Chemical Reviews最新文献

The electrical double layer (EDL) plays a central role in electrochemical energy systems, impacting charge transfer mechanisms and reaction rates. The fundamental importance of the EDL in interfacial electrochemistry has motivated researchers to develop theoretical and experimental approaches to assess EDL properties. In this contribution, we review recent progress in evaluating EDL characteristics such as the double-layer capacitance, highlighting some discrepancies between theory and experiment and discussing strategies for their reconciliation. We further discuss the merits and challenges of various experimental techniques and theoretical approaches having important implications for aqueous electrocatalysis. A strong emphasis is placed on the substantial impact of the electrode composition and structure and the electrolyte chemistry on the double-layer properties. In addition, we review the effects of temperature and pressure and compare solid–liquid interfaces to solid–solid interfaces.

Methods rooted in chemical biology have contributed significantly to studies of integral membrane proteins. One recent key approach has been the application of genetic code expansion (GCE), which enables the site-specific incorporation of noncanonical amino acids (ncAAs) with defined chemical properties into proteins. Efficient GCE is challenging, especially for membrane proteins, which have specialized biogenesis and cell trafficking machinery and tend to be expressed at low levels in cell membranes. Many eukaryotic membrane proteins cannot be expressed functionally in E. coli and are most effectively studied in mammalian cell culture systems. Recent advances have facilitated broader applications of GCE for studies of membrane proteins. First, AARS/tRNA pairs have been engineered to function efficiently in mammalian cells. Second, bioorthogonal chemical reactions, including cell-friendly copper-free “click” chemistry, have enabled linkage of small-molecule probes such as fluorophores to membrane proteins in live cells. Finally, in concert with advances in GCE methodology, the variety of available ncAAs has increased dramatically, thus enabling the investigation of protein structure and dynamics by multidisciplinary biochemical and biophysical approaches. These developments are reviewed in the historical framework of the development of GCE technology with a focus on applications to studies of membrane proteins.

O-Linked β-N-acetylglucosamine (O-GlcNAc) is an essential, dynamic monosaccharide post-translational modification (PTM) found on serine and threonine residues of thousands of nucleocytoplasmic proteins. The installation and removal of O-GlcNAc is controlled by a single pair of enzymes, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), respectively. Since its discovery four decades ago, O-GlcNAc has been found on diverse classes of proteins, playing important functional roles in many cellular processes. Dysregulation of O-GlcNAc homeostasis has been implicated in the pathogenesis of disease, including neurodegeneration, X-linked intellectual disability (XLID), cancer, diabetes, and immunological disorders. These foundational studies of O-GlcNAc in disease biology have motivated efforts to target O-GlcNAc therapeutically, with multiple clinical candidates under evaluation. In this review, we describe the characterization and biochemistry of OGT and OGA, cellular O-GlcNAc regulation, development of OGT and OGA inhibitors, O-GlcNAc in pathophysiology, clinical progress of O-GlcNAc modulators, and emerging opportunities for targeting O-GlcNAc. This comprehensive resource should motivate further study into O-GlcNAc function and inspire strategies for therapeutic modulation of O-GlcNAc.

DNA-encoded library (DEL) technology is a powerful platform for the efficient identification of novel chemical matter in the early drug discovery process enabled by parallel screening of vast libraries of encoded small molecules through affinity selection and deep sequencing. While DEL selections provide rich data sets for computational drug discovery, the underlying technical factors influencing DEL data remain incompletely understood. This review systematically examines the key parameters affecting the chemical information in DEL data and their impact on hit triaging and machine learning integration. The need for rigorous data handling and interpretation is emphasized, with standardized methods being critical for the success of DEL-based approaches. Major challenges include the relationship between sequence counts and binding affinities, frequent hitters, and the influence of factors such as inhomogeneous library composition, DNA damage, and linkers on binding modes. Experimental artifacts, such as those caused by protein immobilization and screening matrix effects, further complicate data interpretation. Recent advancements in using machine learning to denoise DEL data and predict drug candidates are highlighted. This review offers practical guidance on adopting best practices for integrating robust methodologies, comprehensive data analysis, and computational tools to improve the accuracy and efficacy of DEL-driven hit discovery.

Magic angle spinning (MAS) nuclear magnetic resonance (NMR) has evolved significantly over the past three decades and established itself as a vital tool for the structural analysis of biological macromolecules and materials. This review delves into the development and application of dipolar recoupling techniques in MAS NMR, which are crucial for obtaining detailed structural and dynamic information. We discuss a variety of homonuclear and heteronuclear recoupling methods which are essential for measuring spatial restraints and explain in detail the spin dynamics that these sequences generate. We also explore recent developments in high spinning frequency MAS, proton detection, and dynamic nuclear polarization, underscoring their importance in advancing biomolecular NMR. Our aim is to provide a comprehensive account of contemporary dipolar recoupling methods, their principles, and their application to structural biology and materials, highlighting significant contributions to the field and emerging techniques that enhance resolution and sensitivity in MAS NMR spectroscopy.

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.