Publications by authors named "Guixia Liu"

185 Publications

Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches.

Chin Med 2021 Jul 23;16(1):59. Epub 2021 Jul 23.

Laboratory of Molecular Modeling and Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.

Background: The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods.

Methods: Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein-protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed.

Results: 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway.

Conclusions: This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13020-021-00473-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306236PMC
July 2021

In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods.

Chem Res Toxicol 2021 Jul 13. Epub 2021 Jul 13.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

Cytochrome P450 2C8 (CYP2C8) is a major drug-metabolizing enzyme in humans and is responsible for the metabolism of ∼5% drugs in clinical use. Thus, inhibition of CYP2C8, which causes potential adverse drug events, cannot be neglected. The in vitro drug interaction studies guidelines for industry issued by the FDA also point out that it needs to be determined whether investigated drugs are CYP2C8 inhibitors before clinical trials. However, current studies mainly focus on predicting the inhibitors of other major P450 enzymes, and the importance of CYP2C8 inhibition has been overlooked. Therefore, there is a need to develop models for identifying potential CYP2C8 inhibition. In this study, in silico classification models for predicting CYP2C8 inhibition were built by five machine-learning methods combined with nine molecular fingerprints. The performance of the models built was evaluated by test and external validation sets. The best model had AUC values of 0.85 and 0.90 for the test and external validation sets, respectively. The applicability domain was analyzed based on the molecular similarity and exhibited an impact on the improvement of prediction accuracy. Furthermore, several representative privileged substructures such as 1-benzo[]imidazole, 1-phenyl-1-pyrazole, and quinoline were identified by information gain and substructure frequency analysis. Overall, our results would be helpful for the prediction of CYP2C8 inhibition.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.chemrestox.1c00078DOI Listing
July 2021

Novel photosensitive dual-anisotropic conductive Janus film endued with magnetic-luminescent properties and derivative 3D structures.

J Colloid Interface Sci 2021 May 26;601:899-914. Epub 2021 May 26.

Key Laboratory of Applied Chemistry and Nanotechnology at Universities of Jilin Province, Changchun University of Science and Technology, Changchun 130022, China.

A new photosensitive dual-anisotropic conductive Janus film (PDCJF) is proposed for the first time. It is rationally designed and manufactured by facile electrospinning. PDCJF is firstly constructed using 2,7-dibromo-9-fluorenone (DBF) with photoconductive and luminescent properties. Janus nanofibers are respectively used as the building units to construct the top layer (T-PDCJF) and the bottom layer (B-PDCJF) of PDCJF. The two layers are tightly bonded to form PDCJF. Under light irradiation, there is photosensitive dual-anisotropic conduction in PDCJF, but there is no anisotropic conduction without light. Thus, the transition of PDCJF from mono-functional magnetism to tri-functionalities is realized under light and without light. The luminescence color of PDCJF is tunable and it emits white-light. This is made possible by modulating the amounts of luminescent substances and excitation wavelength. The microscopic Janus nanofibers used as building units and macroscopic Janus film structure ensure high photosensitive dual-anisotropic conduction and excellent fluorescence in PDCJF. The two-dimensional (2D) PDCJF is rolled to obtain three-dimensional (3D) Janus-type tubes and 2D plus 3D complete flag-like structures with exceptional multi-functionalities. The new findings can strongly guide in developing advanced multi-functional nanostructures.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jcis.2021.05.141DOI Listing
May 2021

Discovery of Natural Products Targeting NQO1 via an Approach Combining Network-Based Inference and Identification of Privileged Substructures.

J Chem Inf Model 2021 05 6;61(5):2486-2498. Epub 2021 May 6.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.

NAD(P)H:quinone oxidoreductase 1 (NQO1) has been shown to be a potential therapeutic target for various human diseases, such as cancer and neurodegenerative disorders. Recent advances in computational methods, especially network-based methods, have made it possible to identify novel compounds for a target with high efficiency and low cost. In this study, we designed a workflow combining network-based methods and identification of privileged substructures to discover new compounds targeting NQO1 from a natural product library. According to the prediction results, we purchased 56 compounds for experimental validation. Without the assistance of privileged substructures, 31 compounds (31/56 = 55.4%) showed IC < 100 μM, and 11 compounds (11/56 = 19.6%) showed IC < 10 μM. With the assistance of privileged substructures, the two success rates were increased to 61.8 and 26.5%, respectively. Seven natural products further showed inhibitory activity on NQO1 at the cellular level, which may serve as lead compounds for further development. Moreover, network analysis revealed that osthole may exert anticancer effects against multiple cancer types by inhibiting not only carbonic anhydrases IX and XII but also NQO1. Our workflow and computational methods can be easily applied in other targets and become useful tools in drug discovery and development.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.jcim.1c00260DOI Listing
May 2021

Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches.

J Chem Inf Model 2021 05 26;61(5):2475-2485. Epub 2021 Apr 26.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug-pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug-pathway associations based on substructure-drug-pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug-pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug-pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.jcim.1c00009DOI Listing
May 2021

Iron-Catalyzed Regio- and Stereoselective Hydrosilylation of 1,3-Enynes To Access 1,3-Dienylsilanes.

Org Lett 2021 03 10;23(6):2375-2379. Epub 2021 Mar 10.

Chang-Kung Chuang Institute, East China Normal University, Shanghai 200062, China.

A regio- and stereoselective hydrosilylation of 1,3-enynes with primary and secondary silanes to access 1,3-dienylsilanes is accomplished by employing an iron precatalyst bearing iminopyridine-oxazoline (IPO) ligand. The hydrosilylation proceeds via -addition of a Si-H bond to the alkyne group of 1,3-enynes, incorporating the silyl group at the site proximal to the alkene. The reaction features mild conditions, broad substrate scope, and good functional group tolerance. The synthetic utility was demonstrated by gram-scale reactions and further transformations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.orglett.1c00670DOI Listing
March 2021

Assessment of CYP2C9 Structural Models for Site of Metabolism Prediction.

ChemMedChem 2021 Jun 18;16(11):1754-1763. Epub 2021 Mar 18.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 20023, P. R. China.

Structure-based prediction of a compound's potential sites of metabolism (SOMs) mediated by cytochromes P450 (CYPs) is highly advantageous in the early stage of drug discovery. However, the accuracy of the SOMs prediction can be influenced by several factors. CYP2C9 is one of the major drug-metabolizing enzymes in humans and is responsible for the metabolism of ∼13 % of clinically used drugs. In this study, we systematically evaluated the effects of protein crystal structure models, scoring functions, heme forms, conserved active-site water molecules, and protein flexibility on SOMs prediction of CYP2C9 substrates. Our results demonstrated that, on average, ChemScore and GlideScore outperformed four other scoring functions: Vina, GoldScore, ChemPLP, and ASP. The performance of the crystal structure models with pentacoordinated heme was generally superior to that of the hexacoordinated iron-oxo heme (referred to as Compound I) models. Inclusion of the conserved active-site water molecule improved the prediction accuracy of GlideScore, but reduced the accuracy of ChemScore. In addition, the effect of the conserved water on SOMs prediction was found to be dependent on the receptor model and the substrate. We further found that one of snapshots from molecular dynamics simulations on the apo form can improve the prediction accuracy when compared to the crystal structural model.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/cmdc.202000964DOI Listing
June 2021

Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.

Int J Mol Sci 2021 Jan 19;22(2). Epub 2021 Jan 19.

College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun 130012, China.

Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/ijms22020939DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832895PMC
January 2021

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods.

J Appl Toxicol 2021 Jan 20. Epub 2021 Jan 20.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.

Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone. Therefore, there is an urgent need to develop models that could accurately predict the mitochondrial toxicity of chemicals. In this paper, suitable data were obtained from literature and databases first. Then nine types of fingerprints were used to characterize these compounds. Finally, different algorithms were used to build models. Meanwhile, the applicability domain of the prediction models was defined. We have also explored the structural alerts of mitochondrial toxicity, which would be helpful for medicinal chemists to better predict mitochondrial toxicity and further optimize lead compounds.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jat.4141DOI Listing
January 2021

In silico prediction of chemical acute contact toxicity on honey bees via machine learning methods.

Toxicol In Vitro 2021 Apr 11;72:105089. Epub 2021 Jan 11.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China. Electronic address:

In recent years, the decline of honey bees and the collapse of bee colonies have caught the attention of ecologists, and the use of pesticides is one of the main reasons for the decline. Therefore, ecological risk assessment of pesticides is essential and necessary. In silico tools, such as QSAR models can play an important role in predicting physicochemical and biological properties of chemicals. In this study, a total of 54 classification models were developed by combination of 6 machine learning methods along with 9 kinds of molecular fingerprints based on the experimental honey bees acute contact toxicity data (LD) of 676 structurally diverse pesticides. The best model proposed was SVM algorithm combined with CDK extended fingerprint. The analysis of the applicability domain of the model successfully excluded some extreme molecules. Additionally, 9 structural alerts about honey bees acute contact toxicity were identified by information gain and substructure frequency analysis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.tiv.2021.105089DOI Listing
April 2021

Insights into the Molecular Mechanisms of Liuwei Dihuang Decoction via Network Pharmacology.

Chem Res Toxicol 2021 Jan 17;34(1):91-102. Epub 2020 Dec 17.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target network-based inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A "target-(pathway)-target" (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.chemrestox.0c00359DOI Listing
January 2021

MetaADEDB 2.0: a comprehensive database on adverse drug events.

Bioinformatics 2020 Nov 18. Epub 2020 Nov 18.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

Summary: MetaADEDB is an online database we developed to integrate comprehensive information on adverse drug events (ADEs). The first version of MetaADEDB was released in 2013 and has been widely used by researchers. However, it has not been updated for more than seven years. Here, we reported its second version by collecting more and newer data from the U.S. FDA Adverse Event Reporting System (FAERS) and Canada Vigilance Adverse Reaction Online Database, in addition to the original three sources. The new version consists of 744 709 drug-ADE associations between 8498 drugs and 13 193 ADEs, which has an over 40% increase in drug-ADE associations compared to the previous version. Meanwhile, we developed a new and user-friendly web interface for data search and analysis. We hope that MetaADEDB 2.0 could provide a useful tool for drug safety assessment and related studies in drug discovery and development.

Availability And Implementation: The database is freely available at: http://lmmd.ecust.edu.cn/metaadedb/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa973DOI Listing
November 2020

Ruthenium-Catalyzed Hydrodefluorination with Silane as the Directing and Reducing Group.

Org Lett 2020 Dec 23;22(23):9298-9302. Epub 2020 Nov 23.

State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200032, China.

We describe herein an efficient and selective Ru-catalyzed intramolecular HDF directed by a silyl group, which is readily installed, and removable and transformable following the HDF reaction. The hydrosilyl group in polyfluoroaryl silane acts not only as the directing group but also as the internal reductant, enabling precise control of the -selectivity and avoiding overdefluorination. Mechanistic studies reveal a plausible catalytic cycle involving a Ru(IV)-aryne intermediate.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.orglett.0c03530DOI Listing
December 2020

Insights into the mechanism of Arnebia euchroma on leukemia via network pharmacology approach.

BMC Complement Med Ther 2020 Oct 27;20(1):322. Epub 2020 Oct 27.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.

Background: Arnebia euchroma (A. euchroma) is a traditional Chinese medicine (TCM) used for the treatment of blood diseases including leukemia. In recent years, many studies have been conducted on the anti-tumor effect of shikonin and its derivatives, the major active components of A. euchroma. However, the underlying mechanism of action (MoA) for all the components of A. euchroma on leukemia has not been explored systematically.

Methods: In this study, we analyzed the MoA of A. euchroma on leukemia via network pharmacology approach. Firstly, the chemical components and their concentrations in A. euchroma as well as leukemia-related targets were collected. Next, we predicted compound-target interactions (CTIs) with our balanced substructure-drug-target network-based inference (bSDTNBI) method. The known and predicted targets of A. euchroma and leukemia-related targets were merged together to construct A. euchroma-leukemia protein-protein interactions (PPIs) network. Then, weighted compound-target bipartite network was constructed according to combination of eight central attributes with concentration information through Cytoscape. Additionally, molecular docking simulation was performed to calculate whether the components and predicted targets have interactions or not.

Results: A total of 65 components of A. euchroma were obtained and 27 of them with concentration information, which were involved in 157 targets and 779 compound-target interactions (CTIs). Following the calculation of eight central attributes of targets in A. euchroma-leukemia PPI network, 37 targets with all central attributes greater than the median values were selected to construct the weighted compound-target bipartite network and do the KEGG pathway analysis. We found that A. euchroma candidate targets were significantly associated with several apoptosis and inflammation-related biological pathways, such as MAPK signaling, PI3K-Akt signaling, IL-17 signaling, and T cell receptor signaling pathways. Moreover, molecular docking simulation demonstrated that there were eight pairs of predicted CTIs had the strong binding free energy.

Conclusions: This study deciphered that the efficacy of A. euchroma in the treatment of leukemia might be attributed to 10 targets and 14 components, which were associated with inhibiting leukemia cell survival and inducing apoptosis, relieving inflammatory environment and inhibiting angiogenesis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12906-020-03106-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7590697PMC
October 2020

Local structure modulation of Mn-doped NaSiGeF red phosphors for enhancement of emission intensity, moisture resistance, thermal stability and application in warm pc-WLEDs.

Dalton Trans 2020 Oct;49(39):13805-13817

Key Laboratory of Applied Chemistry and Nanotechnology at Universities of Jilin Province, Changchun University of Science and Technology, Changchun 130022, P. R. China.

Nowadays, the development of Mn4+-activated fluoride phosphors with efficient water and thermal stabilities continues to pose a huge challenge with regard to prolonging the service life and stabilizing the light output for phosphor-converted white light-emitting diodes (pc-WLEDs). Therefore, the synthesis strategy of simple crystal structure optimization is proposed to realize simultaneously the high hydrophobic and thermal stabilities of fluoride phosphors. Herein, Mn4+-doped Na2Si1-yGeyF6 red phosphors are successfully synthesized by a simple coprecipitation method. Satisfactorily, the optimization of Ge4+ and Mn4+ concentrations successfully enhances the luminescence intensity of the original phosphor (Na2SiF6:Mn4+) and an obvious red shift can be found. Moreover, the CIE coordinates of red light show that the phosphor has low correlated color temperature and excellent color purity. Based on excitation and emission spectra, the crystal field strength (Dq), Racah parameters (B and C) and nephelauxetic ratio (β1) show that a new Na2Si0.5Ge0.5F6 matrix can meet the high requirements of the crystal field environment when Mn4+ becomes the fluorescence center. Interestingly, the local structure modulation stabilizes the state of existence of Mn4+ in the matrix and enhances the moisture resistance of the phosphor. In addition, the as-prepared Na2Si0.5Ge0.5F6:Mn4+ phosphor possesses admirable thermal quenching behavior and color stability at high temperature. More importantly, low correlated color temperature (3408 K), high color rendering index (89.4) and preeminent luminous efficiency (112.89 Im W-1) are achieved using the YAG:Ce3+-Na2Si0.5Ge0.5F6:0.06Mn4+ system as color converters for warm pc-WLEDs. The work provides a new insight into the construction of red phosphors with favorable water and thermal stabilities for warm pc-WLEDs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1039/d0dt02935aDOI Listing
October 2020

Smooth muscle 22α deficiency impairs oxytocin-induced uterine contractility in mice at full-term pregnancy.

Biochem Biophys Res Commun 2020 09 28;529(4):884-889. Epub 2020 Jul 28.

Department of Biochemistry and Molecular Biology, College of Basic Medicine, Key Laboratory of Medical Biotechnology of Hebei Province, Hebei Medical University, Shijiazhuang, China. Electronic address:

Smooth muscle 22α (SM22α, namely Transgelin), as an actin-binding protein, regulates the contractility of vascular smooth muscle cells (VSMCs) by modulation of the stress fiber formation. However, little is known about the roles of SM22α in the regulation of uterine contraction during parturition. Here, we showed that contraction in response to oxytocin (OT) was significantly decreased in the uterine muscle strips from SM22α knockout (Sm22α-KO) mice, especially at full-term pregnancy, which may be resulted from impaired formation of stress fibers. Furthermore, serious mitochondrial damage such as the mitochondrial swelling, cristae disruption and even disappearance were observed in the myometrium of Sm22α-KO mice at full-term pregnancy, eventually resulting in the collapse of mitochondrial membrane potential and impairment in ATP synthesis. Our data indicate that SM22α is necessary to maintain uterine contractility at delivery in mice, and acts as a novel target for preventive or therapeutic manipulation of uterine atony during parturition.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bbrc.2020.05.220DOI Listing
September 2020

NetInfer: A Web Server for Prediction of Targets and Therapeutic and Adverse Effects via Network-Based Inference Methods.

J Chem Inf Model 2020 08 4;60(8):3687-3691. Epub 2020 Aug 4.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.

In this study, we developed a web server named NetInfer for prediction of targets and therapeutic and adverse effects via network-based inference methods. Compared with our previously developed standalone version of NetInfer, this web server provides a user-friendly interface. With the web server, users can easily predict potential target proteins, microRNAs, Anatomical Therapeutic Chemical (ATC) classification codes, or adverse drug events for small molecules of their interests in a few steps. Most of the prediction models were constructed on the basis of our previous studies, where those models have been evaluated systematically and demonstrated high performance. The high-quality models can generate accurate predictions. As a case study, we predicted ATC codes and target proteins for several drugs. The predicted therapeutic effects of these drugs on cardiovascular diseases and their potential molecular mechanisms were validated by the literature. This successful case study demonstrated that our web server would be a powerful tool in drug repositioning and systems pharmacology. The web server of NetInfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.jcim.0c00291DOI Listing
August 2020

prediction of chemical neurotoxicity using machine learning.

Toxicol Res (Camb) 2020 Jun 29;9(3):164-172. Epub 2020 Apr 29.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Rd, Xuhui District, Shanghai 200237, China.

Neurotoxicity is one of the main causes of drug withdrawal, and the biological experimental methods of detecting neurotoxic toxicity are time-consuming and laborious. In addition, the existing computational prediction models of neurotoxicity still have some shortcomings. In response to these shortcomings, we collected a large number of data set of neurotoxicity and used PyBioMed molecular descriptors and eight machine learning algorithms to construct regression prediction models of chemical neurotoxicity. Through the cross-validation and test set validation of the models, it was found that the extra-trees regressor model had the best predictive effect on neurotoxicity ([Formula: see text] = 0.784). In addition, we get the applicability domain of the models by calculating the standard deviation distance and the lever distance of the training set. We also found that some molecular descriptors are closely related to neurotoxicity by calculating the contribution of the molecular descriptors to the models. Considering the accuracy of the regression models, we recommend using the extra-trees regressor model to predict the chemical autonomic neurotoxicity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/toxres/tfaa016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7329181PMC
June 2020

Detecting Cancer Survival Related Gene Markers Based on Rectified Factor Network.

Front Bioeng Biotechnol 2020 23;8:349. Epub 2020 Apr 23.

Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.

Detecting gene sets that serve as biomarkers for differentiating patient survival groups may help diagnose diseases robustly and develop multi-gene targeted therapies. However, due to the exponential growth of search space imposed by gene combinations, the performance of existing methods is still far from satisfactory. In this study, we developed a new method called BISG (BIclustering based Survival-related Gene sets detection) based on a rectified factor network (RFN) model, which allows efficiently biclustering gene subsets. By correlating genes in each significant bicluster with patient survival outcomes using a log-rank test and multi-sampling strategy, multiple survival-related gene sets can be detected. We applied BISG on three different cancer types, and the resulting gene sets were tested as biomarkers for survival analyses. Secondly, we systematically analyzed 12 different cancer datasets. Our analysis shows that the genes in all the survival-related gene sets are mainly from five gene families: microRNA protein coding host genes, zinc fingers C2H2-type, solute carriers, CD (cluster of differentiation) molecules, and ankyrin repeat domain containing genes. Moreover, we found that they are mainly enriched in heme metabolism, apoptosis, hypoxia and inflammatory response-related pathways. We compared BISG with two other methods, GSAS and IPSOV. Results show that BISG can better differentiate patient survival groups in different datasets. The identified biomarkers suggested by our study provide useful hypotheses for further investigation. BISG is publicly available with open source at https://github.com/LingtaoSu/BISG.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fbioe.2020.00349DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212422PMC
April 2020

Computational Insights into Molecular Activation and Positive Cooperative Mechanisms of FFAR1 Modulators.

J Chem Inf Model 2020 06 8;60(6):3214-3230. Epub 2020 May 8.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

Free fatty acid receptor 1 (FFAR1), a member of class A in G-protein-coupled receptors (GPCRs), is a promising antidiabetic target. The crystal structure of FFAR1 revealed that one agonist (MK-8666) binds to the extracellular vestibule of this receptor, while another (AP8) occupies the surface pocket between transmembrane (TM) helices TM4 and TM5. In this study, we performed 1 μs unbiased molecular dynamics (MD) simulation on each of five systems, to uncover why two ligands in completely different sites both serve as agonists and how they exert a positive synergistic effect together. They are two agonist-bound systems (FFAR1_MK-8666 and FFAR1_AP8), a ternary complex system FFAR1_MK-8666_AP8, an antagonist-bound system (FFAR1_15i), and an unliganded (apo) system, among which the antagonist 15i-bound and apo systems were used as controls. The results showed that Y91 played a pivotal role in the activation process of FFAR1. The agonist could disrupt the Y91-centered residue interaction network within protein, whereas the antagonist could stabilize the network. Furthermore, our simulations revealed that the hydrophobic layer amino acid residues next to the transmission switch (CWXP) formed a gate and could open only upon agonist activation, which might exert an important role in the formation of water pathway. These results would be helpful for elucidating the molecular activation mechanism of FFAR1 and provide insights into the design and discovery of novel allosteric agonists of FFAR1 for the treatment of type 2 diabetes mellitus (T2DM).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.jcim.0c00030DOI Listing
June 2020

Drug repositioning by prediction of drug's anatomical therapeutic chemical code via network-based inference approaches.

Brief Bioinform 2021 Mar;22(2):2058-2072

East China University of Science and Technology, Shanghai, China.

Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug's anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd- and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 ± 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen-glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bbaa027DOI Listing
March 2021

In Silico Prediction of Metabolic Epoxidation for Drug-like Molecules via Machine Learning Methods.

Mol Inform 2020 08 31;39(8):e1900178. Epub 2020 Mar 31.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.

Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 : 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.873 and 0.944 for the test set, 0.838 and 0.987 for the external validation set, respectively. The models built in this study could accurately predict whether a compound will undergo epoxidation and which part is most susceptible to epoxidation, which is of great significance for drug design.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/minf.201900178DOI Listing
August 2020

Computational Insight into the Allosteric Activation Mechanism of Farnesoid X Receptor.

J Chem Inf Model 2020 03 4;60(3):1540-1550. Epub 2020 Mar 4.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

The farnesoid X receptor (FXR) is a bile acid-sensing transcription factor with indispensable roles in regulating metabolic processes. Nowadays, FXR has become a highly promising drug target for severe liver disorders, especially nonalcoholic steatohepatitis (NASH). A recent study showed that imatinib and its analogues were able to allosterically enhance agonist-induced FXR activation and its target gene expression. However, the allosteric modulation mechanism of FXR by these compounds remains unclear. In this work, the most effective imatinib analogue, P16, was used as a probe to explore this issue by computational approaches. Our results identified one potential allosteric site surrounded by residues Ile335, Phe336, Lys338, Glu339, Leu340, and Leu348, which could efficiently accommodate P16. In addition, the long-time molecular dynamics simulations indicated that the binding of P16 could significantly decrease the fluctuation of the co-activator and enhance the communications between the endogenous ligand chenodeoxycholic acid (CDCA) and FXR. By analyzing the residue interaction network, we observed two unique communication pathways connecting P16 and CDCA through three key residues, Arg331, Ser332, and Phe336. The communications of network organization in the P16-bound complex may allow the synergistic effect of the two compounds via robust signal transmission between the binding sites and global network bridges, which coordinate allosteric transitions and modulate the receptor activity. Our study offers insights into the allosteric modulation occurring in FXR and would be helpful for discovery of new allosteric modulators targeting FXR for further clinical research.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.jcim.9b00914DOI Listing
March 2020

Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery.

Chem Res Toxicol 2020 06 5;33(6):1312-1322. Epub 2020 Mar 5.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.

Structural alerts are a simple and easy way to identify toxic compounds being widely used in environmental toxicology research and drug discovery. With the emergence of big data techniques in recent years and their applications in chemistry and toxicology, computational approaches have become a promising method to identify structural alerts. In this Review, we describe the recent progress in computational methods for identification of structural alerts and their applications in toxicology. Two major computational approaches, namely frequency analysis and interpretable machine learning models, are reviewed. Recent studies have shown that both approaches are superior to expert systems with respect to predictive capability. Methodologies for defining the applicability domain of such approaches are also reviewed, with their importance stemming from their ability to not only improve the predictive performance of structural alert models but also ensure the confidence of a prediction. In addition to toxicity prediction, structural alerts could be also used to explain quantitative structure-activity relationship models and guide lead optimization in drug discovery. Nevertheless, there are still some challenges to be solved, such as how to address the co-existence of several structural alerts in one molecule, how to directly compare computationally derived structural alerts with expert systems, and how to explore new mechanisms of toxicity.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.chemrestox.0c00006DOI Listing
June 2020

One-step hydrothermal synthesis of Ni-Co sulfide on Ni foam as a binder-free electrode for lithium-sulfur batteries.

J Colloid Interface Sci 2020 Apr 26;565:378-387. Epub 2019 Dec 26.

School of Chemistry and Environmental Engineering, Changchun University of Science and Technology, Changchun 130022, PR China. Electronic address:

NiCoS, CoS/NiS and Ni-Co sulfide grown on Ni-foam were successfully synthesized with a simple one-step hydrothermal method via adjusting the mass fraction of cobalt and sulphur source, forming a free-standing advanced hybrid electrode for Li-S battery. Interestingly, compared with typical synthesis methods of Ni-Co sulfide, this new synthetic method supplies nickel source through Ni-foam rather than soluble inorganic salt and avoid destroying the original structure in the process of secondary hydrothermal reaction. With the integrity of the whole mechanical structure of the hosts, the hybrid electrode behave strong chemical bonding for polysulfides and superior electrocatalytic activity for accelerating the polysulfides redox reactions. The results revealed that foam-like structure of S/[email protected] sulfide (S/[email protected]) electrode delivers the highest capacity of 1352.36 mAh g at 1 C after 10 cycles and the initial capacity of S/[email protected] electrode is 1920.28 mAh g at 0.1 C. The results offer a facile and promising engineering strategy to achieve high sulfur utilization.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jcis.2019.12.112DOI Listing
April 2020

Prediction of Human Renal Clearance of Compounds Using Quantitative Structure-Pharmacokinetic Relationship Models.

Chem Res Toxicol 2020 02 28;33(2):640-650. Epub 2020 Jan 28.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China.

Renal clearance (CLr) plays an essential role in the elimination of drugs. In this study, 636 compounds were obtained from various sources to develop models for the prediction of CLr. Stepwise multiple linear regression and random forest regression methods were employed to build global models and local models according to ionization state or net elimination pathways. The local models toward compounds undergoing different net elimination pathways showed good predictive power: the geometric mean fold error was close to 2, indicating the clearance of most compounds could be predicted within a 2-fold error range. Six classification methods were used to construct classification models. However, the performance of these classification models was less than satisfactory, and the mean accuracy of the top five models in test sets was 0.65. Moreover, qualitative analysis of physicochemical profiles between compounds undergoing different net elimination pathways revealed that compounds with higher lipophilicity tended to be reabsorbed more easily and showed lower CLr, while compounds with higher values of polar descriptors tended to secrete more easily and showed higher CLr.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1021/acs.chemrestox.9b00447DOI Listing
February 2020

Prediction of the allergic mechanism of haptens via a reaction-substructure-compound-target-pathway network system.

Toxicol Lett 2019 Dec 30;317:68-81. Epub 2019 Sep 30.

Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China. Electronic address:

Skin sensitization, frequently leading to allergic contact dermatitis (ACD), is authenticated to be a significant endpoint in the field of drug discovery and cosmetics. The initiation of ACD, also known as the skin sensitization mechanism, has been documented as an adverse outcome pathway (AOP), which can be studied experimentally and computationally. In this study, we collected 154 haptens and applied systems toxicology methods to develop a reaction-substructure-compound- target-pathway network system. For the collected haptens, their key substructures were identified and associated with their protein binding reactions. The targets of haptens, including the known targets collected from four databases and the potential targets predicted via our balanced substructure-drug-target network-based inference (bSDTNBI) method, were matched to skin proteins to obtain skin targets. The dermatitis-related pathways were enriched and were subject to literature verification. The network system we developed can be applied to predict the reactions, targets and pathways of new haptens, which contributed to evaluating chemical safety and optimizing chemical structures. The study of skin sensitization mechanism is helpful for understanding the skin immunity and resisting ACD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.toxlet.2019.09.019DOI Listing
December 2019

Identifying protein complexes based on an edge weight algorithm and core-attachment structure.

BMC Bioinformatics 2019 Sep 14;20(1):471. Epub 2019 Sep 14.

School of International Economics, China Foreign Affairs University, 24 Zhanlanguan Road, Xicheng District, Beijing, 100037, China.

Background: Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins.

Results: In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy.

Conclusions: In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-019-3007-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744658PMC
September 2019

Synthesis of multifunctional rare-earth fluoride/Ag nanowire nanocomposite for efficient therapy of cancer.

Mater Sci Eng C Mater Biol Appl 2019 Nov 3;104:109940. Epub 2019 Jul 3.

Key Laboratory of Applied Chemistry and Nanotechnology at Universities of Jilin Province, Changchun University of Science and Technology, Changchun 130022, PR China. Electronic address:

Well-dispersed Ag nanowires and PVP-modified BaGdF: Yb, Er spherical nanoparticles were prepared by simple solvothermal and hydrothermal method, and they were further combined to obtain photo-thermal-magnetic multifunctional Ag/BaGdF: Yb, Er nanocomposites. Under NIR laser irradiation, monodispersed rare-earth fluoride BaGdF: Yb, Er in nanocomposite exhibit good upconversion fluorescent. Meanwhile, under the action of an external magnetic field, the nanocomposite also exhibits good magnetic properties and excellent contrast efficiency by CT/MR imaging. The NCs possess good structure and photothermal stability at multiple cycles due to that Ag nanowires are modified by polyvinyl pyrrolidone and sodium citrate. The biocompatibility and low toxicity of NCs are also remarkable. Importantly, the unique linear morphology of Ag particles can achieve high efficiency conversion between light and heat. Furthermore, in vitro tests also confirm the high efficiency of photothermal therapy for cancer cells.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.msec.2019.109940DOI Listing
November 2019
-->