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.

-Linked β--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.

Phytoplankton have a high potential for CO capture and conversion. Besides being a vital food source at the base of oceanic and freshwater food webs, microalgae provide a critical platform for producing chemicals and consumer products. Enhanced nutrient levels, elevated CO, and rising temperatures increase the frequency of algal blooms, which often have negative effects such as fish mortalities, loss of flora and fauna, and the production of algal toxins. Harmful algal blooms (HABs) produce toxins that pose major challenges to water quality, ecosystem function, human health, tourism, and the food web. These toxins have complex chemical structures and possess a wide range of biological properties with potential applications as new therapeutics. This review presents a balanced and comprehensive assessment of the roles of algal blooms in generating fixed carbon for the food chain, sequestering carbon, and their unique secondary metabolites. The structural complexity of these metabolites has had an unprecedented impact on structure elucidation technologies and total synthesis, which are highlighted throughout this review. In addition, the influence of biogeochemical environmental perturbations on algal blooms and their influence on biospheric environments is discussed. Lastly, we summarize work on management strategies and technologies for the control and treatment of HABs.

Noncovalent interactions between a lone pair of electrons and π systems can be categorized into two types based on the nature of π systems. Lone pair-π(C═O) interactions with π systems of unsaturated, polarized bonds are primarily attributed to orbital interactions, whereas lone pair-π(Ar) interactions with π systems of aromatic functional groups result from electrostatic attractions (for electron-deficient aryls) or dispersion attractions and Pauli repulsions (for electron-rich/neutral aryls). Unlike well-established noncovalent interactions, lone pair-π interactions have been comparatively underappreciated or less used to influence reaction outcomes. This review emphasizes experimental and computational studies aimed at integrating lone pair-π interactions into the design of catalytic systems and utilizing these interactions to regulate the reactivity and selectivity of chemical transformations. The role of lone pair-π interactions is highlighted in the stabilization or destabilization of transition states and ground-state binding. Examples influenced by lone pair-π interactions with both unsaturated, polarized bonds and aromatic rings as π systems are included. At variance with previous reviews, the present review is not structured according to the physical origin of particular classes of lone pair-π interactions but is divided into chapters according to ways in which lone pair-π interactions affect kinetics and/or selectivity of reactions.

Catalytic CO (CO and CO) hydrogenation to valued chemicals is one of the promising approaches to address challenges in energy, environment, and climate change. HO is an inevitable side product in these reactions, where its existence and effect are often ignored. In fact, HO significantly influences the catalytic active centers, reaction mechanism, and catalytic performance, preventing us from a definitive and deep understanding on the structure-performance relationship of the authentic catalysts. It is necessary, although challenging, to clarify its effect and provide practical strategies to tune the concentration and distribution of HO to optimize its influence. In this review, we focus on how HO in CO hydrogenation induces the structural evolution of catalysts and assists in the catalytic processes, as well as efforts to understand the underlying mechanism. We summarize and discuss some representative tuning strategies for realizing the rapid removal or local enrichment of HO around the catalysts, along with brief techno-economic analysis and life cycle assessment. These fundamental understandings and strategies are further extended to the reactions of CO and CO reduction under an external field (light, electricity, and plasma). We also present suggestions and prospects for deciphering and controlling the effect of HO in practical applications.

The development of potent, specific, and pharmacologically viable chemical probes and therapeutics is a central focus of chemical biology and therapeutic development. However, a significant portion of predicted disease-causal proteins have proven resistant to targeting by traditional small molecule and biologic modalities. Many of these so-called "undruggable" targets feature extended, dynamic protein-protein and protein-nucleic acid interfaces that are central to their roles in normal and diseased signaling pathways. Here, we discuss the development of synthetically stabilized peptide and protein mimetics as an ever-expanding and powerful region of chemical space to tackle undruggable targets. These molecules aim to combine the synthetic tunability and pharmacologic properties typically associated with small molecules with the binding footprints, affinities and specificities of biologics. In this review, we discuss the historical and emerging platforms and approaches to design, screen, select and optimize synthetic "designer" peptidomimetics and synthetic biologics. We examine the inspiration and design of different classes of designer peptidomimetics: (i) macrocyclic peptides, (ii) side chain stabilized peptides, (iii) non-natural peptidomimetics, and (iv) synthetic proteomimetics, and notable examples of their application to challenging biomolecules. Finally, we summarize key learnings and remaining challenges for these molecules to become useful chemical probes and therapeutics for historically undruggable targets.

Cellular function is controlled through intricate networks of signals, which lead to the myriad pathways governing cell fate. Fluorescent biosensors have enabled the study of these signaling pathways in living systems across temporal and spatial scales. Over the years there has been an explosion in the number of fluorescent biosensors, as they have become available for numerous targets, utilized across spectral space, and suited for various imaging techniques. To guide users through this extensive biosensor landscape, we discuss critical aspects of fluorescent proteins for consideration in biosensor development, smart tagging strategies, and the historical and recent biosensors of various types, grouped by target, and with a focus on the design and recent applications of these sensors in living systems.

Noncanonical amino acids (ncAAs) are synthetic building blocks that, when incorporated into proteins, confer novel functions and enable precise control over biological processes. These small yet powerful tools offer unprecedented opportunities to investigate and manipulate various complex life forms. In particular, ncAA incorporation technology has garnered significant attention in the study of animals and their constituent cells, which serve as invaluable model organisms for gaining insights into human physiology, genetics, and diseases. This review will provide a comprehensive discussion on the applications of ncAA incorporation technology in animals and animal cells, covering past achievements, current developments, and future perspectives.

The electron density determines all properties of a system of nuclei and electrons. It is both computable and observable. Its topology allows gaining insight into the mechanisms of bonding and other phenomena in a way that is complementary to and beyond that available from the molecular orbital picture and the formal oxidation state (FOS) formalism. The ability to derive mechanistic insight from electron density is also important with methods where orbitals are not available, such as orbital-free density functional theory (OF-DFT). While density topology-based analyses such as QTAIM (quantum theory of atoms-in-molecules) have been widely used, novel, vector-based techniques recently emerged such as next-generation (NG) QTAIM. Density-dependent quantities are also actively used in machine learning (ML)-based methods, in particular, for ML DFT functional development, including machine-learnt kinetic energy functionals. We review QTAIM and its recent extensions such as NG-QTAIM and localization-delocalization matrices (LDM) and their uses in the analysis of bonding, conformations, mechanisms of redox reactions excitations, as well as ultrafast phenomena. We review recent research showing that direct density analysis can circumvent certain pitfalls of the FOS formalism, in particular in the description of anionic redox, and of the widely used (spherically) projected density of states analysis. We discuss uses of density-based quantities for the construction of DFT functionals and prospects of applications of analyses of density topology to get mechanistic insight with OF-DFT and recently developed time-dependent OF-DFT.

Photoelectrochemical (PEC) systems are essential for solar energy conversion, addressing critical energy and environmental issues. However, the low efficiency in utilizing photogenerated charge carriers significantly limits overall energy conversion. Consequently, there is a growing focus on developing strategies to enhance photoelectrode performance. This review systematically explores recent advancements in PEC system modifications, spanning from atomic and nanoscopic levels to configuration engineering. We delve into the relationships between PEC structures, intrinsic properties, kinetics of photogenerated charge carriers, and their utilization. Additionally, we propose future directions and perspectives for developing more efficient PEC systems, offering valuable insights into potential innovations in the field.

The vast majority of the human genome codes for RNA, but RNA-targeting therapeutics account for a small fraction of approved drugs. As such, there is great incentive to improve old and develop new approaches to RNA targeting. For many RNA targeting modalities, just binding is not sufficient to exert a therapeutic effect; thus, targeted RNA degradation and induced decay emerged as powerful approaches with a pronounced biological effect. This review covers the origins and advanced use cases of targeted RNA degrader technologies grouped by the nature of the targeting modality as well as by the mode of degradation. It covers both well-established methods and clinically successful platforms such as RNA interference, as well as emerging approaches such as recruitment of RNA quality control machinery, CRISPR, and direct targeted RNA degradation. We also share our thoughts on the biggest hurdles in this field, as well as possible ways to overcome them.

Hydrogen-deuterium exchange (HDX) has become a pivotal method for investigating the structural and dynamic properties of proteins. The versatility and sensitivity of mass spectrometry (MS) made the technique the ideal companion for HDX, and today HDX-MS is addressing a growing number of applications in both academic research and industrial settings. The prolific generation of experimental data has spurred the concurrent development of numerous computational tools, designed to automate parts of the workflow while employing different strategies to achieve common objectives. Various computational methods are available to perform automated peptide searches and identification; different statistical tests have been implemented to quantify differences in the exchange pattern between two or more experimental conditions; alternative strategies have been developed to deconvolve and analyze peptides showing multimodal behavior; and different algorithms have been proposed to computationally increase the resolution of HDX-MS data, with the ultimate aim to provide information at the level of the single residue. This review delves into a comprehensive examination of the merits and drawbacks associated with the diverse strategies implemented by software tools for the analysis of HDX-MS data.

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.

In this contribution, we review the electrochemical upgrading of saccharides (e.g., glucose) and sugar alcohols (e.g., glycerol) on metal and metal-oxide electrodes by drawing conclusions on common trends and differences between these two important classes of biobased compounds. For this purpose, we critically review the literature on the electrocatalytic oxidation of saccharides and sugar alcohols, seeking trends in the effect of reaction conditions and electrocatalyst design on the selectivity for the oxidation of specific functional groups toward value-added compounds. Importantly, we highlight and discuss the competition between electrochemical and non-electrochemical pathways. This is a crucial and yet often neglected aspect that should be taken into account and optimized for achieving the efficient electrocatalytic conversion of monosaccharides and related sugar alcohols into valuable products, which is a target of growing interest in the context of the electrification of the chemical industry combined with the utilization of renewable feedstock.

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 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.

The genetic code is the foundation for all life. With few exceptions, the translation of nucleic acid messages into proteins follows conserved rules, which are defined by codons that specify each of the 20 proteinogenic amino acids. For decades, leading research groups have developed a catalogue of innovative approaches to extend nature's amino acid repertoire to include one or more noncanonical building blocks in a single protein. In this review, we summarize advances in the history of and genetic code expansion, and highlight recent innovations that increase the scope of biochemically accessible monomers and codons. We further summarize state-of-the-art knowledge in engineered cellular translation, as well as alterations to regulatory mechanisms that improve overall genetic code expansion. Finally, we distill existing limitations of these technologies into must-have improvements for the next generation of technologies, and speculate on future strategies that may be capable of overcoming current gaps in knowledge.

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.