic barriers under realistic solvation and potential conditions, known as critical in electrochemistry in recent years, have not been widely applied in the screening of electrocatalysts, mainly due to the high computational cost. Here, we demonstrate the establishment of quantitative relations between thermodynamics and kinetic barriers, which guides electrocatalyst screening from 51 candidates, taking single-atom@coinage-metal (M₁@CM) alloys catalyzing electrochemical nitrogen reduction reaction (eNRR) as an example. For CM = Cu, Ag, and Au, separated linear relations are found between the free energy changes (ΔG) based on the computational hydrogen-electrode model and the kinetic barriers (ΔG ^# ) calculated from enhanced sampling of constant-potential ab initio molecular dynamics (cp-AIMD). The variations among Cu, Ag, and Au can be primarily attributed to differences in interfacial water orientation and surface charge under the calculated potential, properties governed by their respective work functions. Furthermore, a unified mapping from ΔG to ΔG ^# is found with a prediction error of about 0.05 eV across the three hosts using machine learning regression methods. Based on these relations, the high-active zone is identified, while the full path is calculated for the representative case Re₁@Ag. Indeed, all barriers are no higher than 0.85 eV, significantly lower than other reported systems if barriers of all steps are examined. This work not only presents a screening strategy to quickly identify an eNRR catalyst with all-low kinetic barriers along the full path but also demonstrates how to establish and apply the quantitative relation between thermodynamics and cp-AIMD barriers, to significantly accelerate accurate screening of electrocatalysts.