Cryo-Electron Microscopy Methodology: Current Aspects and Future Directions

Artificial intelligence approaches for anti-addiction drug discovery

Drug addiction remains a complex global public health challenge, with traditional anti-addiction drug discovery hindered by limited efficacy and slow progress in targeting intricate neurochemical systems. Advanced algorithms within artificial intelligence (AI) present a transformative solution that boosts both speed and precision in therapeutic development. This review examines how artificial intelligence serves as a crucial element in developing anti-addiction medications by targeting the opioid system along with dopaminergic and GABAergic systems, which are essential in addiction pathology. It identifies upcoming trends promising in studying less-researched addiction-linked systems through innovative general-purpose drug discovery techniques. AI holds the potential to transform anti-addiction research by breaking down conventional limitations, which will enable the development of superior treatment methods.

Cryo-electron microscopy reveals a single domain antibody with a unique binding epitope on fibroblast activation protein alpha

Fibroblast activation protein alpha (FAP) is a serine protease that is expressed at basal levels in benign tissues but is overexpressed in a variety of pathologies, including cancer. Despite this unique expression profile, designing functional diagnostic and therapeutic agents that effectively target this biomarker remains elusive. Here we report the structural characterization of the interaction between a novel single domain antibody (sdAb), I3, and FAP using cryo-electron microscopy. The reconstructions were determined to a resolution of 2.7 Å and contained two distinct populations; one I3 bound and two I3 molecules bound to the FAP dimer. In both cases, the sdAb bound a unique epitope that was distinct from the active site of the enzyme. Furthermore, this report describes the rational mutation of specific residues within the complementarity determining region 3 (CDR3) loop to enhance affinity and selectivity of the I3 molecule for FAP. This report represents the first sdAb-FAP structure to be described in the literature.

An ultra-low vibration cryostat with split design

The development of cryogenic technology promotes frontier scientific discoveries, while the device performance is often limited by vibration of cryostats. We present the design and implementation of a split-type, ultra-low vibration cryostat using a pulse tube cryocooler. Methods of gas-liquid helium mixture damping, non-contact heat exchangers, soft connections, and vibration isolating foundation are used together to suppress vibration. These innovations reduce background vibrations at the sample area to 5 × 10-7 m/s2/Hz1/2 (7 × 10-9 m/Hz1/2) @ 1-10 Hz in all directions and effectively suppress vibration harmonics of the pulse tube frequency with a suppression ratio up to 23 dB. In addition, with a low heat leakage design, the 2.2 L large sample area can be cooled down to below 4 K in 36 h, and the temperature fluctuation is 0.03 mK under active control. The performance of ultra-low vibration and fast cooling of a large sample space to below 4 K is outstanding among the reported low-vibration cryostats, which meets the demands of advanced cryogenic applications.

Computed Vibrational Heat Capacities for Gas-Phase Biomolecular Ions

Collision induced dissociation (CID) and collision induced unfolding (CIU) experiments are important tools for determining the structures of and differences between biomolecular complexes with mass spectrometry. However, quantitative comparison of CID/CIU data acquired on different platforms or even using different regions of the same instrument can be very challenging due to differences in gas identity and pressure, electric fields, and other experimental parameters. In principle, these can be reconciled by a detailed understanding of how ions heat, cool, and dissociate or unfold in time as a function of these parameters. Fundamental information needed to model these processes for different ion types and masses is their heat capacity as a function of the internal (i.e., vibrational) temperature. Here, we use quantum computational theory to predict average heat capacities as a function of temperature for a variety of model biomolecule types from 100 to 3000 K. On a degree-of-freedom basis, these values are remarkably invariant within each biomolecule type and can be used to estimate heat capacities of much larger biomolecular ions. We also explore effects of ion heating, cooling, and internal energy distribution as a function of time using a home-built program (IonSPA). We observe that these internal energy distributions can be nearly Boltzmann for larger ions (greater than a few kDa) through most of the CID/CIU kinetic window after a brief (few-μs) induction period. These results should be useful in reconciling CID/CIU results across different instrument platforms and under different experimental conditions, as well as in designing instrumentation and experiments to control CID/CIU behavior.

Transient Cross-linking Mass Spectrometry: Taking Conformational Snapshots of Proteins

The dynamic nature of protein conformations is central to their biological functions. Conventional structural biology techniques provide static snapshots, whereas a comprehensive understanding requires an analysis of the dynamic conformations. In this study, we develop a transient cross-linking mass spectrometry method using a photo-cross-linker DCD. This cross-linker can be transiently activated to accomplish cross-linking, and with sample freezing, transient conformations are preserved, allowing temporal control and on-demand cross-linking. Its cross-linking site covers all amino acids, exhibiting diversity and providing rich structural information. Additionally, we develop a data-processing strategy by integrating a DCD-specific reporter ion and a defined ambiguous site annotation criterion, thereby ensuring the confidence in identification and cross-link site annotation. Thus, the developed transient cross-linking mass spectrometry, leveraging the distinctive features of DCD, has enabled us to analyze protein conformations and protein complexes with high resolution, take conformational snapshots, discern the coexistence of conformational intermediates, and decipher conformational fluctuations, shedding light on how proteins conformationally respond to biological signals and engage with interacting partners. Our results highlight DCD's potential for probing protein conformational changes, facilitating the elucidation of their pivotal roles within biological systems.

Balanced plant helper NLR activation by a modified host protein complex

Nucleotide-binding leucine-rich repeat (NLR) receptors play crucial roles in plant immunity by sensing pathogen effectors1. In Arabidopsis, certain sensor NLRs function as NADases to catalyse the production of second messengers2,3, which can be recognized by enhanced disease susceptibility 1 (EDS1) with its partner senescence-associated gene 101 (SAG101), to activate helper NLR N requirement gene 1 (NRG1)4. A cryoelectron microscopy structure shows that second-messenger-activated EDS1-SAG101 mainly contacts the leucine-rich repeat domain of NRG1A to mediate the formation of an induced EDS1-SAG101-NRG1A complex. Structural comparisons show that binding of a second messenger induces conformational changes in EDS1-SAG101, which are recognized by NRG1A, leading to its allosteric activation. We further show that an inhibitory NRG1 family member, NRG1C, efficiently outcompetes NRG1A for binding to second-messenger-activated EDS1-SAG101. These findings uncover mechanisms for NRG1A activation through its recognition of a modified host EDS1-SAG101 complex, and NRG1A inhibition by NRG1C through sequestration of the activated EDS1-SAG101, thus shedding light on the activation and constraint of a central plant immune response system.

Navigating the challenges of engineering composite specialized metabolite pathways in plants

Plants are a valuable source of diverse specialized metabolites with numerous applications. However, these compounds are often produced in limited quantities, particularly under unfavorable ecological conditions. To achieve sufficient levels of target metabolites, alternative strategies such as pathway engineering in heterologous systems like microbes (e.g., bacteria and fungi) or cell-free systems can be employed. Another approach is plant engineering, which aims to either enhance the native production in the original plant or reconstruct the target pathway in a model plant system. Although increasing metabolite production in the native plant is a promising strategy, these source plants are often exotic and pose significant challenges for genetic manipulation. Effective pathway engineering requires comprehensive prior knowledge of the genes and enzymes involved, as well as the precursor, intermediate, branching, and final metabolites. Thus, a thorough elucidation of the biosynthetic pathway is closely linked to successful metabolic engineering in host or model systems. In this review, we highlight recent advances in strategies for biosynthetic pathway elucidation and metabolic engineering. We focus on efforts to engineer complex, multi-step pathways that require the expression of at least eight genes for transient and three genes for stable transformation. Reports on the engineering of complex pathways in stably transformed plants remain relatively scarce. We discuss the major hurdles in pathway elucidation and strategies for overcoming them, followed by an overview of achievements, challenges, and solutions in pathway reconstitution through metabolic engineering. Recent advances including computer-based predictions offer valuable platforms for the sustainable production of specialized metabolites in plants.

Chiral nanosystem and chiral supraparticles for drug delivery: an expert opinion

INTRODUCTION

Chiral nanocarriers enhance therapeutic efficacy by improving in vivo stability and cellular uptake. Chemical functionalization reduces cytotoxicity, resulting in favorable biocompatibility. Nanoparticles self-assemble into supraparticles, enhancing drug delivery through improved retention and drug loading.

AREA COVERED

This review covers chiral nanostructures and chiral supraparticles, and their applications in drug delivery and various healthcare applications.

EXPERT OPINION

The chirality of biomaterials is crucial for advancing nanomedicine. Chiral nanosystem enhance drug delivery by interacting selectively with biological molecules, improving their specificity and efficacy. This reduces off-target effects and improves therapeutic outcomes. Research has focused on cellular uptake and elimination to ensure safety, and chiral nanomaterials also show promise in optical sensing and gene editing. Their biocompatibility and ability to self-assemble into supraparticles may make them ideal for drug delivery systems.

Real-Time Observation of Polymer Fluctuations During Phase Transition Using Transmission Electron Microscope

Measuring molecular dynamics improves understanding of the structure-function relationships of materials. In this study, we present a novel technique for observing material dynamics using transmission electron microscopy (TEM), in which the gold nanoparticles are employed as motion probes for tracing the polymer dynamics in real space. A thin layer of polymer materials was generated on the 2 μm diameter holes of Quantifoil grids, and gold nanoparticles were dispersed on the membrane surface. By tracking the movement of gold nanoparticles from a series of TEM images taken under continuous temperature control, we obtained mean squared displacement (MSD) curves. The dynamics of poly{2-(perfluorooctyl)ethyl acrylate} (PC8FA) and poly(stearyl acrylate) (PSA) were analyzed. In the temperature-dependent analysis of the MSD, sharp peaks were observed for both PC8FA and PSA at positions corresponding to their melting and crystallization temperatures. These results demonstrate the capability of TEM to provide valuable insights into the dynamics of polymer materials, highlighting its potential for widespread application in materials sciences.

Advances and Mechanisms of RNA-Ligand Interaction Predictions

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.

Deciphering olfactory receptor binding mechanisms: a structural and dynamic perspective on olfactory receptors

Olfactory receptors, classified as G-protein coupled receptors (GPCRs), have been a subject of scientific inquiry since the early 1950s. Historically, investigations into the sensory mechanisms of olfactory receptors were often confined to behavioral characteristics in model organisms or the expression of related proteins and genes. However, with the development of cryo-electron microscopy techniques, it has gradually become possible to decipher the specific structures of olfactory receptors in insects and humans. This has provided new insights into the binding mechanisms between odor molecules and olfactory receptors. Furthermore, due to the rapid advancements in related fields such as computer simulations, the prediction and exploration of odor molecule binding to olfactory receptors have been progressively achieved through molecular dynamics simulations. Through this comprehensive review, we aim to provide a thorough analysis of research related to the binding mechanisms between odor molecules and olfactory receptors from the perspectives of structural biology and molecular dynamics simulations. Finally, we will provide an outlook on the future of research in the field of olfactory receptor sensory mechanisms.

Practical considerations for plunge freezing samples over 40 °C for Cryo-EM

Cryo-EM is now an established tool for examining samples in their native, hydrated states-a leap made possible by vitrification. Utilising this sample preparation method to directly visualise temperature-responsive samples allows for deeper insights into their structural behaviours under functional conditions. This requires samples to be plunge-frozen at elevated temperatures and presents additional challenges, including condensation within the blotting chamber and difficulties in maintaining a stable sample temperatures. Here, we address these challenges and suggest practical strategies to minimise condensation and reduce temperature fluctuations during the plunge-freezing of samples at elevated temperatures (>40 °C). By preheating equipment and reducing chamber humidity and blotting times, we can improve sample preservation and grid reproducibility. These considerations are then demonstrated on poly(N-isopropylacrylamide) microgels, which exhibit a volume phase transition due to temperature changes.

Ice thickness control and measurement in the VitroJet for time-efficient single particle structure determination

Embedding biomolecules in vitreous ice of optimal thickness is critical for structure determination by cryo-electron microscopy. Ice thickness assessment and selection of suitable holes for data collection are currently part of time-consuming preparatory routines performed on expensive electron microscopes. To address this challenge, a routine has been developed to measure ice thickness during sample preparation using an optical camera integrated in the VitroJet. This method allows to estimate the ice thickness with an error below ±20 nm for ice layers in the range of 0-70 nm. Additionally, we characterized the influence of pin printing parameters and found that the median ice thickness can be reproduced with a standard deviation below ±11 nm for thicknesses up to 75 nm. Therefore, the ice thickness of buffer-suspended holes on an EM grid can be tuned and measured within the working range relevant for single particle cryo-EM. Single particle structures of apoferritin were determined at two distinct thicknesses of 30 nm and 70 nm. These reconstructions demonstrate the importance of ice thickness for time-efficient cryo-EM structure determination.

Orientation Determination of Cryo-EM Projection Images Using Reliable Common Lines and Spherical Embeddings

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a critical technique for recovering and studying the fine 3D structure of proteins and other biological macromolecules, where the primary issue is to determine the orientations of projection images with high levels of noise. This paper proposes a method to determine the orientations of cryo-EM projection images using reliable common lines and spherical embeddings. First, the reliability of common lines between projection images is evaluated using a weighted voting algorithm based on an iterative improvement technique and binarized weighting. Then, the reliable common lines are used to calculate the normal vectors and local -axis vectors of projection images after two spherical embeddings. Finally, the orientations of projection images are determined by aligning the results of the two spherical embeddings using an orthogonal constraint. Experimental results on both synthetic and real cryo-EM projection image datasets demonstrate that the proposed method can achieve higher accuracy in estimating the orientations of projection images and higher resolution in reconstructing preliminary 3D structures than some common line-based methods, indicating that the proposed method is effective in single-particle cryo-EM 3D reconstruction.

Using reverse vaccinology techniques combined with B-cell epitope prediction to screen potential antigenic proteins of the bovine pathogen Clostridium perfringens type A

Clostridium perfringens type A frequently causes necrohaemorrhagic enteritis in cattle, a rapidly progressing disease with a high mortality rate, thus inflicting substantial economic losses in the cattle industry. Effective prevention and control of this disease rely on rapid detection and vaccination strategies, making the screening of antigenic proteins with diagnostic and vaccine potential particularly crucial. In this study, we conducted a pangenomic analysis of 15 bacterial strains, grounded in traditional reverse vaccinology and supplemented with B-cell linear and conformational epitope analysis tools. This approach led to the identification of 2304 core genes and 3606 accessory genes, among which 58 surface-exposed proteins, encoded by core genes, were identified Proteins lacking tertiary structure information were predicted via AlphaFold2, ultimately identifying four target proteins and 14 candidate proteins enriched with linear and conformational epitopes, including virulence proteins such as alpha-toxin, theta-toxin, and alpha-clostripain, and extracellular solute-binding proteins, rhodanese-like proteins, and the accessory gene-encoded lysozyme inhibitor LprI family protein. Our findings demonstrate that the combined use of multiple B-cell epitope analysis tools can help overcome the limitations of any single tool. The proteins selected in this study offer valuable references for rapid diagnostics and the development of genetically engineered vaccines.

Cryo-electron microscopy reveals a single domain antibody with a unique binding epitope on fibroblast activation protein alpha

Fibroblast activation protein alpha (FAP) is a serine protease that is expressed at basal levels in benign tissues but is overexpressed in a variety of pathologies, including cancer. Despite this unique expression profile, designing effective diagnostic and therapeutic agents that effectively target this biomarker remain elusive. Here we report the structural characterization of the interaction between a novel single domain antibody (sdAbs), I3, and FAP using cryo-electron microscopy. The reconstructions were determined to a resolution of 2.7 Å and contained two distinct populations; one I3 bound and two I3 molecules bound to the FAP dimer. In both cases, the sdAbs bound a unique epitope that was distinct from the active site of the enzyme. Furthermore, this report describes the rational mutation of specific residues within the complementarity determining region 3 (CDR3) loop to enhance affinity and selectivity of the I3 molecule for FAP. This report represents the first sdAb-FAP structure to be described in the literature.

Conformational Characterization of Peptides and Proteins by 193 nm Ultraviolet Photodissociation in the Collision Cell of a Trapped Ion Mobility Spectrometry-Time-of-Flight Mass Spectrometer

Ultraviolet photodissociation (UVPD) has been shown to be a versatile ion activation strategy for the characterization of peptides and intact proteins among other classes of biological molecules. Combining the high-performance mass spectrometry (MS/MS) capabilities of UVPD with the high-resolution separation of trapped ion mobility spectrometry (TIMS) presents an opportunity for enhanced structural elucidation of biological molecules. In the present work, we integrate a 193 nm excimer laser in a TIMS-time-of-flight (TIMS-TOF) mass spectrometer for UVPD in the collision cell and use it for the analysis of several mass-mobility-selected species of ubiquitin and myoglobin. The resultant data displayed differences in fragmentation that could be correlated with changes in protein conformation. Additionally, this mobility-resolved UVPD strategy was applied to collision-induced unfolded ions of ubiquitin to follow changes in fragmentation patterns relating to the extent of protein unfolding. This platform and methodology offer new opportunities for exploring how conformational variations are manifested in the fragmentation patterns of gas-phase ions.

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.

Semantic segmentation-based detection algorithm for challenging cryo-electron microscopy RNP samples

In this study, we present a novel and robust methodology for the automatic detection of influenza A virus ribonucleoproteins (RNPs) in single-particle cryo-electron microscopy (cryo-EM) images. Utilizing a U-net architecture-a type of convolutional neural network renowned for its efficiency in biomedical image segmentation-our approach is based on a pretraining phase with a dataset annotated through visual inspection. This dataset facilitates the precise identification of filamentous RNPs, including the localization of the filaments and their terminal coordinates. A key feature of our method is the application of semantic segmentation techniques, enabling the automated categorization of micrograph pixels into distinct classifications of particle and background. This deep learning strategy allows to robustly detect these intricate particles, a crucial step in achieving high-resolution reconstructions in cryo-EM studies. To encourage collaborative advancements in the field, we have made our routines, the pretrained U-net model, and the training dataset publicly accessible. The reproducibility and accessibility of these resources aim to facilitate further research and validation in the realm of cryo-EM image analysis.

Low-dose cryo-electron ptychography of proteins at sub-nanometer resolution

Cryo-transmission electron microscopy (cryo-EM) of frozen hydrated specimens is an efficient method for the structural analysis of purified biological molecules. However, cryo-EM and cryo-electron tomography are limited by the low signal-to-noise ratio (SNR) of recorded images, making detection of smaller particles challenging. For dose-resilient samples often studied in the physical sciences, electron ptychography - a coherent diffractive imaging technique using 4D scanning transmission electron microscopy (4D-STEM) - has recently demonstrated excellent SNR and resolution down to tens of picometers for thin specimens imaged at room temperature. Here we apply 4D-STEM and ptychographic data analysis to frozen hydrated proteins, reaching sub-nanometer resolution 3D reconstructions. We employ low-dose cryo-EM with an aberration-corrected, convergent electron beam to collect 4D-STEM data for our reconstructions. The high frame rate of the electron detector allows us to record large datasets of electron diffraction patterns with substantial overlaps between the interaction volumes of adjacent scan positions, from which the scattering potentials of the samples are iteratively reconstructed. The reconstructed micrographs show strong SNR enabling the reconstruction of the structure of apoferritin protein at up to 5.8 Å resolution. We also show structural analysis of the Phi92 capsid and sheath, tobacco mosaic virus, and bacteriorhodopsin at slightly lower resolutions.

High-resolution single-particle imaging at 100-200 keV with the Gatan Alpine direct electron detector

Developments in direct electron detector technology have played a pivotal role in enabling high-resolution structural studies by cryo-EM at 200 and 300 keV. Yet, theory and recent experiments indicate advantages to imaging at 100 keV, energies for which the current detectors have not been optimized. In this study, we evaluated the Gatan Alpine detector, designed for operation at 100 and 200 keV. Compared to the Gatan K3, Alpine demonstrated a significant DQE improvement at these energies, specifically a ∼ 4-fold improvement at Nyquist at 100 keV. In single-particle cryo-EM experiments, Alpine datasets yielded better than 2 Å resolution reconstructions of apoferritin at 120 and 200 keV on a ThermoFisher Scientific (TFS) Glacios microscope fitted with a non-standard SP-Twin lens. We also achieved a ∼ 3.2 Å resolution reconstruction of a 115 kDa asymmetric protein complex, proving Alpine's effectiveness with complex biological samples. In-depth analysis revealed that Alpine reconstructions are comparable to K3 reconstructions at 200 keV, and remarkably, reconstruction from Alpine at 120 keV on a TFS Glacios surpassed all but the 300 keV data from a TFS Titan Krios with GIF/K3. Additionally, we show Alpine's capability for high-resolution data acquisition and screening on lower-end systems by obtaining ∼ 3 Å resolution reconstructions of apoferritin and aldolase at 100 keV and detailed 2D averages of a 55 kDa sample using a side-entry cryo holder. Overall, we show that Gatan Alpine performs well with the standard 200 keV imaging systems and may potentially capture the benefits of lower accelerating voltages, bringing smaller sized particles within the scope of cryo-EM.

Recent progress in quantitative analysis of self-assembled peptides

Self-assembled peptides have been among the important biomaterials due to its excellent biocompatibility and diverse functions. Over the past decades, substantial progress and breakthroughs have been made in designing self-assembled peptides with multifaceted biomedical applications. The techniques for quantitative analysis, including imaging-based quantitative techniques, chromatographic technique and computational approach (molecular dynamics simulation), are becoming powerful tools for exploring the structure, properties, biomedical applications, and even supramolecular assembly processes of self-assembled peptides. However, a comprehensive review concerning these quantitative techniques remains scarce. In this review, recent progress in techniques for quantitative investigation of biostability, cellular uptake, biodistribution, self-assembly behaviors of self-assembled peptide etc., are summarized. Specific applications and roles of these techniques are highlighted in detail. Finally, challenges and outlook in this field are concluded. It is believed that this review will provide technical guidance for researchers in the field of peptide-based materials and pharmaceuticals, and facilitate related research for newcomers in this field.

Visualizing Dynamic Single Atom Catalysis

Many industrial chemical processes, including for producing fuels, foods, pharmaceuticals, chemicals and environmental controls, employ heterogeneous solid state catalysts at elevated temperatures in gas or liquid environments. Dynamic reactions at the atomic level play a critical role in catalyst stability and functionality. In situ visualization and analysis of atomic-scale processes in real time under controlled reaction environments can provide important insights into practical frameworks to improve catalytic processes and materials. This review focuses on innovative real time in situ electron microscopy (EM) methods, including recent progress in analytical in situ environmental (scanning) transmission EM (E(STEM), incorporating environmental scanning TEM (ESTEM) and environmental transmission EM (ETEM), with single atom resolution for visualizing and analysing dynamic single atom catalysis under controlled flowing gas reaction environments. ESTEM studies of single atom dynamics of reactions, and of sintering deactivation, contribute to a better-informed understanding of the yield and stability of catalyst operations. Advances in in situ technologies, including gas and liquid sample holders, nanotomography, and higher voltages, as well as challenges and opportunities in tracking reacting atoms, are highlighted. The findings show that the understanding and application of fundamental processes in catalysis can be improved, with valuable economic, environmental, and societal benefits.

CryoEM Workflow Acceleration with Feret Signatures

Common challenges in cryogenic electron microscopy, such as orientation bias, conformational diversity, and 3D misclassification, complicate single particle analysis and lead to significant resource expenditure. We previously introduced an in silico method using the maximum Feret diameter distribution, the Feret signature, to characterize sample heterogeneity of disc-shaped samples. Here, we expanded the Feret signature methodology to identify preferred orientations of samples containing arbitrary shapes with only about 1000 particles required. This method enables real-time adjustments of data acquisition parameters for optimizing data collection strategies or aiding in decisions to discontinue ineffective imaging sessions. Beyond detecting preferred orientations, the Feret signature approach can serve as an early-warning system for inconsistencies in classification during initial image processing steps, a capability that allows for strategic adjustments in data processing. These features establish the Feret signature as a valuable auxiliary tool in the context of single particle analysis, significantly accelerating the structure determination process.

Taste and its receptors in human physiology: A comprehensive look

Increasing evidence shows that food has significance beyond traditional perception (providing nutrition and energy) in maintaining normal life activities. It is indicated that the sense of taste plays a crucial part in regulating human life activities. Taste is one of the basic physiological sensations in mammals, and it is the fundamental guarantee for them to perceive, select, and ingest nutrients in order to survive. With the advances in electrophysiology, molecular biology, and structural biology, studies on the intracellular and extracellular transduction mechanisms of taste have made great progress and gradually revealed the indispensable role of taste receptors in the regulation and maintenance of normal physiological activities. Up to now, how food regulates life activities through the taste pathway remains unclear. Thus, this review comprehensively and systematically summarizes the current study about the sense of taste, the function of taste receptors, the taste–structure relationship of gustatory molecules, the cross‐talking between distinctive tastes, and the role of the gut–organ axis in the realization of taste. Moreover, we also provide forward‐looking perspectives on taste research to afford a scientific basis for revealing the scientific connotation of taste receptors regulating body health.

Mitigating the Blurring Effect of CryoEM Averaging on a Flexible and Highly Symmetric Protein Complex through Sub-Particle Reconstruction

Many macromolecules are inherently flexible as a feature of their structure and function. During single-particle CryoEM processing, flexible protein regions can be detrimental to high-resolution reconstruction as signals from thousands of particles are averaged together. This "blurring" effect can be difficult to overcome and is possibly more pronounced when averaging highly symmetric complexes. Approaches to mitigating flexibility during CryoEM processing are becoming increasingly critical as the technique advances and is applied to more dynamic proteins and complexes. Here, we detail the use of sub-particle averaging and signal subtraction techniques to precisely target and resolve flexible DARPin protein attachments on a designed tetrahedrally symmetric protein scaffold called DARP14. Particles are first aligned as full complexes, and then the symmetry is reduced by alignment and focused refinement of the constituent subunits. The final reconstructions we obtained were vastly improved over the fully symmetric reconstructions, with observable secondary structure and side-chain placement. Additionally, we were also able to reconstruct the core region of the scaffold to 2.7 Å. The data processing protocol outlined here is applicable to other dynamic and symmetric protein complexes, and our improved maps could allow for new structure-guided variant designs of DARP14.

Mitochondria in disease: changes in shapes and dynamics

Mitochondrial structure often determines the function of these highly dynamic, multifunctional, eukaryotic organelles, which are essential for maintaining cellular health. The dynamic nature of mitochondria is apparent in descriptions of different mitochondrial shapes [e.g., donuts, megamitochondria (MGs), and nanotunnels] and crista dynamics. This review explores the significance of dynamic alterations in mitochondrial morphology and regulators of mitochondrial and cristae shape. We focus on studies across tissue types and also describe new microscopy techniques for detecting mitochondrial morphologies both in vivo and in vitro that can improve understanding of mitochondrial structure. We highlight the potential therapeutic benefits of regulating mitochondrial morphology and discuss prospective avenues to restore mitochondrial bioenergetics to manage diseases related to mitochondrial dysfunction.

High-resolution single-particle imaging at 100-200 keV with the Gatan Alpine direct electron detector

Developments in direct electron detector technology have played a pivotal role in enabling high-resolution structural studies by cryo-EM at 200 and 300 keV. Yet, theory and recent experiments indicate advantages to imaging at 100 keV, energies for which the current detectors have not been optimized. In this study, we evaluated the Gatan Alpine detector, designed for operation at 100 and 200 keV. Compared to the Gatan K3, Alpine demonstrated a significant DQE improvement at these voltages, specifically a ~4-fold improvement at Nyquist at 100 keV. In single-particle cryo-EM experiments, Alpine datasets yielded better than 2 Å resolution reconstructions of apoferritin at 120 and 200 keV on a ThermoFisher Scientific (TFS) Glacios microscope. We also achieved a ~3.2 Å resolution reconstruction for a 115 kDa asymmetric protein complex, proving its effectiveness with complex biological samples. In-depth analysis revealed that Alpine reconstructions are comparable to K3 reconstructions at 200 keV, and remarkably, reconstruction from Alpine at 120 keV on a TFS Glacios surpassed all but the 300 keV data from a TFS Titan Krios with GIF/K3. Additionally, we show Alpine's capability for high-resolution data acquisition and screening on lower-end systems by obtaining ~3 Å resolution reconstructions of apoferritin and aldolase at 100 keV and detailed 2D averages of a 55 kDa sample using a side-entry cryo holder. Overall, we show that Gatan Alpine performs well with the standard 200 keV imaging systems and may potentially capture the benefits of lower accelerating voltages, possibly bringing smaller sized particles within the scope of cryo-EM.

Chemical Reactions in Living Systems

The term "in vivo ("in the living") chemistry" refers to chemical reactions that take place in a complex living system such as cells, tissue, body liquids, or even in an entire organism. In contrast, reactions that occur generally outside living organisms in an artificial environment (e.g., in a test tube) are referred to as in vitro. Over the past decades, significant contributions have been made in this rapidly growing field of in vivo chemistry, but it is still not fully understood, which transformations proceed efficiently without the formation of by-products or how product formation in such complex environments can be characterized. Potential applications can be imagined that synthesize drug molecules directly within the cell or confer new cellular functions through controlled chemical transformations that will improve the understanding of living systems and develop new therapeutic strategies. The guiding principles of this contribution are twofold: 1) Which chemical reactions can be translated from the laboratory to the living system? 2) Which characterization methods are suitable for studying reactions and structure formation in complex living environments?

High-Throughput Identification of Crystalline Natural Products from Crude Extracts Enabled by Microarray Technology and microED

The structural determination of natural products (NPs) can be arduous because of sample heterogeneity. This often demands iterative purification processes and characterization of complex molecules that may be available only in miniscule quantities. Microcrystal electron diffraction (microED) has recently shown promise as a method to solve crystal structures of NPs from nanogram quantities of analyte. However, its implementation in NP discovery remains hampered by sample throughput and purity requirements, akin to traditional NP-discovery workflows. In the methods described herein, we leverage the resolving power of transmission electron microscopy (TEM) and the miniaturization capabilities of deoxyribonucleic acid (DNA) microarray technology to address these challenges through the establishment of an NP screening platform, array electron diffraction (ArrayED). In this workflow, an array of high-performance liquid chromatography (HPLC) fractions taken from crude extracts was deposited onto TEM grids in picoliter-sized droplets. This multiplexing of analytes on TEM grids enables 1200 or more unique samples to be simultaneously inserted into a TEM instrument equipped with an autoloader. Selected area electron diffraction analysis of these microarrayed grids allows for the rapid identification of crystalline metabolites. In this study, ArrayED enabled structural characterization of 14 natural products, including four novel crystal structures and two novel polymorphs, from 20 crude extracts. Moreover, we identify several chemical species that would not be detected by standard mass spectrometry (MS) or ultraviolet-visible (UV/vis) spectroscopy and crystal forms that would not be characterized using traditional methods.

Dose symmetric electron diffraction tomography (DS-EDT): Implementation of a dose-symmetric tomography scheme in 3D electron diffraction

Beam sensitive nanomaterials such as zeolites or metal-organic frameworks (MOF) represent a great challenge for crystallographic structure determination and refinement. The strong electron-matter interaction and the high spatial resolution achievable make electron diffraction the technique of choice for particles of sizes below a micrometer and many different 3-dimensional electron diffraction (3D ED) techniques have been developed in recent years. Nevertheless, beam sensitivity of the samples can lead to the crystal structure being damaged during the data acquisition impeding the determination of its structure. A simple way to reduce beam damage is to lower the dose during the experiment. However, this implies weaker diffraction intensities which can become problematic for the exploitation of the data. In order to obtain complete data sets with strong intensities without damaging the crystals, we developed the dose symmetric electron diffraction tomography (DS-EDT) method, combining the low-dose electron diffraction tomography (LD-EDT) technique with the dose-symmetric tomography scheme known from cryo-EM. In order to reduce the dose on an individual crystal and still obtain enough data for a structure solution and refinement, we partition the dose over several crystals. The individual datasets are then merged in order to achieve the necessary completeness. On two test structures we first show that merging of data from small domains of the reciprocal space is indeed sufficient to obtain reliable data for structure solution and refinement. Second, we show on the beam sensitive manganese formate that high-quality data can be obtained on a few frames while the frames that have suffered from beam damage can still be used to determine the orientation matrix and the unit cell of the crystals. The results from the dynamical refinement on the obtained data show a high accuracy of the atom positions. In this way, DS-EDT can reduce the total dose on an individual crystal by an order of magnitude with respect to the already very dose-efficient LD-EDT.

A minority of final stacks yields superior amplitude in single-particle cryo-EM

Cryogenic electron microscopy (cryo-EM) is widely used to determine near-atomic resolution structures of biological macromolecules. Due to the low signal-to-noise ratio, cryo-EM relies on averaging many images. However, a crucial question in the field of cryo-EM remains unanswered: how close can we get to the minimum number of particles required to reach a specific resolution in practice? The absence of an answer to this question has impeded progress in understanding sample behavior and the performance of sample preparation methods. To address this issue, we develop an iterative particle sorting and/or sieving method called CryoSieve. Extensive experiments demonstrate that CryoSieve outperforms other cryo-EM particle sorting algorithms, revealing that most particles are unnecessary in final stacks. The minority of particles remaining in the final stacks yield superior high-resolution amplitude in reconstructed density maps. For some datasets, the size of the finest subset approaches the theoretical limit.

Predicting virus Fitness: Towards a structure-based computational model

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogens. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein (∼380,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

Detecting continuous structural heterogeneity in single-molecule localization microscopy data

Fusion of multiple chemically identical complexes, so-called particles, in localization microscopy, can improve the signal-to-noise ratio and overcome under-labeling. To this end, structural homogeneity of the data must be assumed. Biological heterogeneity, however, could be present in the data originating from distinct conformational variations or (continuous) variations in particle shapes. We present a prior-knowledge-free method for detecting continuous structural variations with localization microscopy. Detecting this heterogeneity leads to more faithful fusions and reconstructions of the localization microscopy data as their heterogeneity is taken into account. In experimental datasets, we show the continuous variation of the height of DNA origami tetrahedrons imaged with 3D PAINT and of the radius of Nuclear Pore Complexes imaged in 2D with STORM. In simulation, we study the impact on the heterogeneity detection pipeline of Degree Of Labeling and of structural variations in the form of two independent modes.

Reconstruction of a fatty acid synthesis cycle from acyl carrier protein and cofactor structural snapshots

Fatty acids (FAs) play a central metabolic role in living cells as constituents of membranes, cellular energy reserves, and second messenger precursors. A 2.6 MDa FA synthase (FAS), where the enzymatic reactions and structures are known, is responsible for FA biosynthesis in yeast. Essential in the yeast FAS catalytic cycle is the acyl carrier protein (ACP) that actively shuttles substrates, biosynthetic intermediates, and products from one active site to another. We resolve the S. cerevisiae FAS structure at 1.9 Å, elucidating cofactors and water networks involved in their recognition. Structural snapshots of ACP domains bound to various enzymatic domains allow the reconstruction of a full yeast FA biosynthesis cycle. The structural information suggests that each FAS functional unit could accommodate exogenous proteins to incorporate various enzymatic activities, and we show proof-of-concept experiments where ectopic proteins are used to modulate FAS product profiles.

Thirty years of the NaCl cotransporter: from cloning to physiology and structure

The primary structure of the thiazide-sensitive NaCl cotransporter (NCC) was resolved 30 years ago by the molecular identification of the cDNA encoding this cotransporter, from the winter's flounder urinary bladder, following a functional expression strategy. This review outlines some aspects of how the knowledge about thiazide diuretics and NCC evolved, the history of the cloning process, and the expansion of the SLC12 family of electroneutral cotransporters. The diseases associated with activation or inactivation of NCC are discussed, as well as the molecular model by which the activity of NCC is regulated. The controversies in the field are discussed as well as recent publication of the three-dimensional model of NCC obtained by cryo-electron microscopy, revealing not only the amino acid residues critical for Na+ and Cl- translocation but also the residues critical for polythiazide binding to the transporter, opening the possibility for a new era in thiazide diuretic therapy.

SLC12A cryo-EM: analysis of relevant ion binding sites, structural domains, and amino acids

The solute carrier family 12A (SLC12A) superfamily of membrane transporters modulates the movement of cations coupled with chloride across the membrane. In doing so, these cotransporters are involved in numerous aspects of human physiology: cell volume regulation, ion homeostasis, blood pressure regulation, and neurological action potential via intracellular chloride concentration modulation. Their physiological characterization has been largely studied; however, understanding the mechanics of their function and the relevance of structural domains or specific amino acids has been a pending task. In recent years, single-particle cryogenic electron microscopy (cryo-EM) has been successfully applied to members of the SLC12A family including all K+:Cl- cotransporters (KCCs), Na+:K+:2Cl- cotransporter NKCC1, and recently Na+:Cl- cotransporter (NCC); revealing structural elements that play key roles in their function. The present review analyzes the data provided by these cryo-EM reports focusing on structural domains and specific amino acids involved in ion binding, domain interactions, and other important SCL12A structural elements. A comparison of cryo-EM data from NKCC1 and KCCs is presented in the light of the two recent NCC cryo-EM studies, to propose insight into structural elements that might also be found in NCC and are necessary for its proper function. In the final sections, the importance of key coordination residues for substrate specificity and their implication on various pathophysiological conditions and genetic disorders is reviewed, as this could provide the basis to correlate structural elements with the development of novel and selective treatments, as well as mechanistic insight into the function and regulation of cation-coupled chloride cotransporters (CCCs).

Preparation of Uniform Nano Liposomes Using Focused Ultrasonic Technology

Liposomes are microspheres produced by placing phospholipids in aqueous solutions. Liposomes have the advantage of being able to encapsulate both hydrophilic and hydrophobic functional substances and are thus important mediators used in cosmetics and pharmaceuticals. It is important for liposomes to have small sizes, uniform particle size distribution, and long-term stability. Previously, liposomes have been prepared using a homo mixer, microfluidizer, and horn and bath types of sonicators. However, it is difficult to produce liposomes with small sizes and uniform particle size distribution using these methods. Therefore, we have developed a focused ultrasound method to produce nano-sized liposomes with better size control. In this study, the liposome solutions were prepared using the focused ultrasound method and conventional methods. The liposome solutions were characterized for their size distribution, stability, and morphology. Results showed that the liposome solution prepared using focused ultrasonic equipment had a uniform particle size distribution with an average size of 113.6 nm and a polydispersity index value of 0.124. Furthermore, the solution showed good stability in dynamic light scattering measurements for 4 d and Turbiscan measurements for 1 week.

Determining subunit-subunit interaction from statistics of cryo-EM images: observation of nearest-neighbor coupling in a circadian clock protein complex

Biological processes are typically actuated by dynamic multi-subunit molecular complexes. However, interactions between subunits, which govern the functions of these complexes, are hard to measure directly. Here, we develop a general approach combining cryo-EM imaging technology and statistical modeling and apply it to study the hexameric clock protein KaiC in Cyanobacteria. By clustering millions of KaiC monomer images, we identify two major conformational states of KaiC monomers. We then classify the conformational states of (>160,000) KaiC hexamers by the thirteen distinct spatial arrangements of these two subunit states in the hexamer ring. We find that distributions of the thirteen hexamer conformational patterns for two KaiC phosphorylation mutants can be fitted quantitatively by an Ising model, which reveals a significant cooperativity between neighboring subunits with phosphorylation shifting the probability of subunit conformation. Our results show that a KaiC hexamer can respond in a switch-like manner to changes in its phosphorylation level.

A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps

Cryo electron microscopy (cryo-EM) is used by biological research to visualize biomolecular complexes in 3D, but the heterogeneity of cryo-EM reconstructions is not easily estimated. Current processing paradigms nevertheless exert great effort to reduce flexibility and heterogeneity to improve the quality of the reconstruction. Clustering algorithms are typically employed to identify populations of data with reduced variability, but lack assessment of remaining heterogeneity. Here we develope a fast and simple algorithm based on spatial filtering to estimate the heterogeneity of a reconstruction. In the absence of flexibility, this estimate approximates macromolecular component occupancy. We show that our implementation can derive reasonable input parameters, that composition heterogeneity can be estimated based on contrast loss, and that the reconstruction can be modified accordingly to emulate altered constituent occupancy. This stands to benefit conventionally employed maximum-likelihood classification methods, whereas we here limit considerations to cryo-EM map interpretation, quantification, and particle-image signal subtraction.

Chloroplast gene expression: Recent advances and perspectives

Chloroplasts evolved from an ancient cyanobacterial endosymbiont more than 1.5 billion years ago. During subsequent coevolution with the nuclear genome, the chloroplast genome has remained independent, albeit strongly reduced, with its own transcriptional machinery and distinct features, such as chloroplast-specific innovations in gene expression and complicated post-transcriptional processing. Light activates the expression of chloroplast genes via mechanisms that optimize photosynthesis, minimize photodamage, and prioritize energy investments. Over the past few years, studies have moved from describing phases of chloroplast gene expression to exploring the underlying mechanisms. In this review, we focus on recent advances and emerging principles that govern chloroplast gene expression in land plants. We discuss engineering of pentatricopeptide repeat proteins and its biotechnological effects on chloroplast RNA research; new techniques for characterizing the molecular mechanisms of chloroplast gene expression; and important aspects of chloroplast gene expression for improving crop yield and stress tolerance. We also discuss biological and mechanistic questions that remain to be answered in the future.

Big data in contemporary electron microscopy: challenges and opportunities in data transfer, compute and management

The second decade of the twenty-first century witnessed a new challenge in the handling of microscopy data. Big data, data deluge, large data, data compliance, data analytics, data integrity, data interoperability, data retention and data lifecycle are terms that have introduced themselves to the electron microscopy sciences. This is largely attributed to the booming development of new microscopy hardware tools. As a result, large digital image files with an average size of one terabyte within one single acquisition session is not uncommon nowadays, especially in the field of cryogenic electron microscopy. This brings along numerous challenges in data transfer, compute and management. In this review, we will discuss in detail the current state of international knowledge on big data in contemporary electron microscopy and how big data can be transferred, computed and managed efficiently and sustainably. Workflows, solutions, approaches and suggestions will be provided, with the example of the latest experiences in Australia. Finally, important principles such as data integrity, data lifetime and the FAIR and CARE principles will be considered.

Development of Single Molecule Techniques for Sensing and Manipulation of CRISPR and Polymerase Enzymes

Clustered regularly interspaced short palindromic repeats (CRISPR) and polymerases are powerful enzymes and their diverse applications in genomics, proteomics, and transcriptomics have revolutionized the biotechnology industry today. CRISPR has been widely adopted for genomic editing applications and Polymerases can efficiently amplify genomic transcripts via polymerase chain reaction (PCR). Further investigations into these enzymes can reveal specific details about their mechanisms that greatly expand their use. Single-molecule techniques are an effective way to probe enzymatic mechanisms because they may resolve intermediary conformations and states with greater detail than ensemble or bulk biosensing techniques. This review discusses various techniques for sensing and manipulation of single biomolecules that can help facilitate and expedite these discoveries. Each platform is categorized as optical, mechanical, or electronic. The methods, operating principles, outputs, and utility of each technique are briefly introduced, followed by a discussion of their applications to monitor and control CRISPR and Polymerases at the single molecule level, and closing with a brief overview of their limitations and future prospects.

Cryo-EM uniqueness in structure determination of macromolecular complexes: A selected structural anthology

Cryogenic electron microscopy (cryo-EM) has become in the past 10 years one of the major tools for the structure determination of proteins. Nowadays, the structure prediction field is experiencing the same revolution and, using AlphaFold2, it is possible to have high-confidence atomic models for virtually any polypeptide chain, smaller than 4000 amino acids, in a simple click. Even in a scenario where all polypeptide chain folding were to be known, cryo-EM retains specific characteristics that make it a unique tool for the structure determination of macromolecular complexes. Using cryo-EM, it is possible to obtain near-atomic structures of large and flexible mega-complexes, describe conformational panoramas, and potentially develop a structural proteomic approach from fully ex vivo specimens.

Enhancement of SARS-CoV-2 receptor-binding domain activity by two microbial defensins

Peptide binders are of great interest to both basic and biomedical research due to their unique properties in manipulating protein functions in a precise spatial and temporal manner. The receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein is a ligand that captures human angiotensin-converting enzyme 2 (ACE2) to initiate infection. The development of binders of RBDs has value either as antiviral leads or as versatile tools to study the functional properties of RBDs dependent on their binding positions on the RBDs. In this study, we report two microbe-derived antibacterial defensins with RBD-binding activity. These two naturally occurring binders bind wild-type RBD (WT RBD) and RBDs from various variants with moderate-to-high affinity (7.6-1,450 nM) and act as activators that enhance the ACE2-binding activity of RBDs. Using a computational approach, we mapped an allosteric pathway in WT RBD that connects its ACE2-binding sites to other distal regions. The latter is targeted by the defensins, in which a cation-π interaction could trigger the peptide-elicited allostery in RBDs. The discovery of the two positive allosteric peptides of SARS-CoV-2 RBD will promote the development of new molecular tools for investigating the biochemical mechanisms of RBD allostery.

Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates

Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Cα atoms in protein chains were developed: (1) a mathematical approach, (2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.

3D reconstruction of unstained weakly scattering cells from a single defocused hologram

We investigate the problem of 3D complex field reconstruction corresponding to unstained red blood cells (RBCs) with a single defocused off-axis digital hologram. The main challenge in this problem is the localization of cells to the correct axial range. While investigating the volume recovery problem for a continuous phase object like the RBC, we observe an interesting feature of the backpropagated field that it does not show a clear focusing effect. Therefore, sparsity enforcement within the iterative optimization framework using a single hologram data frame cannot effectively restrict the reconstruction to the true object volume. For phase objects, it is known that the amplitude contrast of the backpropagated object field at the focus plane is minimum. We use this information available in the recovered object field in the hologram plane to device depth-dependent weights that are proportional to the inverse of amplitude contrast. This weight function is employed in the iterative steps of the optimization algorithm to assist the object volume localization. The overall reconstruction process is performed using the mean gradient descent (MGD) framework. Experimental illustrations of 3D volume reconstruction of the healthy as well as malaria-infected RBCs are presented. A test sample of polystyrene microsphere bead is also used to validate the axial localization capability of the proposed iterative technique. The proposed methodology is simple to implement experimentally and provides an approximate tomographic solution, which is axially restricted and consistent with the object field data.

Development of Yin-Yang ligand for cannabinoid receptors

Cannabinoid receptors (CBs), including CB1 and CB2, are the key components of a lipid signaling endocannabinoid system (ECS). Development of synthetic cannabinoids has been attractive to modulate ECS functions. CB1 and CB2 are structurally closely related subtypes but with distinct functions. While most efforts focus on the development of selective ligands for single subtype to circumvent the undesired off-target effect, Yin-Yang ligands with opposite pharmacological activities simultaneously on two subtypes, offer unique therapeutic potential. Herein we report the development of a new Yin-Yang ligand which functions as an antagonist for CB1 and concurrently an agonist for CB2. We found that in the pyrazole-cored scaffold, the arm of N1-phenyl group could be a switch, modification of which yielded various ligands with distinct activities. As such, the ortho-morpholine substitution exerted the desired Yin-Yang bifunctionality which, based on the docking study and molecular dynamic simulation, was proposed to be resulted from the hydrogen bonding with S173 and S285 in CB1 and CB2, respectively. Our results demonstrated the feasibility of structure guided ligand evolution for challenging Yin-Yang ligand.

In silico modelling of the function of disease-related CAZymes

In silico modelling of proteins comprises a diversity of computational tools aimed to obtain structural, electronic, and/or dynamic information about these biomolecules, capturing mechanistic details that are challenging to experimental approaches, such as elusive enzyme-substrate complexes, short-lived intermediates, and reaction transition states (TS). The present article gives the reader insight on the use of in silico modelling techniques to understand complex catalytic reaction mechanisms of carbohydrate-active enzymes (CAZymes), along with the underlying theory and concepts that are important in this field. We start by introducing the significance of carbohydrates in nature and the enzymes that process them, CAZymes, highlighting the conformational flexibility of their carbohydrate substrates. Three commonly used in silico methods (classical molecular dynamics (MD), hybrid quantum mechanics/molecular mechanics (QM/MM), and enhanced sampling techniques) are described for nonexpert readers. Finally, we provide three examples of the application of these methods to unravel the catalytic mechanisms of three disease-related CAZymes: β-galactocerebrosidase (GALC), responsible for Krabbe disease; α-mannoside β-1,6-N-acetylglucosaminyltransferase V (MGAT5), involved in cancer; and O-fucosyltransferase 1 (POFUT1), involved in several human diseases such as leukemia and the Dowling-Degos disease.