RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules. In particular, we focus on recognition between RNA and small-molecule binders, on selectivity, and on the expected properties of RNA ligands.

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Models of insulin secretory vesicles from pancreatic beta cells have been created using the cellPACK suite of tools to research, curate, construct and visualise the current state of knowledge. The model integrates experimental information from proteomics, structural biology, cryoelectron microscopy and X-ray tomography, and is used to generate models of mature and immature vesicles. A new method was developed to generate a confidence score that reconciles inconsistencies between three available proteomes using expert annotations of cellular localisation. The models are used to simulate soft X-ray tomograms, allowing quantification of features that are observed in experimental tomograms, and in turn, allowing interpretation of X-ray tomograms at the molecular level.

When biological samples are first exposed to electrons in cryo-electron microcopy (cryo-EM), proteins exhibit a rapid ‘burst’ phase of beam-induced motion that cannot be corrected with software. This lowers the quality of the initial frames, which are the least damaged by the electrons. Hence, they are commonly excluded or down-weighted during data processing, reducing the undamaged signal and the resolution in the reconstruction. By decreasing the cooling rate during sample preparation, either with a cooling-rate gradient or by increasing the freezing temperature, we show that the quality of the initial frames for various protein and virus samples can be recovered. Incorporation of the initial frames in the reconstruction increases the resolution by an amount equivalent to using ~60% more data. Moreover, these frames preserve the high-quality cryo-EM densities of radiation-sensitive residues, which is often damaged or very weak in canonical three-dimensional reconstruction. The improved freezing conditions can be easily achieved using existing devices and enhance the overall quality of cryo-EM structures.

SARS-CoV-2 nucleocapsid (N) protein plays the essential roles in key steps of the viral life cycle, thus representing a top drug target. Functionality of N protein including liquid–liquid phase separation (LLPS) depends on its interaction with nucleic acids. Only the variants with N proteins functional in binding nucleic acids might survive and spread in evolution and indeed, the residues critical for binding nucleic acids are highly conserved. Hydroxychloroquine (HCQ) was shown to prevent the transmission in a large-scale clinical study in Singapore but so far, no specific SARS-CoV-2 protein was experimentally identified to be targeted by HCQ. Here by NMR, we unambiguously decode that HCQ specifically binds NTD and CTD of N protein with Kd of 112.1 and 57.1 μM, respectively to inhibit their interaction with nucleic acid, as well as to disrupt LLPS. Most importantly, HCQ-binding residues are identical in SARS-CoV-2 variants and therefore HCQ is likely effective to different variants. The results not only provide a structural basis for the anti-SARS-CoV-2 activity of HCQ, but also renders HCQ to be the first known drug capable of targeting LLPS. Furthermore, the unique structure of the HCQ-CTD complex suggests a promising strategy for design of better anti-SARS-CoV-2 drugs from HCQ.

Chaperones protect other proteins against misfolding and aggregation, a key requirement for maintaining biological function. Experimental observations of changes in solubility of amyloid proteins in the presence of certain chaperones are discussed here in terms of thermodynamic driving forces. We outline how chaperones can enhance amyloid solubility through the formation of heteromolecular aggregates (co-aggregates) based on the second law of thermodynamics and the flux towards equal chemical potential of each compound in all phases of the system. Higher effective solubility of an amyloid peptide in the presence of chaperone implies that the chemical potential of the peptide is higher in the aggregates formed under these conditions compared to peptide-only aggregates. This must be compensated by a larger reduction in chemical potential of the chaperone in the presence of peptide compared to chaperone alone. The driving force thus relies on the chaperone being very unhappy on its own (high chemical potential), thus gaining more free energy than the amyloid peptide loses upon forming the co-aggregate. The formation of heteromolecular aggregates also involves the kinetic suppression of the formation of homomolecular aggregates. The unhappiness of the chaperone can explain the ability of chaperones to favour an increased population of monomeric client protein even in the absence of external energy input, and with broad client specificity. This perspective opens for a new direction of chaperone research and outlines a set of outstanding questions that aim to provide additional cues for therapeutic development in this area.

Mist is generated by ultrasonic cavitation of water (Fisher Biograde, pH 5.5–6.5) at room temperature (20–25 °C) in open air with nearly constant temperature (22–25 °C) but varying relative humidity (RH; 24–52%) over the course of many months. Water droplets in the mist are initially about 7 μm in diameter at about 50% RH. They are collected, and the concentration of hydrogen peroxide (H2O2) is measured using commercial peroxide test strips and by bromothymol blue oxidation. The quantification method is based on the Fenton chemistry of dye degradation to determine the oxidation capacity of water samples that have been treated by ultrasonication. It is found that the hydrogen peroxide concentration varies nearly linearly with RH over the range studied, reaching a low of 2 parts per million (ppm) at 24% RH and a high of 6 ppm at 52% RH. Some possible public health implications concerning the transmission of respiratory viral infections are suggested for this threefold change in H2O2 concentration with RH.

Modafinil is a mild psychostimulant-like drug enhancing wakefulness, improving attention and developing performance in various cognitive tasks, but its mechanism of action is not completely understood. This is the first combination of amperometry, electrochemical cytometry and mass spectrometry to interrogate the mechanism of action of a drug, here modafinil, at cellular and sub-cellular level. We employed single-cell amperometry (SCA) and intracellular vesicle impact electrochemical cytometry (IVIEC) to investigate the alterations in exocytotic release and vesicular catecholamine storage following modafinil treatment. The SCA results reveal that modafinil slows down the exocytosis process so that, the number of catecholamines released per exocytotic event is enhanced in the modafinil-treated cells. Also, IVIEC results offer an upregulation effect of modafinil on the vesicular catecholamine storage. Mass spectrometry imaging by time-of-flight secondary ion mass spectrometry (ToF-SIMS) illustrates that treatment with modafinil reduces the cylindrical-shaped phosphatidylcholine at the cellular membrane, while the high curvature lipids with conical structures such as phosphatidylethanolamine and phosphatidylinositol are elevated after modafinil treatment. Combining the results obtained by SCA, IVIEC and ToF-SIMS suggests that modafinil-treated cells release a larger portion of their vesicular content at least in part by changing the lipid composition of the cell membrane, suggesting regulation of cognition.

We report the G protein-first mechanism for activation of G protein-coupled receptors (GPCR) for the three closest subtypes of the opioid receptors (OR), μOR, κOR and δOR. We find that they couple to the inactive Gi protein-bound guanosine diphosphate (GDP) prior to agonist binding. The inactive Gi protein forms anchors to the intracellular loops of the inactive apo-μOR, apo-κOR and apo-δOR, inducing opening of the cytoplasmic region to form a pre-activated state that holds Gi protein in place until agonist binds. Then, agonist binds to μOR, κOR and δOR already complexed with Gi protein, to trigger the Gαi to open up the tightly coupled GDP binding site, making GDP accessible for GTP exchange, an essential step for Gi signalling. We show that the agonist alone cannot open the intracellular region of μOR and κOR, requiring Gi protein to open the cytoplasmic region by itself. We consider that this G protein-first mechanism may apply to activation of other Class A GPCRs. However, for δOR, agonist binding can open up the intracellular region to encourage Gi protein recruitment. Thus, activation of Gi protein mediated by δOR favourably may proceed with either ligand-first or G protein-first activation mechanisms.