ACS Central Science最新文献

With over 10,000 new reaction protocols arising every year, only a handful of these procedures transition from academia to application. A major reason for this gap stems from the lack of comprehensive knowledge about a reaction’s scope, i.e., to which substrates the protocol can or cannot be applied. Even though chemists invest substantial effort to assess the scope of new protocols, the resulting scope tables involve significant biases, reducing their expressiveness. Herein we report a standardized substrate selection strategy designed to mitigate these biases and evaluate the applicability, as well as the limits, of any chemical reaction. Unsupervised learning is utilized to map the chemical space of industrially relevant molecules. Subsequently, potential substrate candidates are projected onto this universal map, enabling the selection of a structurally diverse set of substrates with optimal relevance and coverage. By testing our methodology on different chemical reactions, we were able to demonstrate its effectiveness in finding general reactivity trends by using a few highly representative examples. The developed methodology empowers chemists to showcase the unbiased applicability of novel methodologies, facilitating their practical applications. We hope that this work will trigger interdisciplinary discussions about biases in synthetic chemistry, leading to improved data quality.

We introduce an objective substrate scope selection method for assessing the generality of chemical reactions.

Herein, we introduce a model of electronic spin isomers, the electronic counterpart of nuclear spin isomers, by using a stable organic diradical. The diradical, composed of two benzotriazinyl radicals connected by a rigid triptycene skeleton, exhibits a small singlet–triplet energy gap of −3.0 kJ/mol, indicating ca. 1:1 coexistence of the two spin states at room temperature. The diradical shows characteristic near-IR absorption bands, which are absent in the corresponding monoradical subunit. Variable temperature measurements revealed that the absorbance of the NIR band depends on the abundance of the singlet state, allowing us to identify the NIR band as the singlet-specific absorption band. It enables photoexcitation of one of the two spin states coexisting in thermal equilibrium. Transient absorption spectroscopy disclosed that the two spin states independently follow qualitatively different excited-state dynamics. These results demonstrate a novel approach to the design and study of electronic spin isomers based on organic diradicals.

We demonstrate that an organic diradical can be regarded as its electronic counterpart of nuclear spin isomers (e.g., ortho/para-hydrogens) with spin bistability and spin-dependent properties.

Here, we present HelixDiff, a score-based diffusion model for generating all-atom helical structures. We developed a hot spot-specific generation algorithm for the conditional design of α-helices targeting critical hotspot residues in bioactive peptides. HelixDiff generates α-helices with near-native geometries for most test scenarios with root-mean-square deviations (RMSDs) less than 1 Å. Significantly, HelixDiff outperformed our prior GAN-based model with regard to sequence recovery and Rosetta scores for unconditional and conditional generations. As a proof of principle, we employed HelixDiff to design an acetylated GLP-1 D-peptide agonist that activated the glucagon-like peptide-1 receptor (GLP-1R) cAMP accumulation without stimulating the glucagon-like peptide-2 receptor (GLP-2R). We predicted that this D-peptide agonist has a similar orientation to GLP-1 and is substantially more stable in MD simulations than our earlier D-GLP-1 retro-inverse design. This D-peptide analogue is highly resistant to protease degradation and induces similar levels of AKT phosphorylation in HEK293 cells expressing GLP-1R compared to the native GLP-1. We then discovered that matching crucial hotspots for the GLP-1 function is more important than the sequence orientation of the generated D-peptides when constructing D-GLP-1 agonists.

We developed a score-based diffusion model for generating all-atom α-helix structures and enabling conditional peptide design by mimicking critical hotspot residues in bioactive peptides.

Most viruses start their invasion by binding to glycoproteins’ moieties on the cell surface (heparan sulfate proteoglycans [HSPG] or sialic acid [SA]). Antivirals mimicking these moieties multivalently are known as broad-spectrum multivalent entry inhibitors (MEI). Due to their reversible mechanism, efficacy is lost when concentrations fall below an inhibitory threshold. To overcome this limitation, we modify MEIs with hydrophobic arms rendering the inhibitory mechanism irreversible, i.e., preventing the efficacy loss upon dilution. However, all our HSPG-mimicking MEIs only showed reversible inhibition against HSPG-binding SARS-CoV-2. Here, we present a systematic investigation of a series of small molecules, all containing a core and multiple hydrophobic arms terminated with HSPG-mimicking moieties. We identify the ones that have irreversible inhibition against all viruses including SARS-CoV-2 and discuss their design principles. We show efficacy in vivo against SARS-CoV-2 in a Syrian hamster model through both intranasal instillation and aerosol inhalation in a therapeutic setting (12 h postinfection). We also show the utility of the presented design rules in producing SA-mimicking MEIs with irreversible inhibition against SA-binding influenza viruses.

Multivalent sulfated benzene derivative inhibits viruses through a virucidal mechanism. It shows therapeutic efficacy in vivo against SARS-CoV-2 when administered intranasally 12 h postinfection.

The response of an oxide crystal to the atmosphere can be personified as breathing─a dynamic equilibrium between O₂ gas and O^2– anions in the solid. We characterize the analogous defect reaction in an iodide double-perovskite semiconductor, Cs₂SnI₆. Here, I₂ gas is released from the crystal at room temperature, forming iodine vacancies. The iodine vacancy defect is a shallow electron donor and is therefore ionized at room temperature; thus, the loss of I₂ is accompanied by spontaneous n-type self-doping. Conversely, at high I₂ pressures, I₂ gas is resorbed by the perovskite, consuming excess electrons as I₂ is converted to 2I^–. Halide mobility and irreversible halide loss or exchange reactions have been studied extensively in halide perovskites. However, the reversible exchange equilibrium between iodide and iodine [2I^–_(s) ↔ I_2(g) + 2e^–] described here has often been overlooked in prior studies, though it is likely general to halide perovskites and operative near room temperature, even in the dark. An analysis of the 2I^–_(s)/I_2(g) equilibrium thermodynamics and related transport kinetics in single crystals of Cs₂SnI₆ therefore provides insight toward achieving stable composition and electronic properties in the large family of iodide perovskite semiconductors.

We study the reversible exchange between I₂ gas and iodide ions in a single crystal of the double perovskite Cs₂SnI₆. Measurements of bulk ion diffusion, electron transport properties, and thermodynamics of the 2I⁻_(s)/I_2(g) equilibrium indicate that I₂ off-gassing is spontaneous near room temperature and dopes the perovskite with excess electrons. Mitigating this reaction is critical to achieving stable electronic properties from perovskite semiconductors.

Many research laboratories are inaccessible to disabled scientists, but they don’t have to be.

The evolutionary biologist’s new book explores how humans have used plant poisons for spices, medicines, and more.

In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency with automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated organic reactions. We find 68% of these reactions present significant multiconfigurational character in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to reactivity, enabled by automated active space selection algorithms and the computation of electronic correlation with MC-PDFT on-top functionals. This approach can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed.

We introduce an approach that automates accurate calculations of hundreds of organic reactions, based on multiconfigurational pair-density functional theory.

We present the first hardware implementation of electrostatic interaction energies by using a trapped-ion quantum computer. As test system for our computation, we focus on the reduction of NO to N₂O catalyzed by a nitric oxide reductase (NOR). The quantum computer is used to generate an approximate ground state within the NOR active space. To efficiently measure the necessary one-particle density matrices, we incorporate fermionic basis rotations into the quantum circuit without extending the circuit length, laying the groundwork for further efficient measurement routines using factorizations. Measurements in the computational basis are then used as inputs for computing the electrostatic interaction energies on a classical computer. Our experimental results strongly agree with classical noise-less simulations of the same circuits, finding electrostatic interaction energies within chemical accuracy despite hardware noise. This work shows that algorithms tailored to specific observables of interest, such as interaction energies, may require significantly fewer quantum resources than individual ground state energies would require in the straightforward supermolecular approach.

The first demonstration of the computation of electrostatic interaction energies on a trapped-ion quantum computer, tested on the reduction of NO to N₂O, shows strong agreement with classical simulations.

The inefficient translocation of proteins across biological membranes limits their application as potential therapeutics and research tools. In many cases, the translocation of a protein involves two discrete steps: uptake into the endocytic pathway and endosomal escape. Certain charged or amphiphilic molecules can achieve high protein uptake, but few are capable of efficient endosomal escape. One exception to this rule is ZF5.3, a mini-protein that exploits elements of the natural endosomal maturation machinery to translocate across endosomal membranes. Although some ZF5.3–protein conjugates are delivered efficiently to the cytosol or nucleus, overall delivery efficiency varies widely for different cargoes with no obvious design rules. Here we show that delivery efficiency depends on the ability of the cargo to unfold. Using fluorescence correlation spectroscopy, a single-molecule technique that precisely measures intracytosolic protein concentration, we show that regardless of size and pI, low-T_m cargoes of ZF5.3 (including intrinsically disordered domains) bias endosomal escape toward a high-efficiency pathway that requires the homotypic fusion and protein sorting (HOPS) complex. Small protein domains are delivered with moderate efficiency through the same HOPS portal, even if the T_m is high. These findings imply a novel pathway out of endosomes that is exploited by ZF5.3 and provide clear guidance for the selection or design of optimally deliverable therapeutic cargo.

The cell-permeant mini-protein ZF5.3 crosses endosomal membranes most efficiently to deliver protein cargoes to the cell cytosol when the cargo is capable of unfolding under physiological conditions.