ACS Central Science最新文献

The post-transcriptional reduction of uridine to dihydrouridine (D) by dihydrouridine synthase (DUS) enzymes is among the most ubiquitous transformations in RNA biology. D is found at multiple sites in tRNAs, and studies in yeast have proposed that each of the four eukaryotic DUS enzymes modifies a different site; however, the molecular basis for this exquisite selectivity is unknown, and human DUS enzymes have remained largely uncharacterized. Here we investigate the substrate specificity of human dihydrouridine synthase 2 (hDUS2) using mechanism-based cross-linking with 5-bromouridine (5-BrUrd)-modified oligonucleotide probes and in vitro dihydrouridylation assays. We find that hDUS2 exclusively modifies U20 across diverse tRNA substrates and identify a minimal GU sequence within the tRNA D loop that underlies selective substrate modification. Further, we use our mechanism-based platform to screen small molecule inhibitors of hDUS2, a potential anticancer target. Our work elucidates the principles of substrate modification by a conserved DUS and provides a general platform for studying RNA modifying enzymes with sequence-defined activity-based probes.

We report a systematic investigation of the sequence and structural requirements for human dihydrouridine synthase 2 (hDUS2)-mediated dihydrouridylation using 5-bromouridine-modified tRNA activity probes and oligonucleotide LC-MS/MS-based analysis.

Zhu et al. describe the discovery, biosynthesis, and physiological function of coralinone, a 5-methylated pyrazinone isolated from myxobacteria.

We report a blueprint for the rational design of G protein coupled receptor (GPCR) ligands with a tailored functional response. The present study discloses the structure-based design of cannabinoid receptor type 2 (CB₂R) selective inverse agonists (S)-1 and (R)-1, which were derived from privileged agonist HU-308 by introduction of a phenyl group at the gem-dimethylheptyl side chain. Epimer (R)-1 exhibits high affinity for CB₂R with K_d = 39.1 nM and serves as a platform for the synthesis of a wide variety of probes. Notably, for the first time these fluorescent probes retain their inverse agonist functionality, high affinity, and selectivity for CB₂R independent of linker and fluorophore substitution. Ligands (S)-1, (R)-1, and their derivatives act as inverse agonists in CB₂R-mediated cAMP as well as G protein recruitment assays and do not trigger β-arrestin–receptor association. Furthermore, no receptor activation was detected in live cell ERK_1/2 phosphorylation and Ca²⁺-release assays. Confocal fluorescence imaging experiments with (R)-7 (Alexa488) and (R)-9 (Alexa647) probes employing BV-2 microglial cells visualized CB₂R expressed at endogenous levels. Finally, molecular dynamics simulations corroborate the initial docking data in which inverse agonists restrict movement of toggle switch Trp258^6.48 and thereby stabilize CB₂R in its inactive state.

We report a generalizable strategy for structure-based agonist-to-inverse-agonist functional transformation and probe development by ligand modification that modulates the GPCR toggle switch of CB₂R.

Zeolites, nanoporous aluminosilicates with well-defined porous structures, are versatile materials with applications in catalysis, gas separation, and ion exchange. Hydrothermal synthesis is widely used for zeolite production, offering control over composition, crystallinity, and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding of synthesis–structure relationships to optimize the synthesis process. Hitherto, public zeolite synthesis databases only contain a subset of parameters and are small in scale, comprising up to a few thousand synthesis routes. We present ZeoSyn, a dataset of 23,961 zeolite hydrothermal synthesis routes, encompassing 233 zeolite topologies and 921 organic structure-directing agents (OSDAs). Each synthesis route comprises comprehensive synthesis parameters: 1) gel composition, 2) reaction conditions, 3) OSDAs, and 4) zeolite products. Using ZeoSyn, we develop a machine learning classifier to predict the resultant zeolite given a synthesis route with >70% accuracy. We employ SHapley Additive exPlanations (SHAP) to uncover key synthesis parameters for >200 zeolite frameworks. We introduce an aggregation approach to extend SHAP to all building units. We demonstrate applications of this approach to phase-selective and intergrowth synthesis. This comprehensive analysis illuminates the synthesis parameters pivotal in driving zeolite crystallization, offering the potential to guide the synthesis of desired zeolites. The dataset is available at https://github.com/eltonpan/zeosyn_dataset.

Zeolites are nanoporous materials with applications in catalysis and gas separation. We open-source the largest dataset on zeolite synthesis and develop an interpretable machine learning framework to reveal key synthesis−structure relationships.

Antigen processing is critical for therapeutic vaccines to generate epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often limits the quantity of epitopes released. We address this challenge using machine learning to ascribe a proteasomal degradation score to epitope sequences. Epitopes with varying scores were translocated into cells using nontoxic anthrax proteins. Epitopes with a low score show pronounced immunogenicity due to antigen processing, but epitopes with a high score show limited immunogenicity. This work sheds light on the sequence–activity relationships between proteasomal degradation and epitope immunogenicity. We anticipate that future efforts to incorporate proteasomal degradation signals into vaccine designs will lead to enhanced cytotoxic T cell priming by these vaccines in clinical settings.

Design of cytotoxic T cell epitopes for enhancing antigen processing and presentation, enabled by machine learning of human degrons and cytosolic delivery with two anthrax proteins.

Direct inhibitor of tau aggregation has been extensively studied as potential therapeutic agents for Alzheimer’s disease. However, the natively unfolded structure of tau complicates the structure-based ligand design, and the relatively large surface areas that mediate tau–tau interactions in aggregation limit the potential for identifying high-affinity ligand binding sites. Herein, a group of isatin-pyrrolidinylpyridine derivative isomers (IPP1–IPP4) were designed and synthesized. They are like different forms of molecular “transformers”. These isatin isomers exhibit different inhibitory effects on tau self-aggregation or even possess a depolymerizing effect. Our results revealed for the first time that the direct inhibitor of tau protein aggregation is not only determined by the previously reported conjugated structure, substituent, hydrogen bond donor, etc. but also depends more importantly on the molecular shape. In combination with molecular docking and molecular dynamics simulations, a new inhibition mechanism was proposed: like a “molecular clip”, IPP1 could noncovalently bind and fix a tau polypeptide chain at a multipoint to prevent the transition from the “natively unfolded conformation” to the “aggregation competent conformation” before nucleation. At the cellular and animal levels, the effectiveness of the inhibitor of the IPP1 has been confirmed, providing an innovative design strategy as well as a lead compound for Alzheimer’s disease drug development.

We propose that molecular deformation is a key factor in the screening aggregation inhibitor for intrinsic disordered protein tau. We designed and synthesized four isomers with different shapes by a modular combination of isatin and pyrrolidinylpyridine and verified that they have different binding abilities to tau and inhibitory activities against tau aggregation. Our results will provide a new direction for developing a tau aggregation inhibitor.

Achieving substrate-selectivity is a central element of nature’s approach to synthesis. By relying on the ability of a catalyst to discriminate between components in a mixture, control can be exerted over which molecules will move forward in a synthesis. This approach can be powerful when realized but can be challenging to duplicate in the laboratory. In this work, substrate-selective catalysis is leveraged to discriminate between two intermediates that exist in equilibrium, subsequently directing the final cyclization to arrive at either the linear or angular tricyclic core common to subsets of azaphilone natural products. By using a flavin-dependent monooxygenase (FDMO) in sequence with an acyl transferase (AT), the conversion of several orcinaldehyde substrates directly to the corresponding linear tricyclic azaphilones in a single reaction vessel was achieved. Further, mechanistic studies support that a substrate equilibrium together with enzyme substrate selectivity play an import role in the selectivity of the final cyclization step. Using this strategy, five azaphilone natural products were synthesized for the first time as well as a number of unnatural derivatives thereof.

A substrate-selective biocatalytic strategy is used to discriminate between two intermediates in equilibrium, subsequently directing a cyclization step to arrive at either linear or angular tricyclic azaphilone natural products.

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly with physically grounded operations. We present an example of an integrated modeling approach in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those on which it is trained and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parametrization corresponding to a minimal atom-centered basis. These results emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency and providing a blueprint for developing ML-augmented electronic-structure methods.

Development of a hybrid model integrating a data-driven prediction of molecular Hamiltonians and physics-based postprocessing yields an accurate and balanced description of excited states.

The discovery of magic-sized clusters as intermediates in the synthesis of colloidal quantum dots has allowed for insight into formation pathways and provided atomically precise molecular platforms for studying the structure and surface chemistry of those materials. The synthesis of monodisperse InAs quantum dots has been developed through the use of indium carboxylate and As(SiMe₃)₃ as precursors and documented to proceed through the formation of magic-sized intermediates. Herein, we report the synthesis, isolation, and single-crystal X-ray diffraction structure of an InAs nanocluster that is ubiquitous across reports of InAs quantum dot synthesis. The structure, In₂₆As₁₈(O₂CR)₂₄(PR'₃)₃, differs substantially from previously reported semiconductor nanocluster structures even within the III–V family. However, it can be structurally linked to III–V and II–VI cluster structures through the anion sublattice. Further analysis using variable temperature absorbance spectroscopy and support from computation deepen our understanding of the reported structure and InAs nanomaterials as a whole.

Magic sized clusters are ubiquitous intermediates in quantum dot synthesis. We isolate and structurally characterize an InAs nanocluster with a composition of In₂₆As₁₈(O₂CR)₂₄(PR'₃)₃.

Solid-state chemist describes debunking claims that LK-99 was a room-temperature superconductor.