AptaDB: a comprehensive database integrating aptamer-target interactions.

Aptamers have emerged as research hotspots of the next generation due to excellent performance benefits and application potentials in pharmacology, medicine, and analytical chemistry. Despite the numerous aptamer investigations, the lack of comprehensive data integration has hindered the development of computational methods for aptamers and the reuse of aptamers. A public access database named AptaDB, derived from experimentally validated data manually collected from the literature, was hence developed, integrating comprehensive aptamer-related data, which include six key components: (i) experimentally validated aptamer-target interaction information, (ii) aptamer property information, (iii) structure information of aptamer, (iv) target information, (v) experimental activity information, and (vi) algorithmically calculated similar aptamers. AptaDB currently contains 1350 experimentally validated aptamer-target interactions, 1230 binding affinity constants, 1293 aptamer sequences, and more. Compared to other aptamer databases, it contains twice the number of entries found in available databases. The collection and integration of the above information categories is unique among available aptamer databases and provides a user-friendly interface. AptaDB will also be continuously updated as aptamer research evolves. We expect that AptaDB will become a powerful source for aptamer rational design and a valuable tool for aptamer screening in the future. For access to AptaDB, please visit http://lmmd.ecust.edu.cn/aptadb/.

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering.

Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational methods have been developed to predict drug metabolites, but most of them suffer from two main obstacles: the lack of model generalization due to restrictions on metabolic transformation rules or specific enzyme families, and high rate of false-positive predictions. Here, we presented MetaPredictor, a rule-free, end-to-end and prompt-based method to predict possible human metabolites of small molecules including drugs as a sequence translation problem. We innovatively introduced prompt engineering into deep language models to enrich domain knowledge and guide decision-making. The results showed that using prompts that specify the sites of metabolism (SoMs) can steer the model to propose more accurate metabolite predictions, achieving a 30.4% increase in recall and a 16.8% reduction in false positives over the baseline model. The transfer learning strategy was also utilized to tackle the limited availability of metabolic data. For the adaptation to automatic or non-expert prediction, MetaPredictor was designed as a two-stage schema consisting of automatic identification of SoMs followed by metabolite prediction. Compared to four available drug metabolite prediction tools, our method showed comparable performance on the major enzyme families and better generalization that could additionally identify metabolites catalyzed by less common enzymes. The results indicated that MetaPredictor could provide a more comprehensive and accurate prediction of drug metabolism through the effective combination of transfer learning and prompt-based learning strategies.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

Large Polarization Near 50 µC/cm2 in a Single Unit Cell Layer SrTiO3.

The generally nonpolar SrTiO3 has attracted more attention recently because of its possibly induced novel polar states and related paraelectric-ferroelectric phase transitions. By using controlled pulsed laser deposition, high-quality, ultrathin, and strained SrTiO3 layers were obtained. Here, transmission electron microscopy and theoretical simulations have unveiled highly polar states in SrTiO3 films even down to one unit cell at room temperature, which were stabilized in the PbTiO3/SrTiO3/PbTiO3 sandwich structures by in-plane tensile strain and interfacial coupling, as evidenced by large tetragonality (∼1.05), notable polar ion displacement (0.019 nm), and thus ultrahigh spontaneous polarization (up to ∼50 µC/cm2). These values are nearly comparable to those of the strong ferroelectrics as the PbZrxTi1-xO3 family. Our findings provide an effective and practical approach for integrating large strain states into oxide films and inducing polarization in nonpolar materials, which may broaden the functionality of nonpolar oxides and pave the way for the discovery of new electronic materials.

Hydrogen-Bonded Mesoporous Frameworks with Tunable Pore Sizes and Architectures from Nanocluster Assembly Units.

Construction of mesoporous frameworks by noncovalent bonding still remains a great challenge. Here, we report a micelle-directed nanocluster modular self-assembly approach to synthesize a novel type of two-dimensional (2-D) hydrogen-bonded mesoporous frameworks (HMFs) for the first time based on nanoscale cluster units (1.0-3.0 nm in size). In this 2-D structure, a mesoporous cluster plate with ∼100 nm in thickness and several micrometers in size can be stably formed into uniform hexagonal arrays. Meanwhile, such a porous plate consists of several (3-4) dozens of layers of ultrathin mesoporous cluster nanosheets. The size of the mesopores can be precisely controlled from 11.6 to 18.5 nm by utilizing the amphiphilic diblock copolymer micelles with tunable block lengths. Additionally, the pore configuration of the HMFs can be changed from spherical to cylindrical by manipulating the concentration of the micelles. As a general approach, various new HMFs have been achieved successfully via a modular self-assembly of nanoclusters with switchable configurations (nanoring, Keggin-type, and cubane-like) and components (titanium-oxo, polyoxometalate, and organometallic clusters). As a demonstration, the titanium-oxo cluster-based HMFs show efficient photocatalytic activity for hydrogen evolution (3.6 mmol g-1h-1), with a conversion rate about 2 times higher than that of the unassembled titanium-oxo clusters (1.5 mmol g-1h-1). This demonstrates that HMFs exhibited enhanced photocatalytic activity compared with unassembled titanium-oxo clusters units.

Inferring Incidence of Unreported SARS-CoV-2 Infections Using Seroprevalence of Open Reading Frame 8 Antigen, Hong Kong.

We tested seroprevalence of open reading frame 8 antigens to infer the number of unrecognized SARS-CoV-2 Omicron infections in Hong Kong during 2022. We estimate 33.6% of the population was infected, 72.1% asymptomatically. Surveillance and control activities during large-scale outbreaks should account for potentially substantial undercounts.

MEG3 in hematologic malignancies: from the role of disease biomarker to therapeutic target.

Maternally expressed gene 3 ( MEG3 ) is a noncoding RNA that is known as a tumor suppressor in solid cancers. Recently, a line of studies has emphasized its potential role in hematological malignancies in terms of tumorigenesis, metastasis, and drug resistance. Similar to solid cancers, MEG3 can regulate various cancer hallmarks via sponging miRNA, transcriptional, or posttranslational regulation mechanisms, but may regulate different key elements. In contrast with solid cancers, in some subtypes of leukemia, MEG3 has been found to be upregulated and oncogenic. In this review, we systematically describe the role and underlying mechanisms of MEG3 in multiple types of hematological malignancies. Particularly, we highlight the role of MEG3 in drug resistance and as a novel therapeutic target.

Long range topography by dispersion unmatched spectral-domain interferometry based on virtually imaged phased array modes.

We propose to realize a long range topography by dispersion unmatched spectral-domain interferometry based on virtually imaged phased array (VIPA) modes. By filtering the continuous spectrum of a supercontinuum source through a side-entrance Fabry-Perot etalon configured at two input angles, two groups of VIPA modes are generated. A method based on unmatched dispersion is proposed for non-aliasing reconstruction of the true depth from the interference spectrum under-sampled at two groups of VIPA modes. With the high spectral resolution provided by the VIPA modes instead of the grating-based spectrometer, only a 10 dB falloff in sensitivity over a range of 10 mm was demonstrated. The feasibility of the proposed method was confirmed by topography of a sample of gauge blocks and a model of three-dimensional (3D) printed tooth. The occlusal surface of the tooth model was further quantitatively evaluated, demonstrating its potential application in long range 3D topography.

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods.

Skin Corrosion/Irritation (Corr./Irrit.) has long been a health hazard in the Globally Harmonized System (GHS). Several in silico models have been built to predict Skin Corr./Irrit. as an alternative to the increasingly restricted animal testing. However, current studies are limited by data amount/quality and model availability. To address these issues, we compiled a traceable consensus GHS data set comprising 731 Corr., 1283 Irrit., and 1205 negative (Neg.) samples from 6 governmental databases and 2 external data sets. Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. For 10-fold cross-validation, the best Corr. vs Neg. classifier achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 97.1%, while the best Irrit. vs Neg. classifier achieved an AUC of 84.7%. Compared with existing in silico tools on external validation, our Attentive FP classifiers showed the highest metrics on Corr. vs Neg. and the second highest accuracy on Irrit. vs Neg. The SHapley Additive exPlanation approach was further applied to figure out important molecular features, and the attention weights were visualized to perform interpretable prediction. Structural alerts associated with Skin Corr./Irrit. were also identified. The interpretable Attentive FP classifiers were integrated into the software AttentiveSkin at https://github.com/BeeBeeWong/AttentiveSkin. The conventional ML classifiers are also provided on our platform admetSAR at http://lmmd.ecust.edu.cn/admetsar2/. Considering the data deficiency and the limited model availability of Skin Corr./Irrit., we believe that our data set and models could facilitate chemical safety assessment and relevant studies.

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory.

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

Cross-neutralizing antibody against emerging Omicron subvariants of SARS-CoV-2 in infection-naïve individuals with homologous BNT162b2 or BNT162b2(WT + BA.4/5) bivalent booster vaccination.

Since the emergence of SARS-CoV-2, different variants and subvariants successively emerged to dominate global virus circulation as a result of immune evasion, replication fitness or both. COVID-19 vaccines continue to be updated in response to the emergence of antigenically divergent viruses, the first being the bivalent RNA vaccines that encodes for both the Wuhan-like and Omicron BA.5 subvariant spike proteins. Repeated infections and vaccine breakthrough infections have led to complex immune landscapes in populations making it increasingly difficult to assess the intrinsic neutralizing antibody responses elicited by the vaccines. Hong Kong's intensive COVID-19 containment policy through 2020-2021 permitted us to identify sera from a small number of infection-naïve individuals who received 3 doses of the RNA BNT162b2 vaccine encoding the Wuhan-like spike (WT) and were boosted with a fourth dose of the WT vaccine or the bivalent WT and BA.4/5 spike (WT + BA.4/5). While neutralizing antibody to wild-type virus was comparable in both vaccine groups, BNT162b2 (WT + BA.4/BA.5) bivalent vaccine elicited significantly higher plaque neutralizing antibodies to Omicron subvariants BA.5, XBB.1.5, XBB.1.16, XBB.1.9.1, XBB.2.3.2, EG.5.1, HK.3, BA.2.86 and JN.1, compared to BNT162b2 monovalent vaccine. The single amino acid substitution that differentiates the spike of JN.1 from BA.2.86 resulted in a profound antigenic change.

Streptomyces albipurpureus sp. nov., a novel actinobacterium with antimicrobial activity isolated from the rhizospheric soil of Fritillaria cirrhosa D. Don.

A novel actinobacterium, designated strain CWNU-1T, was isolated from the rhizospheric soil of Fritillaria cirrhosa D. Don and examined using a polyphasic taxonomic approach. The organism developed pale blue aerial mycelia that was simply branched and terminated in open or closed coils of three or more volutions on International Streptomyces Project 3 agar. Spores were ellipsoidal to cylindrical with wrinkled surfaces. The strain showed high 16S rRNA gene sequence similarity to Streptomyces kurssanovii NBRC 13192T (98.8 %), Streptomyces xantholiticus NBRC 13354T (98.7 %) and Streptomyces peucetius JCM 9920T (98.6 %). The phylogenetic result based on 16S rRNA gene and genome sequences clearly demonstrated that strain CWNU-1T formed an independent phylogenetic lineage. On the basis of orthologous average nucleotide identity, CWNU-1T was most closely related to Streptomyces inusitatus NBRC 13601T with 79.3 % identity. The results of the digital DNA-DNA hybridization analysis also indicated low levels of relatedness with other species, as the highest value was observed with S. inusitatus NBRC 13601T (25.3 %). With reference to phenotypic characteristics, phylogenetic data, orthologous average nucleotide identity and digital DNA-DNA hybridization results, strain CWNU-1T was readily distinguished from its most closely related strains and classified as representing a novel species, for which the name Streptomyces albipurpureus sp. nov. is proposed. The type strain is CWNU-1T (=CGMCC 4.7758T=MCCC 1K07402T=JCM 35391T).

Silver/Antimony-Base Multifunctional Double Perovskite with H/F Substitution Enhance Properties.

Two-dimensional double perovskites have experienced rapid development due to their outstanding optoelectronic properties and diverse structural characteristics. However, the synthesis of high-performance multifunctional compounds and the regulation of their properties still lack relevant examples. Herein, we synthesized two multifunctional compounds, (C6H14N)4AgSbBr8 (1) and (F2-C6H12N)4AgSbBr8 (2), which exhibit high solid-state phase transition temperature, bistable dielectric constant switching, second harmonic generation (SHG), and bright emission. Through H/F substitution, the transition temperature increases and achieves a smaller band gap attributed to reduced interlayer spacing. Furthermore, we investigated the broad emission mechanism of the compounds through first-principles calculation and variable-temperature fluorescence, confirming the presence of the STE1 emission. Our work provides insight into the further development of multifunctional compounds and chemical modification that enhances compound properties.

High-Temperature Phase Transition with Switchable Dielectric Behavior and Significant Photoluminescence Changes in a Zero-Dimensional Hybrid SbBr6 Perovskite.

In the past decade, metal halide materials have been favored by many researchers because of their excellent physical and chemical properties under thermal, electrical, and light stimuli, such as ferroelectricity, dielectric, nonlinearity, fluorescence, and semiconductors, greatly promoting their application in optoelectronic devices. In this study, we successfully constructed an unleaded organic-inorganic hybrid perovskite crystal: [Cl-C6H4-(CH2)2NH3]3SbBr6 (1), which underwent a high-temperature reversible phase transition near Tp = 368 K. The phase transition behavior of 1 was characterized by differential scanning calorimetry, accompanied by a thermal hysteresis of 6 K. In addition, variable-temperature Raman spectroscopy analysis and PXRD further verified the sensitivity of 1 to temperature and the phase transition from low symmetry to high symmetry. Temperature-dependent dielectric testing shows that 1 can be a sensitive switching dielectric constant switching material. Remarkably, 1 exhibits strong photoluminescence emission with a wavelength of 478 nm and a narrow band gap of 2.7 eV in semiconductors. As the temperature increases and decreases, fluorescence undergoes significant changes, especially near Tc, which further confirms the reversible phase transition of 1. All of these findings provide new avenues for designing and assembling new phase change materials with high Tp and photoluminescence properties.

Unraveling the Ligand-Binding Sites of CYP3A4 by Molecular Dynamics Simulations with Solvent Probes.

Cytochrome P450 3A4 (CYP3A4) is one of the most important drug-metabolizing enzymes in the human body and is well known for its complicated, atypical kinetic characteristics. The existence of multiple ligand-binding sites in CYP3A4 has been widely recognized as being capable of interfering with the active pocket through allosteric effects. The identification of ligand-binding sites other than the canonical active site above the heme is especially important for understanding the atypical kinetic characteristics of CYP3A4 and the intriguing association between the ligand and the receptor. In this study, we first employed mixed-solvent molecular dynamics (MixMD) simulations coupled with the online computational predictive tools to explore potential ligand-binding sites in CYP3A4. The MixMD approach demonstrates better performance in dealing with the receptor flexibility compared with other computational tools. From the sites identified by MixMD, we then picked out multiple sites for further exploration using ensemble docking and conventional molecular dynamics (cMD) simulations. Our results indicate that three extra sites are suitable for ligand binding in CYP3A4, including one experimentally confirmed site and two novel sites.

Network-Based Methods and Their Applications in Drug Discovery.

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

Up-regulation of lncRNA WT1-AS ameliorates Aß-stimulated neuronal injury through modulation of miR-186-5p/CCND2 axis in Alzheimer's disease.

As a common neurodegenerative disorder, Alzheimer's disease (AD) seriously threatens human life. Long non-coding RNAs (lncRNAs) exhibit essential functions in AD development. Nevertheless, the detailed effects and possible mechanisms of lncRNA Wilms tumor 1 Antisense RNA (WT1-AS) in AD are largely unknown. In our studies, a total of 30 serum samples from AD patients were collected, and WT1-AS expressions were detected through qRT-PCR analysis. Additionally, an in vitro AD model was constructed by treating Aß1-42 in human neuroblastoma cells. Functional assays were implemented to assess the impacts of WT1-AS on Aß1-42-stimulated human neuroblastoma cell proliferation together with apoptosis. Moreover, relationship of WT1-AS, microRNA (miR)-186-5p as well as cyclin D2 (CCND2) could be predicted through bioinformatics tools as well as proved via dual-luciferase reporter experiments. Our results showed that WT1-AS together with CCND2 were low-expressed, while miR-186-5p presented high expression in AD serum samples together with Aß1-42-stimulated human neuroblastoma cells. WT1-AS over-expression or miR-186-5p depletion notably promoted the proliferation, reduced the apoptosis, and decreased the p-Tau protein expressions of human neuroblastoma cells induced with Aß1-42. Moreover, miR-186-5p combined with WT1-AS, and CCND2 was modulated by miR-186-5p. Furthermore, CCND2 elevation partially offsets the impacts of miR-186-5p elevation on Aß1-42-stimulated cell proliferation as well as apoptosis mediated with WT1-AS up-regulation. Our results indicated that up-regulation of lncRNA WT1-AS ameliorated Aß-stimulated neuronal damage through modulating miR-186-5p/CCND2 axis, offering a novel direction for AD therapy.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods.

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 µM, with Oxyberberine showing promising effects even at 10 µM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.