Inhibition of M. tuberculosis and human ATP synthase by BDQ and TBAJ-587.

Bedaquiline (BDQ), a first-in-class diarylquinoline anti-tuberculosis drug, and its analogue, TBAJ-587, prevent the growth and proliferation of Mycobacterium tuberculosis by inhibiting ATP synthase1,2. However, BDQ also inhibits human ATP synthase3. At present, how these compounds interact with either M. tuberculosis ATP synthase or human ATP synthase is unclear. Here we present cryogenic electron microscopy structures of M. tuberculosis ATP synthase with and without BDQ and TBAJ-587 bound, and human ATP synthase bound to BDQ. The two inhibitors interact with subunit a and the c-ring at the leading site, c-only sites and lagging site in M. tuberculosis ATP synthase, showing that BDQ and TBAJ-587 have similar modes of action. The quinolinyl and dimethylamino units of the compounds make extensive contacts with the protein. The structure of human ATP synthase in complex with BDQ reveals that the BDQ-binding site is similar to that observed for the leading site in M. tuberculosis ATP synthase, and that the quinolinyl unit also interacts extensively with the human enzyme. This study will improve researchers' understanding of the similarities and differences between human ATP synthase and M. tuberculosis ATP synthase in terms of the mode of BDQ binding, and will allow the rational design of novel diarylquinolines as anti-tuberculosis drugs.

An alternative broad-specificity pathway for glycan breakdown in bacteria.

The vast majority of glycosidases characterized to date follow one of the variations of the 'Koshland' mechanisms1 to hydrolyse glycosidic bonds through substitution reactions. Here we describe a large-scale screen of a human gut microbiome metagenomic library using an assay that selectively identifies non-Koshland glycosidase activities2. Using this, we identify a cluster of enzymes with extremely broad substrate specificities and thoroughly characterize these, mechanistically and structurally. These enzymes not only break glycosidic linkages of both α and ß stereochemistry and multiple connectivities, but also cleave substrates that are not hydrolysed by standard glycosidases. These include thioglycosides, such as the glucosinolates from plants, and pseudoglycosidic bonds of pharmaceuticals such as acarbose. This is achieved through a distinct mechanism of hydrolysis that involves oxidation/reduction and elimination/hydration steps, each catalysed by enzyme modules that are in many cases interchangeable between organisms and substrate classes. Homologues of these enzymes occur in both Gram-positive and Gram-negative bacteria associated with the gut microbiome and other body parts, as well as other environments, such as soil and sea. Such alternative step-wise mechanisms appear to constitute largely unrecognized but abundant pathways for glycan degradation as part of the metabolism of carbohydrates in bacteria.

Structure of the human respiratory complex II.

Human complex II is a key protein complex that links two essential energy-producing processes: the tricarboxylic acid cycle and oxidative phosphorylation. Deficiencies due to mutagenesis have been shown to cause mitochondrial disease and some types of cancers. However, the structure of this complex is yet to be resolved, hindering a comprehensive understanding of the functional aspects of this molecular machine. Here, we have determined the structure of human complex II in the presence of ubiquinone at 2.86 Å resolution by cryoelectron microscopy, showing it comprises two water-soluble subunits, SDHA and SDHB, and two membrane-spanning subunits, SDHC and SDHD. This structure allows us to propose a route for electron transfer. In addition, clinically relevant mutations are mapped onto the structure. This mapping provides a molecular understanding to explain why these variants have the potential to produce disease.

3D based generative PROTAC linker design with reinforcement learning.

Proteolysis targeting chimera (PROTAC), has emerged as an effective modality to selectively degrade disease-related proteins by harnessing the ubiquitin-proteasome system. Due to PROTACs' hetero-bifunctional characteristics, in which a linker joins a warhead binding to a protein of interest (POI), conferring specificity and a E3-ligand binding to an E3 ubiquitin ligase, this could trigger the ubiquitination and transportation of POI to the proteasome, followed by degradation. The rational PROTAC linker design is challenging due to its relatively large molecular weight and the complexity of maintaining the binding mode of warhead and E3-ligand in the binding pockets of counterpart. Conventional linker generation method can only generate linkers in either 1D SMILES or 2D graph, without taking into account the information of ternary structures. Here we propose a novel 3D linker generative model PROTAC-INVENT which can not only generate SMILES of PROTAC but also its 3D putative binding conformation coupled with the target protein and the E3 ligase. The model is trained jointly with the RL approach to bias the generation of PROTAC structures toward pre-defined 2D and 3D based properties. Examples were provided to demonstrate the utility of the model for generating reasonable 3D conformation of PROTACs. On the other hand, our results show that the associated workflow for 3D PROTAC conformation generation can also be used as an efficient docking protocol for PROTACs.

Achievements, challenges, and perspectives in the design of polymer binders for advanced lithium-ion batteries.

Energy storage devices with high power and energy density are in demand owing to the rapidly growing population, and lithium-ion batteries (LIBs) are promising rechargeable energy storage devices. However, there are many issues associated with the development of electrode materials with a high theoretical capacity, which need to be addressed before their commercialization. Extensive research has focused on the modification and structural design of electrode materials, which are usually expensive and sophisticated. Besides, polymer binders are pivotal components for maintaining the structural integrity and stability of electrodes in LIBs. Polyvinylidene difluoride (PVDF) is a commercial binder with superior electrochemical stability, but its poor adhesion, insufficient mechanical properties, and low electronic and ionic conductivity hinder its wide application as a high-capacity electrode material. In this review, we highlight the recent progress in developing different polymeric materials (based on natural polymers and synthetic non-conductive and electronically conductive polymers) as binders for the anodes and cathodes in LIBs. The influence of the mechanical, adhesion, and self-healing properties as well as electronic and ionic conductivity of polymers on the capacity, capacity retention, rate performance and cycling life of batteries is discussed. Firstly, we analyze the failure mechanisms of binders based on the operation principle of lithium-ion batteries, introducing two models of "interface failure" and "degradation failure". More importantly, we propose several binder parameters applicable to most lithium-ion batteries and systematically consider and summarize the relationships between the chemical structure and properties of the binder at the molecular level. Subsequently, we select silicon and sulfur active electrode materials as examples to discuss the design principles of the binder from a molecular structure point of view. Finally, we present our perspectives on the development directions of binders for next-generation high-energy-density lithium-ion batteries. We hope that this review will guide researchers in the further design of novel efficient binders for lithium-ion batteries at the molecular level, especially for high energy density electrode materials.

Halogen-bonding boosting the high performance X-ray imaging of organic scintillators.

Organic scintillators with efficient X-ray excited luminescence are essential for medical diagnostics and security screening. However, achieving excellent organic scintillation materials is challenging due to low X-ray absorption coefficients and inferior radioluminescence (RL) intensity. Herein, supramolecular interactions are incorporated, particularly halogen bonding, into organic scintillators to enhance their radioluminescence properties. By introducing heavy atoms (X = Cl, Br, I) into 9,10-bis(4-pyridyl)anthracene (BPA), the formation of halogen bonding (BPA-X) enhances their X-ray absorption coefficient and restricts the molecular vibration and rotation, which boosts their RL intensity. The RL intensity of BPA-Cl and BPA-Br fluorochromes increased by over 2 and 6.3 times compared to BPA, respectively. Especially, BPA-Br exhibits an ultrafast decay time of 8.25 ns and low detection limits of 25.95 ± 2.49 nGy s-1. The flexible film constructed with BPA-Br exhibited excellent X-ray imaging capabilities. Furthermore, this approach is also applicable to organic phosphors. The formation of halogen bonding in bromophenyl-methylpyridinium iodide (PYI) led to a fourfold increase in RL intensity compared to bromophenyl-methyl-pyridinium (PY). It suggests that halogen bonding serves as a promising and effective molecular design strategy for the development of high-performance organic scintillator materials, presenting new opportunities for their applications in radiology and security screening.

An extensive benchmark study on biomedical text generation and mining with ChatGPT.

MOTIVATION: In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain. RESULTS: To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5. AVAILABILITY AND IMPLEMENTATION: All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.

Mechanochemical Synthesis of Cuprous Complexes for X-ray Scintillation and Imaging.

Cuprous complex scintillators show promise for X-ray detection with abundant raw materials, diverse luminescent mechanisms, and adjustable structures. However, their synthesis typically requires a significant amount of organic solvents, which conflict with green chemistry principles. Herein, we present the synthesis of two high-performance cuprous complex scintillators using a simple mechanochemical method for the first time, namely [CuI(PPh3)2R] (R = 4-phenylpyridine hydroiodide (PH, Cu-1) and 4-(4-bromophenyl)pyridine hydroiodide (PH-Br, Cu-2). Both materials demonstrated remarkable scintillation performances, exhibiting radioluminescence (RL) intensities 1.52 times (Cu-1) and 2.52 times (Cu-2) greater than those of Bi4Ge3O12 (BGO), respectively. Compared to Cu-1, the enhanced RL performance of Cu-2 can be ascribed to its elevated quantum yield of 51.54%, significantly surpassing that of Cu-1 at 37.75%. This excellent luminescent performance is derived from the introduction of PH-Br, providing a more diverse array of intermolecular interactions that effectively constrain molecular vibration and rotation, further suppressing the nonradiative transition process. Furthermore, Cu-2 powder can be prepared into scintillator film with excellent X-ray imaging capabilities. This work establishes a pathway for the rapid, eco-friendly, and cost-effective synthesis of high-performance cuprous complex scintillators.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning.

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

The impact of blood lactic acid levels on retinopathy of prematurity morbidity.

BACKGROUND: Retinopathy of prematurity (ROP) is a common disease in premature infants. In recent years, most researchers have used lactic acid as poor prognosis marker in premature infants. This study aims to explore investigate the impact of blood lactic acid levels on ROP. METHODS: A retrospective case-control study was conducted, and infants with severe ROP born with birth weight (BW) ≤ 1500 g and gestational age (GA) ≤ 32 weeks were enrolled from November 2016 to November 2021. Infants without any stage ROP were included as controls and were matched with ROP infants (1:2) by GA and BW. All selected preterm infants were tested for heel terminal trace blood gas analysis within two weeks of life. Changes in blood lactic acid levels in the two groups were compared and analyzed by using multivariate logistic regression analysis. Sensitivity and specificity were analyzed by receiver operating characteristic (ROC) curve. RESULTS: There were 79 infants in ROP group, and 158 infants in control group. The levels of blood lactic acid were significantly higher in the ROP group on days 1, 3, 5, and 7 compared with control group (all p < 0.05). The blood lactic acid levels on day 5 was an independent risk factor for ROP (p = 0.017). The area under the curve (AUC), sensitivity and specificity were highest on day 5 (AUC 0.716, sensitivity 77.2% and specificity 62.0%, respectively, p < 0.001), and higher on days 1, 3, and 7. CONCLUSION: A high blood lactic acid level in the first seven days of life may be associated with increases ROP occurrence in very preterm infants, and suggest blood lactic acid level may impact the occurrence of ROP.

Flexible complementary circuits operating at sub-0.5 V via hybrid organic-inorganic electrolyte-gated transistors.

Electrolyte-gated transistors (EGTs) hold great promise for next-generation printed logic circuitry, biocompatible integrated sensors, and neuromorphic devices. However, EGT-based complementary circuits with high voltage gain and ultralow driving voltage (<0.5 V) are currently unrealized, because achieving balanced electrical output for both the p- and n-type EGT components has not been possible with current materials. Here we report high-performance EGT complementary circuits containing p-type organic electrochemical transistors (OECTs) fabricated with an ion-permeable organic semiconducting polymer (DPP-g2T) and an n-type electrical double-layer transistor (EDLT) fabricated with an ion-impermeable inorganic indium-gallium-zinc oxide (IGZO) semiconductor. Adjusting the IGZO composition enables tunable EDLT output which, for In:Ga:Zn = 10:1:1 at%, balances that of the DPP-g2T OECT. The resulting hybrid electrolyte-gated inverter (HCIN) achieves ultrahigh voltage gains (>110) under a supply voltage of only 0.7 V. Furthermore, NAND and NOR logic circuits on both rigid and flexible substrates are realized, enabling not only excellent logic response with driving voltages as low as 0.2 V but also impressive mechanical flexibility down to 1-mm bending radii. Finally, the HCIN was applied in electrooculographic (EOG) signal monitoring for recording eye movement, which is critical for the development of wearable medical sensors and also interfaces for human-computer interaction; the high voltage amplification of the present HCIN enables EOG signal amplification and monitoring in which a small ∼1.5 mV signal is amplified to ∼30 mV.

Novel Semiconductive Ternary Hybrid Heterostructures for Artificial Optoelectronic Synapses.

Synaptic devices that mimic biological synapses are considered as promising candidates for brain-inspired devices, offering the functionalities in neuromorphic computing. However, modulation of emerging optoelectronic synaptic devices has rarely been reported. Herein, a semiconductive ternary hybrid heterostructure is prepared with a D-D'-A configuration by introducing polyoxometalate (POM) as an additional electroactive donor (D') into a metalloviologen-based D-A framework. The obtained material features an unprecedented porous 8-connected bcu-net that accommodates nanoscale [α-SiW12 O40 ]4- counterions, displaying uncommon optoelectronic responses. Besides, the fabricated synaptic device based on this material can achieve dual-modulation of synaptic plasticity due to the synergetic effect of electron reservoir POM and photoinduced electron transfer. And it can successfully simulate learning and memory processes similar to those in biological systems. The result provides a facile and effective strategy to customize multi-modality artificial synapses in the field of crystal engineering, which opens a new direction for developing high-performance neuromorphic devices.

Highly stretchable organic electrochemical transistors with strain-resistant performance.

Realizing fully stretchable electronic materials is central to advancing new types of mechanically agile and skin-integrable optoelectronic device technologies. Here we demonstrate a materials design concept combining an organic semiconductor film with a honeycomb porous structure with biaxially prestretched platform that enables high-performance organic electrochemical transistors with a charge transport stability over 30-140% tensional strain, limited only by metal contact fatigue. The prestretched honeycomb semiconductor channel of donor-acceptor polymer poly(2,5-bis(2-octyldodecyl)-3,6-di(thiophen-2-yl)-2,5-diketo-pyrrolopyrrole-alt-2,5-bis(3-triethyleneglycoloxy-thiophen-2-yl) exhibits high ion uptake and completely stable electrochemical and mechanical properties over 1,500 redox cycles with 104 stretching cycles under 30% strain. Invariant electrocardiogram recording cycles and synapse responses under varying strains, along with mechanical finite element analysis, underscore that the present stretchable organic electrochemical transistor design strategy is suitable for diverse applications requiring stable signal output under deformation with low power dissipation and mechanical robustness.

Branched montbretin A mimics allow derivatisation and potent amylase inhibition.

Mimics of the complex flavonol glycoside montbretin A in which a flavonol moiety is coupled to a caffeic acid via partially peptidic linkers have proved to be potent inhibitors of human pancreatic alpha-amylase with potential as therapeutics for control of blood glucose levels. After exploring optimal linker length, a synthetic route to a version with a branched linker was devised based on the structure of the enzyme/inhibitor complex. The resultant branched inhibitors were shown to retain nanomolar potency even when decorated with polymers as a means of modifying solubility. Similar improvements, along with nanomolar affinity, could also be achieved through conjugation to cyclodextrins which have the potential to bind to starch binding sites found on the surface of human amylase. Incorporation of a conjugatable branch into this unusual pharmacophore thereby affords considerable flexibility for further modifications to improve pharmacokinetic behaviour or as a site for attachment of capture tags or fluorophores.

Tree-Invent: A Novel Multipurpose Molecular Generative Model Constrained with a Topological Tree.

De novo molecular design plays an important role in drug discovery. Here, a novel generative model, Tree-Invent, was proposed to integrate topological constraints in the generation of a molecular graph. In this model, a molecular graph is represented as a topological tree in which a ring system, a nonring atom, and a chemical bond are regarded as the ring node, single node, and edge, respectively. The molecule generation is driven by three independent submodels for carrying out operations of node addition, ring generation, and node connection. One unique feature of the generative model is that the topological tree structure can be specified as a constraint for structure generation, which provides more precise control of structure generation. Combined with reinforcement learning, the Tree-Invent model could efficiently explore targeted chemical space. Moreover, the Tree-Invent model is flexible enough to be used in versatile molecule design settings such as scaffold decoration, scaffold hopping, and linker generation.

Enhanced transverse magneto-optical Kerr effect using ferromagnetic metal perforated with nanopore arrays.

Enhancement of transverse magneto-optical Kerr effects (T-MOKEs) based on surface plasmon resonance has attracted wide attention because of their high sensing performance. Most studies, however, mainly focus on prism-based magnetoplasmonic structures or architectures that incorporate a noble metal lattice and ferromagnetic layer, hindering device fabrication, miniaturization, and integration into a microfluidic sensing configuration. Herein, we propose a single Co6Ag94 ferromagnetic-metal layer perforated with a square nanopore array system with a pronounced T-MOKE magnitude 51 times higher than that of a smooth film, and with potential to detect gaseous analytes. The proposed system is sufficient to detect gaseous media for early environmental monitoring and enables an accessible pathway for magneto-optical sensor design.

Novel Robotic Arm Working-Area AI Protection System.

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.

Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications.

This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater's power consumption and the hot-pressing furnace's fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.

Constructing Molybdenum Phosphide@Cobalt Phosphide Heterostructure Nanoarrays on Nickel Foam as a Bifunctional Electrocatalyst for Enhanced Overall Water Splitting.

The construction of multi-level heterostructure materials is an effective way to further the catalytic activity of catalysts. Here, we assembled self-supporting MoS2@Co precursor nanoarrays on the support of nickel foam by coupling the hydrothermal method and electrostatic adsorption method, followed by a low-temperature phosphating strategy to obtain Mo4P3@CoP/NF electrode materials. The construction of the Mo4P3@CoP heterojunction can lead to electron transfer from the Mo4P3 phase to the CoP phase at the phase interface region, thereby optimizing the charge structure of the active sites. Not only that, the introduction of Mo4P3 will make water molecules preferentially adsorb on its surface, which will help to reduce the water molecule decomposition energy barrier of the Mo4P3@CoP heterojunction. Subsequently, H* overflowed to the surface of CoP to generate H2 molecules, which finally showed a lower water molecule decomposition energy barrier and better intermediate adsorption energy. Based on this, the material shows excellent HER/OER dual-functional catalytic performance under alkaline conditions. It only needs 72 mV and 238 mV to reach 10 mA/cm2 for HER and OER, respectively. Meanwhile, in a two-electrode system, only 1.54 V is needed to reach 10 mA/cm2, which is even better than the commercial RuO2/NF||Pt/C/NF electrode pair. In addition, the unique self-supporting structure design ensures unimpeded electron transmission between the loaded nanoarray and the conductive substrate. The loose porous surface design is not only conducive to the full exposure of more catalytic sites on the surface but also facilitates the smooth escape of gas after production so as to improve the utilization rate of active sites. This work has important guiding significance for the design and development of high-performance bifunctional electrolytic water catalysts.

De Novo Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions.

In recent years, molecular deep generative models have attracted much attention for its application in de novo drug design. The data-driven molecular deep generative model approximates the high dimensional distribution of the chemical space through learning from a large number of molecular structural data. So far, most of the molecular generative models rely on purely 2D ligand information in structure generation. Here, we propose a novel molecular deep generative model which adopts a recurrent neural network architecture coupled with a ligand-protein interaction fingerprint as constraints. The fingerprint was constructed on ligand docking poses and represents the 3D binding mode of ligands in the protein pocket. In the current work, generative models constrained with interaction fingerprints were trained and compared with normal RNN models. It has been shown that models trained with constraints of ligand-protein interaction fingerprint have a clear tendency to generating compounds maintaining similar binding modes. Our results demonstrate the potential application of the interaction fingerprint-constrained generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.