While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.

Structure prediction of protein–ligand complexes, called protein–ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein–ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein–ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

Polymyxins are the last-line antibiotics used to treat Gram-negative pathogens. Thus, the discovery and biochemical characterization of the resistance genes against polymyxins are urgently needed for diagnosis, treatment, and novel antibiotic design. Herein, we report novel polymyxin-resistance genes identified from sediment and seawater microbiome. Despite their low sequence identity against the known pmrE and pmrF, they show in vitro activities in UDP-glucose oxidation and l-Ara4N transfer to undecaprenyl phosphate, respectively, which occur as the part of lipid A modification that leads to polymyxin resistance. The expression of pmrE and pmrF also showed substantially high MICs in the presence of vanadate ions, indicating that they constitute polymyxin resistomes.

Polymer cubosomes (PCs) are bicontinuous mesoporous colloidal particles that feature high surface areas and an extremely ordered crystalline pore network. PCs have attracted tremendous attention because of their potential applications in many fields. Herein, we obtained new microparticles with carbonaceous reticulated networks via templated synthesis using PCs as templates. The water-channel networks of the PCs were translated into a carbonaceous skeletal cubic structure. Carbon precursors were polymerized inside the water-channel networks of the PCs under acidic conditions without collapsing the internal crystalline mesophases. The carbonaceous interconnected networks created by the templated synthesis exhibited cubic crystalline skeletal networks similar to those of the PCs. These cubic-ordered mesoporous carbon (cOMC) microparticles exhibited several properties in electrochemical experiments. In addition, the nanoscopic structures and surfaces of these microparticles sustain electrochemically perturbing environments, and thus retain more than 90% capacitance after 1000 charge–discharge cycles.

Intrinsically stretchable organic light-emitting diodes (ISOLEDs) are becoming essential components of wearable electronics. However, the efficiencies of ISOLEDs have been highly inferior compared with their rigid counterparts, which is due to the lack of ideal stretchable electrode materials that can overcome the poor charge injection at 1D metallic nanowire/organic interfaces. Herein, highly efficient ISOLEDs that use graphene-based 2D-contact stretchable electrodes (TCSEs) that incorporate a graphene layer on top of embedded metallic nanowires are demonstrated. The graphene layer modifies the work function, promotes charge spreading, and impedes inward diffusion of oxygen and moisture. The work function (WF) of 3.57 eV is achieved by forming a strong interfacial dipole after deposition of a newly designed conjugated polyelectrolyte with crown ether and anionic sulfonate groups on TCSE; this is the lowest value ever reported among ISOLEDs, which overcomes the existing problem of very poor electron injection in ISOLEDs. Subsequent pressure-controlled lamination yields a highly efficient fluorescent ISOLED with an unprecedently high current efficiency of 20.3 cd A−1, which even exceeds that of an otherwise-identical rigid counterpart. Lastly, a 3 inch five-by-five passive matrix ISOLED is demonstrated using convex stretching. This work can provide a rational protocol for designing intrinsically stretchable high-efficiency optoelectronic devices with favorable interfacial electronic structures.

A face-to-face stacking of aromatic rings is an effective non-covalent strategy to build functional architectures, as elegantly exemplified with protein folding and polynucleotide assembly. However, weak, non-directional, and context-sensitive van der Waals forces pose a significant challenge if one wishes to construct well-organized π-stacks outside the confines of the biological matrix. To meet this design challenge, we have devised a rigid polycyclic template to create a non-collapsible void between two parallel oriented π-faces. In solution, these shape-persistent aromatic clips self-dimerize to form quadruple π-stacks, the thermodynamic stability of which is enhanced by self-complementary N–H···N hydrogen bonds, and finely regulated by the regioisomerism of the π-canopy unit. With assistance from sufficient electrostatic polarization of the π-surface and bifurcated hydrogen bonds, a small polyheterocyclic guest can effectively compete against the self-dimerization of the host to afford a triple π-stack inclusion complex. A combination of solution spectroscopic, X-ray crystallographic, and computational studies aided a detailed understanding of this cooperative vs competitive process to afford layered aromatics with extraordinary structural regularity and fidelity.

In this study, an aqueous nonlinear synaptic element showing plasticity behavior is developed, which is based on the chemical processes in an ionic diode. The device is simple, fully ionic, and easily configurable, requiring only two terminals—for input and output—similar to biological synapses. The key processes realizing the plasticity features are chemical precipitation and dissolution, which occur at forward- or reverse-biased ionic diode junctions in appropriate reservoir electrolytes. Given that the precipitate acts as a physical barrier in the circuit, the above processes change the diode conductivity, which can be interpreted as adjusting “synaptic weight” of the system. By varying the operating conditions, we first demonstrate the four types of plasticity that can be found in biological system: long-term potentiation/depression and short-term potentiation/depression. The plasticity of the proposed iontronic device has characteristics similar to those of neural synapses. To demonstrate its potential use in comparatively complex information processing, we develop a precipitation-based iontronic synapse (PIS) capable of both potentiation and depression. Finally, we show that the postsynaptic signals from the multiple excitatory or inhibitory PISs can be integrated into the total “dendritic” current, which is a function of time and input history, as in actual hippocampal neural circuits.

Understanding how protein–protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect—dehydration penalty—must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody–protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

Although numerous thermoelectric materials based on single-walled carbon nanotubes (SWNTs) and organic semiconductors have been reported during the past decade, the correlation between energy levels of organic semiconductors and thermoelectric performances of their hybrids is still ambiguous. In this study, we demonstrate that simultaneous modulation of the bandgap and highest occupied molecular orbital levels in organic small molecules (OSMs) largely improves the Seebeck coefficient and thus maximizes the figure of merit (ZT) of SWNT/OSM hybrids. SWNT/CzS with an enlarged bandgap and reduced barrier energy exhibited a synergistic increment in the Seebeck coefficient (108.7 μV K–1) and power factor (337.2 μW m–1 K–2), with the best ZT of 0.058 at room temperature among dopant-free carbon nanotube-hybridized thermoelectrics. The efficient charge carrier transport and reduced thermal conductivity of SWNT/CzS provided enhanced thermoelectric performance. Our strategy based on energy level modulation could be broadly applied for performance enhancement of organic and hybrid thermoelectric materials.

Direct identification of the proteins targeted by small molecules can provide clues for disease diagnosis, prevention, and drug development. Despite concentrated attempts, there are still technical limitations associated with the elucidation of direct interactors. Herein, we report a target-ID system called proximity-based compound-binding protein identification (PROCID), which combines our direct analysis workflow of proximity-labeled proteins (Spot-ID) with the HaloTag system to efficiently identify the dynamic proteomic landscape of drug-binding proteins. We successfully identified well-known dasatinib-binding proteins (ABL1, ABL2) and confirmed the unapproved dasatinib-binding kinases (e.g., BTK and CSK) in a live chronic myeloid leukemia cell line. PROCID also identified the DNA helicase protein SMARCA2 as a dasatinib-binding protein, and the ATPase domain was confirmed to be the binding site of dasatinib using a proximity ligation assay (PLA) and in cellulo biotinylation assay. PROCID thus provides a robust method to identify unknown drug-interacting proteins in live cells that expedites the mode of action of the drug.