PubChem 2023 update

Microglial NLRP3 inflammasome-mediated neuroinflammation and therapeutic strategies in depression

Previous studies have demonstrated a bidirectional relationship between inflammation and depression. Activation of the nucleotide-binding oligomerization domain, leucine-rich repeat, and NLR family pyrin domain-containing 3 (NLRP3) inflammasomes is closely related to the pathogenesis of various neurological diseases. In patients with major depressive disorder, NLRP3 inflammasome levels are significantly elevated. Understanding the role that NLRP3 inflammasome-mediated neuroinflammation plays in the pathogenesis of depression may be beneficial for future therapeutic strategies. In this review, we aimed to elucidate the mechanisms that lead to the activation of the NLRP3 inflammasome in depression as well as to provide insight into therapeutic strategies that target the NLRP3 inflammasome. Moreover, we outlined various therapeutic strategies that target the NLRP3 inflammasome, including NLRP3 inflammatory pathway inhibitors, natural compounds, and other therapeutic compounds that have been shown to be effective in treating depression. Additionally, we summarized the application of NLRP3 inflammasome inhibitors in clinical trials related to depression. Currently, there is a scarcity of clinical trials dedicated to investigating the applications of NLRP3 inflammasome inhibitors in depression treatment. The modulation of NLRP3 inflammasomes in microglia holds promise for the management of depression. Further investigations are necessary to ascertain the efficacy and safety of these therapeutic approaches as potential novel antidepressant treatments.

Synergy of machine learning and density functional theory calculations for predicting experimental Lewis base affinity and Lewis polybase binding atoms

Investigation of Lewis acid-base interactions has been conducted by ab initio calculations and machine learning (ML) models. This study aims to resolve two critical tasks that have not been quantitatively investigated. First, ML models developed from density functional theory (DFT) calculations predict experimental BF3 affinity with Pearson correlation coefficients around 0.9 and mean absolute errors around 10 kJ mol-1. The ML models are trained by DFT-calculated BF3 affinity of more than 3000 adducts, with input features readily obtained by rdkit. Second, the ML models have the capability of predicting the relative strength of Lewis base binding atoms in Lewis polybases, which is either an extremely challenging task to conduct experimentally or a computationally expensive task for ab initio methods. The study demonstrates and solidifies the potential of combining DFT calculations and ML models to predict experimental properties, especially those that are scarce and impractical to empirically acquire.

16S rRNA female reproductive microbiome investigation reveals Dalfopristin, Clorgyline, and Hydrazine as potential therapeutics for the treatment of bacterial vaginosis

Bacterial vaginosis (BV) is a prevalent vaginal illness resulting from a disruption in the vaginal microbial equilibrium. The vaginal microbiota has been shown to have a substantial impact on the development and continuation of BV. This work utilized 16S rRNA sequence analysis of vaginal microbiome samples (Control vs BV samples) utilizing Parallel-Meta 3 to investigate the variations in microbial composition. The unique genes identified were used to determine prospective therapeutic targets and their corresponding inhibitory ligands. Further, molecular docking was conducted and then MD simulations were carried out to confirm the docking outcomes. In the BV samples, we detected several anaerobic bacteria recognized for their ability to generate biofilms, namely Acetohalobium, Anaerolineaceae, Desulfobacteraceae, and others. Furthermore, we identified Dalfopristin, Clorgyline, and Hydrazine as potential therapeutic options for the management of BV. This research provides new insights into the causes of BV and shows the potential effectiveness of novel pharmacological treatments.

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation

The urea transporter UT-B1, encoded by the SLC14A1 gene, has been hypothesized to be a significant protein whose deficiency and dysfunction contribute to the pathogenesis of bladder cancer and many other diseases. Several studies reported the association of genetic alterations in the SLC14A1 (UT-B1) gene with bladder carcinogenesis, suggesting a need for thorough characterization of the UT-B1 protein's coding and non-coding variants. This study used various computational techniques to investigate the commonly occurring germ-line missense and non-coding SNPs (ncSNPs) of the SLC14A1 gene (UT-B1) for their structural, functional, and molecular implications for disease susceptibility and dysfunctionality. SLC14A1 missense variants, primarily identified from the ENSEMBL genome browser, were screened through twelve functionality prediction tools leading to two variants D280Y (predicted detrimental by maximum tools) and D280N (high global MAF) for rs1058396. Subsequently, the ConSurf and NetSurf tools revealed the D280 residue to be in a variable site and exposed on the protein surface. According to I-Mutant2.0 and MUpro, both variants are predicted to cause a significant effect on protein stability. Analysis of molecular docking anticipated these two variants to decrease the binding affinity of UT-B1 protein for the examined ligands to a significant extent. Molecular dynamics also disclosed the possible destabilization of the UT-B1 protein due to single nucleotide polymorphism compared to wild-type protein which may result in impaired protein function. Furthermore, several non-coding SNPs were estimated to affect transcription factor binding and regulation of SLC14A1 gene expression. Additionally, two ncSNPs were found to affect miRNA-based post-transcriptional regulation by creating new seed regions for miRNA binding. This comprehensive in-silico study of SLC14A1 gene variants may serve as a springboard for future large-scale investigations examining SLC14A1 polymorphisms.

New methods for drug synergy prediction: A mini-review

In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.

Integrated network pharmacology, molecular docking, and lipidomics to reveal the regulatory effect of Qingxuan Zhike granules on lipid metabolism in lipopolysaccharide-induced acute lung injury

Qingxuan Zhike granules (QXZKG), a traditional Chinese patent medication, has shown therapeutic potential against acute lung injury (ALI). However, the precise mechanism underlying its lung-protective effects requires further investigation. In this study, integrated network pharmacology, molecular docking, and lipidomics were used to elucidate QXZKG's regulatory effect on lipid metabolism in lipopolysaccharide-induced ALI. Animal experiments were conducted to substantiate the efficacy of QXZKG in reducing pro-inflammatory cytokines and mitigating pulmonary pathology. Network pharmacology analysis identified 145 active compounds that directly targeted 119 primary targets of QXZKG against ALI. Gene Ontology function analysis emphasized the roles of lipid metabolism and mitogen-activated protein kinase (MAPK) cascade as crucial biological processes. The MAPK1 protein exhibited promising affinities for naringenin, luteolin, and kaempferol. Lipidomic analysis revealed that 12 lipids showed significant restoration following QXZKG treatment (p < 0.05, FC >1.2 or <0.83). Specifically, DG 38:4, DG 40:7, PC O-40:8, TG 18:1_18:3_22:6, PI 18:2_20:4, FA 16:3, FA 20:3, FA 20:4, FA 22:5, and FA 24:5 were downregulated, while Cer 18:0;2O/24:0 and SM 36:1;2O/34:5 were upregulated in the QXZKG versus model groups. This study enhances our understanding of the active compounds and targets of QXZKG, as well as the potential of lipid metabolism in the treatment of ALI.

Reversine inhibits proliferation and induces apoptosis of human osteosarcoma cells through targeting MEK1

Reversine, or 2-(4-morpholinoanilino)-6-cyclohexylaminopurine, is a 2,6-disubstituted purine derivative. This small molecule shows anti-tumor potential by playing a central role in the inhibition of several kinases related to cell cycle regulation and cytokinesis. In this study, systematic review demonstrated the feasibility and pharmacological mechanism of anti-tumor effect of reversine. Firstly, we grafted MNNG/HOS, U-2 OS, MG-63 osteosarcoma cell aggregates onto chicken embryonic chorioallantoic membrane (CAM) to examine the tumor volume of these grafts after reversine treatment. Following culture, reversine inhibited the growth of osteosarcoma cell aggregates on CAM significantly. In vitro experiment, reversine suppressed osteosarcoma cell viability, colony formation, proliferation, and induced apoptosis and cell cycle arrest at G0-G1 phase. Scratch wound assay demonstrated that reversine restrained cell migration. Reversine increased the protein expression of E-cadherin. The mRNA expression of Rac1, RhoA, CDC42, PTK2, PXN, N-cadherin, Vimentin in MNNG/HOS, U-2 OS and MG-63 cells were suppressed and PTEN increased after reversine treatment. Network pharmacology prediction, molecular docking and systematic review revealed MEK1 can be used as an effective target for reversine to inhibit osteosarcoma. Western blot results show the regulation of MEK1 and ERK1/2 by reversine was not consistent in different osteosarcoma cell lines, but we found that reversine significantly inhibited the protein expression of MEK1 in MNNG/HOS, U-2 OS and MG-63. All these suggested that reversine can exert its anti-tumor effect by targeting the expression of MEK1.

The mechanism of action of myricetin against lung adenocarcinoma based on bioinformatics, in silico and in vitro experiments

Myricetin is a natural flavonoid with anti-cancer and anti-inflammatory effects, but its mechanism for treating lung adenocarcinoma (LUAD) remains unclearly. Therefore, bioinformatics, in silico and in vitro experiments were employed to elucidate this issue in this study. The core targets of myricetin against LUAD were screened by PharmaMapper (v2017), Assistant for Clinical Bioinformatics, STRING (v11.5) and Cytoscape (v3.8.1). Using Kaplan-Meier Plotter (v2022.04.20), UALCAN (v2021.12.13) and GEPIA (v2.0) databases, the correlation between core genes and the prognosis of LUAD patients were analyzed, and the expression levels of core genes were verified. In silico studies were used to analyze the binding energies and sites of myricetin with core genes. The effects of myricetin on H1975 cells were explored through thiazolyl blue (MTT), cell migration, colony formation and western blot assays. A total of 72 potential targets of myricetin against LUAD were identified through bioinformatics. Among the four core targets obtained by multiple networks and in silico assays, the up-regulated MMP9 (HR = 1.14 (1-1.29), logrank P = 0.046) and down-regulated PIK3R1 (HR = 0.58 (0.51-0.66), logrank P < 1E-16) were positively correlated with poor survival outcomes in LUAD patients. In vitro experiments demonstrated that myricetin inhibited the proliferation and migration of H1975 cells, promoting their apoptosis. Myricetin inhibits the proliferation of H1975 cells and induces cell apoptosis through its influence on the expression levels of MMP1, MMP3, MMP9, and PIK3R1 and regulating the multiple pathways these genes participate in. Both MMP9 and PIK3R1 are potential biomarkers for LUAD.

Prediction of viral protease inhibitors using proteochemometrics approach

Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands. We have developed an original protocol allowing us to collect a comprehensive set of 15 protein sequences and more than 9000 ligands from the ChEMBL database. The N-grams were derived from the 3D-based alignment, accurately superposing ligand-binding regions. In testing the ligand set in SAR mode with MNA descriptors, an accuracy above 0.95 was determined that shows the perspective of the antiviral drug search in virtual chemical libraries. The effective PCM models were built with the TLMNA descriptor. The strong validation procedure with pair exclusion simulated the prediction of interactions between the new ligands and new targets, resulting in accuracy estimation up to 0.89. The PCM approach shows slightly lower accuracy caused by more uncertainty compared with SAR, but it overcomes the problem of data incompleteness.

Draft genome sequence and tissue expression panel of Pacific saury (Cololabis saira)

Pacific saury (Cololabis saira) is an important fish in several countries. Notably, the catch of this fish has markedly decreased recently, which might be due to environmental changes, including feeding habitat changes. However, no clear correlation has been observed. Therefore, the physiological basis of Pacific saury in relation to possible environmental factors must be understood. We sequenced the genome of Pacific saury and extracted RNA from nine tissues (brain, eye, gill, anterior/posterior guts, kidney, liver, muscle, and ovary). In 1.09 Gb assembled genome sequences, a total of 26,775 protein-coding genes were predicted, of which 26,241 genes were similar to known genes in a public database. Transcriptome analysis revealed that 24,254 genes were expressed in at least one of the nine tissues, and 7,495 were highly expressed in specific tissues. Based on the similarity of the expression profiles to those of model organisms, the transcriptome obtained was validated to reflect the characteristics of each tissue. Thus, the present genomic and transcriptomic data serve as useful resources for molecular studies on Pacific saury. In particular, we emphasize that the gene expression data, which serve as the tissue expression panel of this species, can be employed in comparative transcriptomics on marine environmental responses.

Bioactive compounds from Ocimum tenuiflorum and Poria cocos: A novel natural Compound for insomnia treatment based on A computational approach

Insomnia, a widespread public health issue, is associated with substantial distress and daytime functionality impairments and can predispose to depression and cardiovascular disease. Cognitive Behavioral Anti-insomnia therapies including benzodiazepines often face limitations due to patient adherence or potential adverse effects. This study focused on identifying novel bioactive compounds from medicinal plants, aiming to discover and develop new therapeutic agents with low risk-to-benefit ratios using computational drug discovery methods. Through a systematic framework involving compound library preparation, evaluation of drug-likeness and pharmacokinetics, toxicity prediction, molecular docking, and molecular dynamic simulations, two natural compounds such as 2-(4-hydroxy-3-methoxyphenyl)-8-methoxy-6-prop-2-enyl-3,4-dihydro-2H-chromen-3-ol from Ocimum tenuiflorum and 7-(2-hydroxypropan-2-yl)-1,4a-dimethyl-9-oxo-3,4,10,10a-tetrahydro-2H-phenanthrene-1-carboxylic acid from Poria cocos exhibited high binding affinity with orexin receptor type 1 (OX1R) and type 2 (OX2R), surpassing commercial drugs used in insomnia treatment. Additionally, they showed interactions with critical amino acid residues within the receptors that play crucial roles in competitive inhibitor activity, like commercial drugs such as Suvorexant, Lemborexant, and Daridorexant. Further, molecular dynamics simulations of the protein-ligand complexes under conditions that mimic the in vivo environment revealed both compounds' sustained and robust interactions with the OX1R and OX2R, reinforcing their potential as effective therapeutic candidates. Furthermore, upon evaluating both compounds' drug-likeness, pharmacokinetics, and toxicity profiles, it was discerned that they displayed considerable drug-like properties and favorable pharmacokinetics, along with diminished toxicity. The research provides a solid foundation for further exploring and validating these compounds as potential anti-insomnia therapeutics.

AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

Mathematical modeling and optimization technique of anticancer antibiotic adsorption onto carbon nanocarriers

This study employs a combination of mathematical derivation and optimization technique to investigate the adsorption of drug molecules on nanocarriers. Specifically, the chemotherapy drugs, fluorouracil, proflavine, and methylene blue, are non-covalently bonded with either a flat graphene sheet or a spherical C 60 fullerene. Mathematical expressions for the interaction energy between an atom and graphene, as well as between an atom and C 60 fullerene, are derived. Subsequently, a discrete summation is evaluated for all atoms on the drug molecule utilizing the U-NSGA-III algorithm. The stable configurations' three-dimensional architectures are presented, accompanied by numerical values for crucial parameters. The results indicate that the nanocarrier's structure effectively accommodates the atoms on the drug's carbon planes. The three drug types' molecules disperse across the graphene surface, whereas only fluorouracil spreads on the C 60 surface; proflavine and methylene blue stack vertically to form a layer. Furthermore, all atomic positions of equilibrium configurations for all systems are obtained. This hybrid method, integrating analytical expressions and an optimization process, significantly reduces computational time, representing an initial step in studying the binding of drug molecules on nanocarriers.

Choice of the Right Supporting Electrolyte in Electrochemical Reductions: A Principal Component Analysis

We present an analysis of a set of molecular, electrical, and electronic properties for a large number of the cations of quaternary ammonium salts usually employed as supporting electrolytes in cathodic reduction reactions. The goal of the present study is to define a measure for the quality of a supporting electrolyte in terms of the yield of the reaction considered. We performed a principal component analysis using the normalized values of the properties in order to lower the number of relevant reaction coordinates and find that the integral variance of 13 properties can well be represented by three principal components. The yield of the electrochemical hydrodimerization of acrylonitrile employing different quaternary ammonium salts as supporting electrolytes was determined in a series of experiments. We found only a very weak correlation between the yield and the values of the properties but a strong correlation between the yield and the values of the most important principal component. Very similar results are obtained for two further existing systematic experimental studies of the impact of the supporting electrolyte on the yield of cathodic reductions. For all three example reactions, a supervised regression using the two most important principal components as variables yields excellent values for the coefficients of determination. For comparison, we also applied our methodology to sets of purely structure-based features that are usually employed in cheminformatics and obtained results of almost similar quality. We therefore conjecture that our methodology in combination with a small number of experiments can be used to predict the yield of a given cathodic reduction on the basis of the properties of the supporting electrolyte.

AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity

Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, environmental monitoring, and beyond. In this work, we developed a machine learning (ML) approach to search for new nanozymes and deployed a web platform, DiZyme, featuring a state-of-the-art database of nanozymes containing 1210 experimental samples, catalytic activity prediction, and DiZyme Assistant interface powered by a large language model (LLM). For the first time, we enable the prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving R2 = 0.75 for the Michaelis-Menten constant and R2 = 0.77 for the maximum velocity on unseen test data. We envision an accurate prediction of multiple catalytic activities (peroxidase, oxidase, and catalase) promoting novel applications for a wide range of surface-modified inorganic nanozymes. The DiZyme Assistant based on the ChatGPT model provides users with supporting information on experimental samples, such as synthesis procedures, measurement protocols, etc. DiZyme (dizyme.aicidlab.itmo.ru) is now openly available worldwide.

In Silico Assisted Identification, Synthesis, and In Vitro Pharmacological Characterization of Potent and Selective Blockers of the Epilepsy-Associated KCNT1 Channel

Gain-of-function (GoF) variants in KCNT1 channels cause severe, drug-resistant forms of epilepsy. Quinidine is a known KCNT1 blocker, but its clinical use is limited due to severe drawbacks. To identify novel KCNT1 blockers, a homology model of human KCNT1 was built and used to screen an in-house library of compounds. Among the 20 molecules selected, five (CPK4, 13, 16, 18, and 20) showed strong KCNT1-blocking ability in an in vitro fluorescence-based assay. Patch-clamp experiments confirmed a higher KCNT1-blocking potency of these compounds when compared to quinidine, and their selectivity for KCNT1 over hERG and Kv7.2 channels. Among identified molecules, CPK20 displayed the highest metabolic stability; this compound also blocked KCNT2 currents, although with a lower potency, and counteracted GoF effects prompted by 2 recurrent epilepsy-causing KCNT1 variants (G288S and A934T). The present results provide solid rational basis for future design of novel compounds to counteract KCNT1-related neurological disorders.

Exploring the mechanism of 6-Methoxydihydrosanguinarine in the treatment of lung adenocarcinoma based on network pharmacology, molecular docking and experimental investigation

BACKGROUND

6-Methoxydihydrosanguinarine (6-MDS) has shown promising potential in fighting against a variety of malignancies. Yet, its anti‑lung adenocarcinoma (LUAD) effect and the underlying mechanism remain largely unexplored. This study sought to explore the targets and the probable mechanism of 6-MDS in LUAD through network pharmacology and experimental validation.

METHODS

The proliferative activity of human LUAD cell line A549 was evaluated by Cell Counting Kit-8 (CCK8) assay. LUAD related targets, potential targets of 6-MDS were obtained from databases. Venn plot analysis were performed on 6-MDS target genes and LUAD related genes to obtain potential target genes for 6-MDS treatment of LUAD. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was utilized to perform a protein-protein interaction (PPI) analysis, which was then visualized by Cytoscape. The hub genes in the network were singled out by CytoHubba. Metascape was employed for GO and KEGG enrichment analyses. molecular docking was carried out using AutoDock Vina 4.2 software. Gene expression levels, overall survival of hub genes were validated by the GEPIA database. Protein expression levels, promotor methylation levels of hub genes were confirmed by the UALCAN database. Timer database was used for evaluating the association between the expression of hub genes and the abundance of infiltrating immune cells. Furthermore, correlation analysis of hub genes expression with immune subtypes of LUAD were performed by using the TISIDB database. Finally, the results of network pharmacology analysis were validated by qPCR.

RESULTS

Experiments in vitro revealed that 6-MDS significantly reduced tumor growth. A total of 33 potential targets of 6-MDS in LUAD were obtained by crossing the LUAD related targets with 6-MDS targets. Utilizing CytoHubba, a network analysis tool, the top 10 genes with the highest centrality measures were pinpointed, including MMP9, CDK1, TYMS, CCNA2, ERBB2, CHEK1, KIF11, AURKB, PLK1 and TTK. Analysis of KEGG enrichment hinted that these 10 hub genes were located in the cell cycle signaling pathway, suggesting that 6-MDS may mainly inhibit the occurrence of LUAD by affecting the cell cycle. Molecular docking analysis revealed that the binding energies between 6-MDS and the hub proteins were all higher than - 6 kcal/Mol with the exception of AURKB, indicating that the 9 targets had strong binding ability with 6-MDS.These results were corroborated through assessments of mRNA expression levels, protein expression levels, overall survival analysis, promotor methylation level, immune subtypes andimmune infiltration. Furthermore, qPCR results indicated that 6-MDS can significantly decreased the mRNA levels of CDK1, CHEK1, KIF11, PLK1 and TTK.

CONCLUSIONS

According to our findings, it appears that 6-MDS could possibly serve as a promising option for the treatment of LUAD. Further investigations in live animal models are necessary to confirm its potential in fighting cancer and to delve into the mechanisms at play.

MolModa: accessible and secure molecular docking in a web browser

Molecular docking advances early-stage drug discovery by predicting the geometries and affinities of small-molecule compounds bound to drug-target receptors, predictions that researchers can leverage in prioritizing drug candidates for experimental testing. Unfortunately, existing docking tools often suffer from poor usability, data security, and maintainability, limiting broader adoption. Additionally, the complexity of the docking process, which requires users to execute a series of specialized steps, often poses a substantial barrier for non-expert users. Here, we introduce MolModa, a secure, accessible environment where users can perform molecular docking entirely in their web browsers. We provide two case studies that illustrate how MolModa provides valuable biological insights. We further compare MolModa to other docking tools to highlight its strengths and limitations. MolModa is available free of charge for academic and commercial use, without login or registration, at https://durrantlab.com/molmoda.

The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions

Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.

MolPROP: Molecular Property prediction with multimodal language and graph fusion

Pretrained deep learning models self-supervised on large datasets of language, image, and graph representations are often fine-tuned on downstream tasks and have demonstrated remarkable adaptability in a variety of applications including chatbots, autonomous driving, and protein folding. Additional research aims to improve performance on downstream tasks by fusing high dimensional data representations across multiple modalities. In this work, we explore a novel fusion of a pretrained language model, ChemBERTa-2, with graph neural networks for the task of molecular property prediction. We benchmark the MolPROP suite of models on seven scaffold split MoleculeNet datasets and compare with state-of-the-art architectures. We find that (1) multimodal property prediction for small molecules can match or significantly outperform modern architectures on hydration free energy (FreeSolv), experimental water solubility (ESOL), lipophilicity (Lipo), and clinical toxicity tasks (ClinTox), (2) the MolPROP multimodal fusion is predominantly beneficial on regression tasks, (3) the ChemBERTa-2 masked language model pretraining task (MLM) outperformed multitask regression pretraining task (MTR) when fused with graph neural networks for multimodal property prediction, and (4) despite improvements from multimodal fusion on regression tasks MolPROP significantly underperforms on some classification tasks. MolPROP has been made available at https://github.com/merck/MolPROP . SCIENTIFIC CONTRIBUTION: This work explores a novel multimodal fusion of learned language and graph representations of small molecules for the supervised task of molecular property prediction. The MolPROP suite of models demonstrates that language and graph fusion can significantly outperform modern architectures on several regression prediction tasks and also provides the opportunity to explore alternative fusion strategies on classification tasks for multimodal molecular property prediction.

Anti-inflammatory effect of proanthocyanidins from blueberry through NF-κB/NLRP3 signaling pathway in vivo and in vitro

Background: Systemic inflammatory response syndrome (SIRS) is an uncontrolled systemic inflammatory response. Proanthocyanidins (PC) is a general term of polyphenol compounds widely existed in blueberry fruits and can treat inflammation-related diseases. This study aimed to explore the regulatory effect of PC on lipopolysaccharide (LPS)-induced systemic inflammation and its potential mechanism, providing effective strategies for the further development of PC.Methods: Here, RAW264.7 macrophages were stimulated with LPS to establish an inflammation model in vitro, while endotoxin shock mouse models were constructed by LPS in vivo. The function of PC was investigated by MTT, ELISA kits, H&E staining, immunohistochemistry, and Western blot analysis.Results: Functionally, PC could demonstrate the potential to mitigate mortality in mice with endotoxin shock, as well as attenuated the levels of inflammatory cytokines (IL-6, TNF-α) and biochemical indicators (AST, ALT, CRE and BUN). Moreover, it had a significant protective effect on lung and kidney tissues damage. Mechanistically, PC exerted anti-inflammatory effects by inhibiting the activation of the NF-κB/NLRP3 signaling pathway.Conclusion: PC might have the potential ability of anti-inflammatory effects via modulation of the NF-κB/NLRP3 signaling pathway.

Grouping strategies for assessing and managing persistent and mobile substances

Background

Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050.

Main

Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples.

Conclusions

Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

Comprehensive analysis of KLF family reveals KLF6 as a promising prognostic and immune biomarker in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors worldwide, with extremely aggressive and complicated biology. Krüppel-like factors (KLFs) encode a series of transcriptional regulatory proteins and play crucial roles in a variety of processes, including tumor cell differentiation and proliferation. However, the potential biological functions and possible pathways of KLFs in the progression of PDAC remain elusive.

METHODS

We systematically evaluated the transcriptional variations and expression patterns of KLFs in pancreatic cancer from the UCSC Xena. Based on difference analysis, the non-negative matrix factorization (NMF) algorithm was utilized to identify the immune characteristics and clinical significance of two different subtypes. The multivariate Cox regression was used to construct the risk model and then explore the differences in tumor immune microenvironment (TIME) and drug sensitivity between high and low groups. Through single-cell RNA sequencing (scRNA-seq) analysis, we screened KLF6 and further investigated its biological functions in pancreatic cancer and pan-cancer.

RESULTS

The KLFs exhibited differential expression and mutations in the transcriptomic profile of PDAC. According to the expression of KLFs, patients were classified into two distinct subtypes, each exhibiting significant differences in prognosis and TIME. Moreover, the KLF signature was developed using univariate Cox and Lasso regression, which proved to be a reliable and effective prognostic model. Furthermore, the KLF_Score was closely associated with immune infiltration, response to immunotherapy, and drug sensitivity and we screened small molecule compounds targeting prognostic genes separately. Through scRNA-seq analysis, KLF6 was selected to further demonstrate its role in the malignance of PC in vitro. Finally, pan-cancer analysis emphasized the biological significance of KLF6 in multiple types of tumors and its clinical utility in assessing cancer prognosis.

CONCLUSION

This study elucidated the pivotal role of KLF family genes in the malignant development of PC through comprehensive analysis and revealed that KLF6 would be a novel diagnostic biomolecule marker and potential therapeutic target for PDAC.

Emission of volatile organic compounds during open fire cooking with wood biomass: Traditional three-stone open fire vs. gasifier cooking stove in rural Kenya

Cooking with wood biomass fuels releases hazardous air pollutants, including volatile organic compounds (VOCs), that often disproportionally affect women and children. This study, conducted in Kwale and Siaya counties in Kenya, employed thermal desorption gas chromatography - mass spectrometry to analyse VOC emissions from cooking with a wood biomass three-stone open fire vs. top-lit updraft gasifier stove. In kitchens with adequate ventilation, total VOC levels increased from 35 to 252 μg∙m-3 before cooking to 2235-5371 μg∙m-3 during open fire cooking, whereas use of a gasifier stove resulted in reduced emissions from cooking by 48-77 % (506-2778 μg∙m-3). However, in kitchens with poor ventilation, there was only a moderate difference in total VOC levels between the two methods of cooking (9034-9378 μg∙m-3 vs. 6727-8201 μg∙m-3 for the three-stone open fire vs. gasifier stove, respectively). Using a non-target screening approach revealed significantly increased levels of VOCs, particularly benzenoids, oxygenated and heterocyclic compounds, when cooking with the traditional open fire, especially in closed kitchens, highlighting the effects of poor ventilation. Key hazardous VOCs included benzene, naphthalene, phenols and furans, suggesting potential health risks from cooking. In kitchens with good ventilation, use of the gasifier stove markedly reduced emissions of these priority toxic VOCs compared to cooking with an open fire. Thus, substituting open fires with gasifier stoves could help to improve household air quality and alleviate health risks. The study revealed that VOCs were present prior to cooking, possibly originating from previously cooked food (buildup) or the outside environment. VOC emissions were also exacerbated by reduced air flow in high humidity during rainfall, suggesting an area for further research. The findings underscore the importance of adopting cleaner cooking technologies and enhancing kitchen ventilation to mitigate the impacts of VOCs in developing countries.

Aspartame carcinogenic potential revealed through network toxicology and molecular docking insights

The research employed network toxicology and molecular docking techniques to systematically examine the potential carcinogenic effects and mechanisms of aspartame (L-α-aspartyl-L-phenylalanine methyl ester). Aspartame, a commonly used synthetic sweetener, is widely applied in foods and beverages globally. In recent years, its safety issues, particularly the potential carcinogenic risk, have garnered widespread attention. The study first constructed an interaction network map of aspartame with gastric cancer targets using network toxicology methods and identified key targets and pathways. Preliminary validation was conducted through microarray data analysis and survival analysis, and molecular docking techniques were employed to further examine the binding affinity and modes of action of aspartame with key proteins. The findings suggest that aspartame has the potential to impact various cancer-related proteins, potentially raising the likelihood of cellular carcinogenesis by interfering with biomolecular function. Furthermore, the study found that the action patterns and pathways of aspartame-related targets are like the mechanisms of known carcinogenic pathways, further supporting the scientific hypothesis of its potential carcinogenicity. However, given the complexity of the in vivo environment, we also emphasize the necessity of validating these molecular-level findings in actual biological systems. The study introduces a fresh scientific method for evaluating the safety of food enhancers and provides a theoretical foundation for shaping public health regulations.

Prioritizing of potential environmental exposure carcinogens beyond IARC group 1-2B based on weight of evidence (WoE) approach

Environmental exposures are the main cause of cancer, and their carcinogenicity has not been fully evaluated, identifying potential carcinogens that have not been evaluated is critical for safety. This study is the first to propose a weight of evidence (WoE) approach based on computational methods to prioritize potential carcinogens. Computational methods such as read across, structural alert, (Quantitative) structure-activity relationship and chemical-disease association were evaluated and integrated. Four different WoE approach was evaluated, compared to the best single method, the WoE-1 approach gained 0.21 and 0.39 improvement in the area under the receiver operating characteristic curve (AUC) and Matthew's correlation coefficient (MCC) value, respectively. The evaluation of 681 environmental exposures beyond IARC list 1-2B prioritized 52 chemicals of high carcinogenic concern, of which 21 compounds were known carcinogens or suspected carcinogens, and eight compounds were identified as potential carcinogens for the first time. This study illustrated that the WoE approach can effectively complement different computational methods, and can be used to prioritize chemicals of carcinogenic concern.

Therapeutic Effects of Anti-Inflammatory and Anti-Oxidant Nutritional Supplementation in Retinal Ischemic Diseases

Appropriate nutrients are essential for cellular function. Dietary components can alter the risk of systemic metabolic diseases, including cardiovascular diseases, cancer, diabetes, and obesity, and can also affect retinal diseases, including age-related macular degeneration, diabetic retinopathy, and glaucoma. Dietary nutrients have been assessed for the prevention or treatment of retinal ischemic diseases and the diseases of aging. In this article, we review clinical and experimental evidence concerning the potential of some nutritional supplements to prevent or treat retinal ischemic diseases and provide further insights into the therapeutic effects of nutritional supplementation on retinopathies. We will review the roles of nutrients in preventing or protecting against retinal ischemic diseases.

Toward the Reconciliation of Inconsistent Molecular Structures from Biochemical Databases

Information on the structure of molecules, retrieved via biochemical databases, plays a pivotal role in various disciplines, including metabolomics, systems biology, and drug discovery. No such database can be complete and it is often necessary to incorporate data from several sources. However, the molecular structure for a given compound is not necessarily consistent between databases. This article presents StructRecon, a novel tool for resolving unique molecular structures from database identifiers. Currently, identifiers from BiGG, ChEBI, Escherichia coli Metabolome Database (ECMDB), MetaNetX, and PubChem are supported. StructRecon traverses the cross-links between entries in different databases to construct what we call identifier graphs. The goal of these graphs is to offer a more complete view of the total information available on a given compound across all the supported databases. To reconcile discrepancies met during the traversal of the databases, we develop an extensible model for molecular structure supporting multiple independent levels of detail, which allows standardization of the structure to be applied iteratively. In some cases, our standardization approach results in multiple candidate structures for a given compound, in which case a random walk-based algorithm is used to select the most likely structure among incompatible alternatives. As a case study, we applied StructRecon to the EColiCore2 model. We found at least one structure for 98.66% of its compounds, which is more than twice as many as possible when using the databases in more standard ways not considering the complex network of cross-database references captured by our identifier graphs. StructRecon is open-source and modular, which enables support for more databases in the future.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Identification of Potential Inhibitors of the SARS-CoV-2 NSP13 Helicase via Structure-Based Ligand Design, Molecular Docking and Nonequilibrium Alchemical Simulations

We have assembled a computational pipeline based on virtual screening, docking techniques, and nonequilibrium molecular dynamics simulations, with the goal of identifying possible inhibitors of the SARS-CoV-2 NSP13 helicase, catalyzing by ATP hydrolysis the unwinding of double or single-stranded RNA in the viral replication process inside the host cell. The druggable sites for broad-spectrum inhibitors are represented by the RNA binding sites at the 5' entrance and 3' exit of the central channel, a structural motif that is highly conserved across coronaviruses. Potential binders were first generated using structure-based ligand techniques. Their potency was estimated by using four popular docking scoring functions. Common docking hits for NSP13 were finally tested using advanced nonequilibrium alchemical techniques for binding free energy calculations on a high-performing parallel cluster. Four potential NSP13 inhibitors with potency from submicrimolar to nanomolar were finally identified.

Metabolic signature and response to glutamine deprivation are independent of p53 status in B cell malignancies

The tumor suppressor p53 has been described to control various aspects of metabolic reprogramming in solid tumors, but in B cell malignancies that role is as yet unknown. We generated pairs of p53 functional and knockout (KO) clones from distinct B cell malignancies (acute lymphoblastic leukemia, chronic lymphocytic leukemia, diffuse large B cell lymphoma, and multiple myeloma). Metabolomics and isotope tracing showed that p53 loss did not drive a common metabolic signature. Instead, cell lines segregated according to cell of origin. Next, we focused on glutamine as a crucial energy source in the B cell tumor microenvironment. In both TP53 wild-type and KO cells, glutamine deprivation induced cell death through the integrated stress response, via CHOP/ATF4. Lastly, combining BH3 mimetic drugs with glutamine starvation emerged as a possibility to target resistant clones. In conclusion, our analyses do not support a common metabolic signature of p53 deficiency in B cell malignancies and suggest therapeutic options for exploration based on glutamine dependency.

Exploring the mechanism of dendrobine in treating metabolic associated fatty liver disease based on network pharmacology and experimental validation

BACKGROUND

This study investigates the therapeutic mechanisms of dendrobine, a primary bioactive compound in Dendrobium nobile, for Metabolic Associated Fatty Liver Disease (MASLD) management. Utilizing network pharmacology combined with experimental validation, the clinical effectiveness of dendrobine in MASLD treatment was assessed and analyzed.

RESULTS

The study demonstrates significant improvement in liver function among MASLD patients treated with Dendrobium nobile. Network pharmacology identified key targets such as Peroxisome Proliferator-Activated Receptor Gamma (PPARG), Interleukin 6 (IL6), Tumor Necrosis Factor (TNF), Interleukin 1 Beta (IL1B), and AKT Serine/Threonine Kinase 1 (AKT1), with molecular docking confirming their interactions. Additionally, dendrobine significantly reduced ALT and AST levels in palmitic acid-treated HepG2 cells, indicating hepatoprotective properties and amelioration of oxidative stress through decreased Malondialdehyde (MDA) levels and increased Superoxide Dismutase (SOD) levels.

CONCLUSION

Dendrobine mitigates liver damage in MASLD through modulating inflammatory and immune responses and affecting lipid metabolism, potentially by downregulating inflammatory mediators like TNF, IL6, IL1B, and inhibiting AKT1 and Signal Transducer and Activator of Transcription 3 (STAT3). This study provides a theoretical basis for the application of dendrobine in MASLD treatment, highlighting its potential as a therapeutic agent.

Exploring SureChEMBL from a drug discovery perspective

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

Cordycepin enhances anti-tumor immunity in breast cancer by enhanceing ALB expression

Objective

The treatment of breast cancer still faces great challenges, and it is necessary to continuously explore effective drugs and targets to promote immune precision medicine. This study aims to investigate the immune-related regulatory mechanism of cordycepin in breast cancer.

Methods

Network pharmacology was employed to discovery the action of cordyceps on breast cancer targets, molecular docking was employed to analyze the interaction pattern between core components and targets, and biological information analysis was used to explore the target-related immune mechanism and verified in vitro experiments.

Results

The results of this study indicate that cordycepin can effectively inhibit breast cancer. The roles of cordycepin's active component and its target gene ALB were elucidated through the combined use of network pharmacology and molecular docking. Bioinformatics analysis revealed convincing associations between ALB and many immune pathway marker genes. ALB was inhibited in tumor expression, and cordycepin was found to enhance the expression of ALB in vitro to play an anti-tumor role.

Conclusion

Cordycepin regulates immune suppression of tumor, which is expected to open a new chapter of breast cancer immunotherapy.

Role of ribosomal pathways and comorbidity in COVID-19: Insight from SARS-CoV-2 proteins and host proteins interaction network analysis

The COVID-19 pandemic has become a significant global issue in terms of public health. While it is largely associated with respiratory complications, recent reports indicate that patients also experience neurological symptoms and other health issues. The objective of this study is to examine the network of protein-protein interactions (PPI) between SARS-CoV-2 proteins and human host proteins, pinpoint the central genes within this network implicated in disease pathology, and assess their viability as targets for drug development. The study adopts a network-based approach to construct a network of 29 SARS-CoV-2 proteins interacting with 2896 host proteins, with 176 host genes being identified as interacting genes with all the viral proteins. Gene ontology and pathway analysis of these host proteins revealed their role in biological processes such as translation, mRNA splicing, and ribosomal pathways. We further identified EEF2, RPS3, RPL9, RPS16, and RPL11 as the top 5 most connected hub genes in the disease-causing network, with significant interactions among each other. These hub genes were found to be involved in ribosomal pathways and cytoplasmic translation. Further a disease-gene interaction was also prepared to investigate the role of hub genes in other disorders and to understand the condition of comorbidity in COVID-19 patients. We also identified 13 drug molecules having interactions with all the hub genes, and estradiol emerged as the top potential drug target for the COVID-19 patients. Our study provides valuable insights using the protein-protein interaction network of SARS-CoV-2 proteins with host proteins and highlights the molecular basis of manifestation of COVID-19 and proposes drug for repurposing. As the pandemic continues to evolve, it is anticipated that investigating SARS-CoV-2 proteins will remain a critical area of focus for researchers globally, particularly in addressing potential challenges posed by specific SARS-CoV-2 variants in the future.

Reusable and inductively regenerable magnetic activated carbon for removal of organic micropollutants from secondary wastewater effluents

This work introduces a new sustainable alternative of powdered activated carbon (PAC) - magnetically harvestable and reusable after regeneration via inductive heating - for the adsorptive removal of organic micropollutants (OMP) from secondary wastewater effluents. For this purpose, two commercial PACs - lignite "L" (1187 m2/g) and coconut "C"-based (1524 m2/g) - were modified with magnetic iron oxide following two different synthesis approaches: infiltration ("infiltr") and surface deposition ("depos") route. The resulting magnetic powdered activated carbons (mPAC) and their precursor PACs were fully characterized before application. The iron oxide content of the modified "L" and "C" samples was ∼30 % and ∼20 %, respectively. Iron oxide gives the PAC beneficial magnetic properties for easy magnetic separation and simultaneously acts as an inductively heatable agent for the carbon regeneration. The infiltrated samples displayed better inductive heating performance and regeneration than their deposited counterparts. Tests with real wastewater showed fast adsorption kinetics of the organic load following the pseudo-second-order kinetic model. Adsorption isotherms were compliant with the Freundlich isotherm model. Sample "L-infiltr" had the best overall adsorption performance throughout 5 reuse cycles when intermediately inductively regenerated (<3 % drop in organics removal per cycle with intermediate regeneration vs. ∼10 % drop per cycle without regeneration). The treated supernatant was additionally tested for 31 representative organic micropollutants and their transformation products (pharmaceuticals, personal care products, industrial chemicals, etc.), where 26 OMPs had consistently high removal (>85 %) throughout 5 cycles with intermediate regeneration and for 28 OMPs the total adsorption efficiency dropped by <5 % after 5 cycles.

Chebifier: automating semantic classification in ChEBI to accelerate data-driven discovery

Connecting chemical structural representations with meaningful categories and semantic annotations representing existing knowledge enables data-driven digital discovery from chemistry data. Ontologies are semantic annotation resources that provide definitions and a classification hierarchy for a domain. They are widely used throughout the life sciences. ChEBI is a large-scale ontology for the domain of biologically interesting chemistry that connects representations of chemical structures with meaningful chemical and biological categories. Classifying novel molecular structures into ontologies such as ChEBI has been a longstanding objective for data scientific methods, but the approaches that have been developed to date are limited in several ways: they are not able to expand as the ontology expands without manual intervention, and they are not able to learn from continuously expanding data. We have developed an approach for automated classification of chemicals in the ChEBI ontology based on a neuro-symbolic AI technique that harnesses the ontology itself to create the learning system. We provide this system as a publicly available tool, Chebifier, and as an API, ChEB-AI. We here evaluate our approach and show how it constitutes an advance towards a continuously learning semantic system for chemical knowledge discovery.

Molecular Dynamic Simulation To Reveal the Mechanism Underlying MGL-3196 Resistance to Thyroxine Receptor Beta

Thyroxine receptor beta (TRβ) is a ligand-dependent nuclear receptor that participates in regulating multiple biological processes, particularly playing an important role in lipid metabolism regulation. TRβ is currently a popular therapeutic target for nonalcoholic steatohepatitis (NASH), while no drugs have been approved to treat this disease. MGL-3196 (Resmetirom) is the first TRβ agonist that has succeeded in phase III clinical trials for the treatment of NASH; therefore, studying its molecular mechanism of action is of great significance. In this study, we employed molecular dynamic simulation to investigate the interaction mode between MGL-3196 and TRβ at the all-atom level. More importantly, by comparing the binding patterns of MGL-3196 in several prevalent TRβ mutants, it was identified that the mutations R243Q and H435R located, respectively, around and within the ligand-binding pocket of TRβ cause TRβ to be insensitive to MGL-3196. This indicates that patients with NASH carrying these two mutations may exhibit resistance to the medication of MGL-3196, thereby highlighting the potential impact of TRβ mutations on TRβ-targeted treatment of NASH and beyond.

Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution Neural Network

We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles. Short force-field-based energy minimization is applied to refine final conformers. All computation-intensive stages of the algorithm are GPU-optimized. The procedure (termed GINGER) is benchmarked on a commonly used test set of bioactive three-dimensional (3D) conformers from the PDB. We demonstrate highly competitive results in conformer recovery and throughput rates suitable for giga-scale compound library processing. A web server that allows interactive conformer ensemble generation by GINGER and their viewing is made freely available at https://www.molsoft.com/gingerdemo.html.

Effect of continuous microwave processing on enzymes and quality attributes of bael beverage

Bael (Aegle marmelos) beverage was pasteurized using continuous-microwave (MW) and traditional thermal processing and the activity of native enzymes, pulp-hydrolyzing enzymes, bioactive, physicochemical, and sensory properties were analyzed. First-order and linear biphasic models fitted well (R2 ≥ 0.90) for enzyme inactivation and bioactive alteration kinetics, respectively. For the most resistant enzyme, polyphenoloxidase (PPO), the inactivation target of ≥ 90 % was achieved at 90 °C TMW (final temperature under MW) and 95 °C for 5 min (conventional thermal). MW treatment displayed faster enzyme inactivation and better retention of TPC and AOC. MW treatment at 90 °C TMW showed 5.3 min D-value, 90% total carotenoid content, 3.42 crisp sensory score (out of 5), and no or minor change in physicochemical attributes. Thermal and MW treatment caused the loss of 14 and 10 bioactive compounds, respectively. The secondary and tertiary structural modifications of PPO enzyme-protein revealed MW's lethality primarily due to its thermal effects.

Multi-technology integrated network pharmacology-based study on phytochemicals, active metabolites, and molecular mechanism of Psoraleae Fructus to promote melanogenesis

ETHNOPHARMACOLOGICAL RELEVANCE

According to the Compendium of Materia Medica (Shizhen Li, Ming dynasty) and Welfare Pharmacy (Song dynasty), Psoraleae Fructus (PF), a traditional Chinese medicine (TCM) has a bitter taste and warm nature, which has the effect of treating spleen and kidney deficiency and skin disease. Although PF has been widely used since ancient times and has shown satisfactory efficacy in treating vitiligo, the active substances and the mechanism of PF in promoting melanogenesis remain unclear.

AIM OF THE STUDY

To explore the active substances and action mechanisms of PF in promoting melanogenesis.

MATERIALS AND METHODS

Firstly, UPLC-UV-Q-TOF/MS was used to characterize the components in PF extract and identify the absorption components and metabolites of PF after oral administration at usual doses in rats. Secondly, the active substances and related targets and pathways were predicted by network pharmacology and molecular docking. Finally, pharmacodynamic and molecular biology experiments were used to verify the prediction results.

RESULTS

The experimental results showed that 15 compounds were identified in PF extract, and 44 compounds, consisting of 8 prototype components and 36 metabolites (including isomers) were identified in rats' plasma. Promising action targets (MAPK1, MAPK8, MAPK14) and signaling pathways (MAPK signaling pathway) were screened and refined to elucidate the mechanism of PF against vitiligo based on network pharmacology. Bergaptol and xanthotol (the main metabolites of PF), psoralen (prototype drug), and PF extract significantly increased melanin production in zebrafish embryos. Furthermore, bergaptol could promote the pigmentation of zebrafish embryos more than psoralen and PF extract. Bergaptol significantly increased the protein expression levels of p-P38 and decreased ERK phosphorylation in B16F10 cells, which was also supported by the corresponding inhibitor/activator combination study. Moreover, bergaptol increased the mRNA expression levels of the downstream microphthalmia-associated transcription factor (MITF) and tyrosinase in B16F10 cells. Our data elucidate that bergaptol may promote melanogenesis by regulating the p-P38 and p-ERK signaling pathway.

CONCLUSIONS

This study will lay a foundation for discovering potential new drugs for treating vitiligo and provide feasible ideas for exploring the mechanism of traditional Chinese medicine.

BitterMasS: Predicting Bitterness from Mass Spectra

Bitter compounds are common in nature and among drugs. Previously, machine learning tools were developed to predict bitterness from the chemical structure. However, known structures are estimated to represent only 5-10% of the metabolome, and the rest remain unassigned or "dark". We present BitterMasS, a Random Forest classifier that was trained on 5414 experimental mass spectra of bitter and nonbitter compounds, achieving precision = 0.83 and recall = 0.90 for an internal test set. Next, the model was tested against spectra newly extracted from the literature 106 bitter and nonbitter compounds and for additional spectra measured for 26 compounds. For these external test cases, BitterMasS exhibited 67% precision and 93% recall for the first and 58% accuracy and 99% recall for the second. The spectrum-bitterness prediction strategy was more effective than the spectrum-structure-bitterness prediction strategy and covered more compounds. These encouraging results suggest that BitterMasS can be used to predict bitter compounds in the metabolome without the need for structural assignment of individual molecules. This may enable identification of bitter compounds from metabolomics analyses, for comparing potential bitterness levels obtained by different treatments of samples and for monitoring bitterness changes overtime.

Anaerobic digestion of process water from hydrothermal treatment processes: a review of inhibitors and detoxification approaches

Integrating hydrothermal treatment processes and anaerobic digestion (AD) is promising for maximizing resource recovery from biomass and organic waste. The process water generated during hydrothermal treatment contains high concentrations of organic matter, which can be converted into biogas using AD. However, process water also contains various compounds that inhibit the AD process. Fingerprinting these inhibitors and identifying suitable mitigation strategies and detoxification methods is necessary to optimize the integration of these two technologies. By examining the existing literature, we were able to: (1) compare the methane yields and organics removal efficiency during AD of various hydrothermal treatment process water; (2) catalog the main AD inhibitors found in hydrothermal treatment process water; (3) identify recalcitrant components limiting AD performance; and (4) evaluate approaches to detoxify specific inhibitors and degrade recalcitrant components. Common inhibitors in process water are organic acids (at high concentrations), total ammonia nitrogen (TAN), oxygenated organics, and N-heterocyclic compounds. Feedstock composition is the primary determinant of organic acid and TAN formation (carbohydrates-rich and protein-rich feedstocks, respectively). In contrast, processing conditions (e.g., temperature, pressure, reaction duration) influence the formation extent of oxygenated organics and N-heterocyclic compounds. Struvite precipitation and zeolite adsorption are the most widely used approaches to eliminate TAN inhibition. In contrast, powdered and granular activated carbon and ozonation are the preferred methods to remove toxic substances before AD treatment. Currently, ozonation is the most effective approach to reduce the toxicity and recalcitrance of N and O-heterocyclic compounds during AD. Microaeration methods, which disrupt the AD microbiome less than ozone, might be more practical for nitrifying TAN and degrading recalcitrant compounds, but further research in this area is necessary.

FlyBase: updates to the Drosophila genes and genomes database

FlyBase (flybase.org) is a model organism database and knowledge base about Drosophila melanogaster, commonly known as the fruit fly. Researchers from around the world rely on the genetic, genomic, and functional information available in FlyBase, as well as its tools to view and interrogate these data. In this article, we describe the latest developments and updates to FlyBase. These include the introduction of single-cell RNA sequencing data, improved content and display of functional information, updated orthology pipelines, new chemical reports, and enhancements to our outreach resources.

Target prediction and potential application of dihydroartemisinin on hepatocarcinoma treatment

With high incidence of hepatocarcinoma and limited effective treatments, most patients suffer in pain. Antitumor drugs are single-targeted, toxicity, causing adverse side effects and resistance. Dihydroartemisinin (DHA) inhibits tumor through multiple mechanisms effectively. This study explores and evaluates safety and potential mechanism of DHA towards human hepatocarcinoma based on network pharmacology in a comprehensive way. Adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of DHA were evaluated with pkCSM, SwissADME, and ADMETlab. Potential targets of DHA were obtained from SwissTargetPrediction, Drugbank, TargetNET, and PharmMapper. Target gene of hepatocarcinoma was obtained from OMIM, GeneCards, and DisGeNET. Overlapping targets and hub genes were identified and analyzed for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway. Molecular docking was utilized to investigate the interactions sites and hydrogen bonds. Cell counting kit-8 (CCK8), wound healing, invasion, and migration assays on HepG2 and SNU387 cell proved DHA inhibits malignant biological features of hepatocarcinoma cell. DHA is safe and desirable for clinical application. A total of 131 overlapping targets were identified. Biofunction analysis showed targets were involved in kinase activity, protein phosphorylation, intracellular reception, signal transduction, transcriptome dysregulation, PPAR pathway, and JAK-STAT signaling axis. Top 9 hub genes were obtained using MCC (Maximal Clique Centrality) algorithm, namely CDK1, CCNA2, CCNB1, CCNB2, KIF11, CHEK1, TYMS, AURKA, and TOP2A. Molecular docking suggests that all hub genes form a stable interaction with DHA for optimal binding energy were all less than - 5 kcal/mol. Dihydroartemisinin might be a potent and safe anticarcinogen based on its biological safety and effective therapeutic effect.

Should Transformation Products Change the Way We Manage Chemicals?

When chemical pollutants enter the environment, they can undergo diverse transformation processes, forming a wide range of transformation products (TPs), some of them benign and others more harmful than their precursors. To date, the majority of TPs remain largely unrecognized and unregulated, particularly as TPs are generally not part of routine chemical risk or hazard assessment. Since many TPs formed from oxidative processes are more polar than their precursors, they may be especially relevant in the context of persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances, which are two new hazard classes that have recently been established on a European level. We highlight herein that as a result, TPs deserve more attention in research, chemicals regulation, and chemicals management. This perspective summarizes the main challenges preventing a better integration of TPs in these areas: (1) the lack of reliable high-throughput TP identification methods, (2) uncertainties in TP prediction, (3) inadequately considered TP formation during (advanced) water treatment, and (4) insufficient integration and harmonization of TPs in most regulatory frameworks. A way forward to tackle these challenges and integrate TPs into chemical management is proposed.

Heterologous expression of nattokinase in E. coli: Biochemical characterization and functional analysis of fibrin binding residues

Heterologous expression of nattokinase, a potent fibrinolytic enzyme, has been successfully carried out in various microorganisms. However, the successful expression of this enzyme as a soluble protein was not achieved in E. coli. This study delves into the expression of nattokinase in E. coli as a soluble protein followed by its biochemical characterization and functional analysis for fibrinolytic activity. E. coli BL21C41 and pET32a vector host strain with pGro7 protein chaperone induced with IPTG at 16 °C 180 rpm for 16 h enabled the production of recombinant nattokinase in soluble fraction. Enzymatic assays demonstrated its protease activity, while characterization revealed optimal catalytic conditions at 37 °C and pH 8.0, with remarkable stability over a broad pH range (6.0-10.0) and up to 50 °C. The kinetic constants were determined as follows: Km = 25.83 ± 3.43 μM, Vmax = 62.91 ± 1.68 μM/s, kcat = 38.45 ± 1.06 s-1, and kcat/Km = 1.49 × 106 M-1 s-1. In addition, the fibrinolytic activity of NK, quantified by the fibrin plate hydrolysis assay was 1038 ± 156 U/ml, with a corresponding specific activity of 1730 ± 260 U/mg and the assessment of clot lysis time on an artificial clot (1 mg) was found to be 51.5 ± 2.5 min unveiling nattokinase's fibrinolytic potential. Through molecular docking, a substantial binding energy of -6.46 kcal/mol was observed between nattokinase and fibrin, indicative of a high binding affinity. Key fibrin binding residues, including Ser300, Leu302, and Asp303, were identified and confirmed. These mutants affected specifically the fibrin binding and not the proteolytic activity of NK. This comprehensive study provides crucial conditions for the expression of protein in soluble form in E. coli and biochemical properties paving the way for future research and potential applications in medicine and biotechnology.

Deciphering the mechanisms and interactions of the endocrine disruptor bisphenol A and its analogs with the androgen receptor

Bisphenol A (BPA) and its various forms used as BPA alternatives in industries are recognized toxic compounds and antiandrogenic endocrine disruptors. These chemicals are widespread in the environment and frequently detected in biological samples. Concerns exist about their impact on hormones, disrupting natural biological processes in humans, together with their negative impacts on the environment and biotic life. This study aims to characterize the interaction between BPA analogs and the androgen receptor (AR) and the effect on the receptor's normal activity. To achieve this goal, molecular docking was conducted with BPA and its analogs and dihydrotestosterone (DHT) as a reference ligand. Four BPA analogs exhibited higher affinity (-10.2 to -8.7 kcal/mol) for AR compared to BPA (-8.6 kcal/mol), displaying distinct interaction patterns. Interestingly, DHT (-11.0 kcal/mol) shared a binding pattern with BPA. ADMET analysis of the top 10 compounds, followed by molecular dynamics simulations, revealed toxicity and dynamic behavior. Experimental studies demonstrated that only BPA disrupts DHT-induced AR dimerization, thereby affecting AR's function due to its binding nature. This similarity to DHT was observed during computational analysis. These findings emphasize the importance of targeted strategies to mitigate BPA toxicity, offering crucial insights for interventions in human health and environmental well-being.

Innovative non-targeted screening approach using High-resolution mass spectrometry for the screening of organic chemicals and identification of specific tracers of soil and dust exposure in children

Environmental contamination through direct contact, ingestion and inhalation are common routes of children's exposure to chemicals, in which through indoor and outdoor activities associated with common hand-to-mouth, touching objects, and behavioral tendencies, children can be susceptible and vulnerable to organic contaminants in the environment. The objectives of this study were the screening and identification of a wide range of organic contaminants in indoor dust, soil, food, drinking water, and urine matrices (N = 439), prioritizing chemicals to assess children's environmental exposure, and selection of unique tracers of soil and dust ingestion in young children by non-targeted analysis (NTA) using Q-Exactive Orbitrap followed data processing by the Compound Discoverer (v3.3, SP2). Chemical features were first prioritized based on their predominant abundance (peak area>500,000), detection frequency (in >50% of the samples), available information on their uses and potential toxicological effects. Specific tracers of soil and dust exposure in children were selected in this study including Tripropyl citrate and 4-Dodecylbenzenesulfonic acid. The criteria for selection of the tracers were based on their higher abundance, detection frequency, unique functional uses, measurable amounts in urine (suitable biomarker), and with information on gastrointestinal absorption, metabolism, and excretion, and were further confirmed by authentic standards. We are proposing for the first time suitable unique tracers for dust ingestion by children.