-
[show abstract]
[hide abstract]
ABSTRACT: The transcriptional repressor Rex plays key roles in modulating respiratory gene expression. It senses the redox poise of the NAD(H) pool. Rex from Streptomyces rimosus (Sr-Rex) is a newly identified protein. Its structure and complex with substrates are not determined yet. In this study, the three-dimensional (3D) structural models of Sr-Rex dimer and its complex with cofactors were constructed by homology modeling. The stability of the constructed Sr-Rex models and the detailed interactions between Sr-Rex and cofactors were further investigated by molecular dynamics simulations. The results demonstrated that the conformation of Sr-Rex changed a lot when binding with the reduced NADH or oxidized NAD(+). Once binding with NADH, the Sr-Rex dimer displayed an opener conformation, which would weaken the interaction of Sr-Rex with Rex operator DNA (ROP). Key residues responsible for the binding were then identified. The computational results were consistent with experimental results, and hence provided insights into the molecular mechanism of Sr-Rex binding with ROP and NADH/NAD(+), which might be helpful for the development of biosensor.
Journal of Molecular Modeling 04/2013; · 1.80 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Prediction of polypharmacological profiles of drugs enable us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we described a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effects and therapeutic space. Based on our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connected 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882±0.011 averaged from 100 simulated times tests of 10-fold cross validation for DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for 7 approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.
Journal of Chemical Information and Modeling 03/2013; · 4.68 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Adverse drug events (ADEs) mean the harms associated with uses of given medications at normal dosages, which is crucial for a drug to be approved in clinic use or continue to stay in market. Many ADEs are not identified in clinical trials until the drug is approved for use in clinic, which results in adverse morbidity and mortality. To date millions of ADEs have been reported around the world. How to avoid or reduce ADEs is an important issue for drug discovery and development. Here, we reported a comprehensive database of adverse drug events (namely MetaADEDB), which included more than 52 thousands of drug-ADE associations among 3059 unique compounds (including 1330 drugs) and 13,200 ADE items by data integration and text mining. All compounds and ADEs were annotated with the most commonly used concepts defined in Medical Subject Headings (MeSH). Meanwhile, a computational method, namely phenotypic network inference model (PNIM), was developed for prediction of potential ADEs based on the database. The area under the receive operating characteristic curve (AUC) is more than 0.9 by 10-fold cross validation, while the AUC value was 0.912 for an external validation set extracted from the US-FDA Adverse Events Reporting System, which indicated that the prediction capability of the method was reliable. MetaADEDB is accessible free of charge at http://www.lmmd.org/online_services/metaadedb/. The database and the method provide us a useful tool to search for known side effects or predict potential side effects for a given drug or a compound.
Journal of Chemical Information and Modeling 03/2013; · 4.68 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Farnesoid X receptor (FXR, NR1H4) is a member of nuclear receptor superfamily, which plays important roles in bile acid homeostasis, lipoprotein and glucose metabolism, and hepatic regeneration. GW4064 is a potent and selective FXR agonist and has become a tool compound to probe the physiological functions of FXR. Until now, the mechanism of GW4064 entering and leaving the FXR pocket is still poorly understood. Here, we report a computational study of GW4064 unbinding pathways from FXR by using several molecular dynamics (MD) simulation techniques. Based on the crystal structure of FXR in complex with GW4064, conventional MD was first used to refine the binding and check the stability of GW4064 in the FXR pocket. Random acceleration MD simulations were then performed to explore the possible unbinding pathways of GW4064 from FXR. Four main pathway clusters were found, among which three sub-pathways, namely Path 2A, 2B and 1B, were observed most frequently. Multiple steered MD simulations were further employed to estimate the maximum rupture force and the sum of the forces and to characterize the intermediate states of the ligand unbinding process. By comparing the average force profiles and structural changes, Path 2A and 2B were identified to be the most favorable unbinding pathways. The former is located between the H1-H2 loop and the H5-H6 loop, and the latter is located in the cleft formed by the H5-H6 loop, H6 and H7. Moreover, the residues lining the pathways were analyzed for their roles in ligand unbinding. Based on our results, the possible structural modification strategies on GW4064 were also proposed.
Journal of Chemical Information and Modeling 10/2012; · 4.68 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play key roles in the discovery/development of drugs, pesticides, food additives, consumer products and industrial chemicals. This information is especially useful when to conduct environmental and human hazard assessment. The most critical rate limiting step in the chemical safety assessment workflow is the availability of high quality data. This paper describes an ADMET structure-activity relationship database, abbreviated as admetSAR. It is an open source, text and structure searchable, and continually updated database that collects curates and manages available ADMET-associated properties data from published literatures. In admetSAR, over 210,000 ADMET annotated data points for more than 96,000 unique compounds with 45 kinds of ADMET-associated properties, proteins, species or organisms have been carefully curated from a large number of diverse literatures. The database provides a user-friendly interface to query a specific chemical profile, using either CAS registry number, common name or structure similarity. In addition, the database includes 22 qualitative classification and 5 quantitative regression models with highly predictive accuracy, allowing to estimate ecological/mammalian ADMET properties for novel chemicals. The admetSAR is accessible free of charge at http://www.admetexp.org.
Journal of Chemical Information and Modeling 10/2012; · 4.68 Impact Factor
-
Guoping Hu,
Xi Li,
Xuan Zhang,
Yaozong Li,
Lei Ma,
Liu-Meng Yang,
Guixia Liu, Weihua Li,
Jin Huang,
Xu Shen,
Lihong Hu,
Yong-Tang Zheng,
Yun Tang
[show abstract]
[hide abstract]
ABSTRACT: This study aims to identify inhibitors that bind at the interface of HIV-1 integrase (IN) and human LEDGF/p75, which represents a novel target for anti-HIV therapy. To date, only a few such inhibitors have been reported. Here structure-based virtual screening was performed to search for the inhibitors from an in-house library of natural products and their derivatives. Among the 38 compounds selected by our strategy, 18 hits were discovered. The two most potent inhibitors showed IC(50) values at 0.32 and 0.26 μM, respectively. Three compounds were subsequently selected for anti-HIV assays, among which (E)-3-(2-chlorophenyl)-1-(2,4-dihydroxyphenyl)prop-2-en-1-one (NPD170) showed the highest antiviral activity (EC(50) = 1.81 μM). The antiviral mechanism of these compounds was further explored, and the results validated that the compounds interrupted the binding of transfected IN to endogenous LEDGF/p75. These findings could be helpful for anti-HIV drug discovery.
Journal of Medicinal Chemistry 10/2012; · 4.80 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Farnesol X receptor (FXR) is a member of the metabolic nuclear receptor (NR) superfamily of regulatory proteins. FXR was recognized to be a transcriptional sensor for bile acids, and now it has been shown that activating FXR has important roles in controlling bile acid homeostasis, lipoprotein and glucose metabolism, and hepatic regeneration. For the sake of discovering new, potent non-steroidal FXR ligands, we have established a virtual screening workflow by using Phase Shape and induced fit docking (IFD). Phase shape was performed based on a combination of shape-only and atom types or pharmacophore modes. The results indicated that the pharmacophore mode yielded the best result for our system. The best receptor model was chosen by evaluating the cross-IFD models induced by three crystal structures 3DCT, 3FLI and 3OKI. The Enamine database was screened by the proposed workflow and 50 molecules were selected and purchased for bioassays. Among them, two compounds were found to be the new, potent FXR ligands in cell-based assay.
Bioorganic & medicinal chemistry letters 09/2012; 22(22):6848-53. · 2.65 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.
PLoS Computational Biology 05/2012; 8(5):e1002503. · 5.22 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Human microsomal cytochrome P450 2E1 (CYP2E1) can oxidize not only low molecular weight xenobiotic compounds such as ethanol, but also many endogenous fatty acids. The crystal structure of CYP2E1 in complex with indazole reveals that the active site is deeply buried into the protein center. Thus, the unbinding pathways and associated unbinding mechanisms remain elusive. In this study, random acceleration molecular dynamics simulations combined with steered molecular dynamics and potential of mean force calculations were performed to identify the possible unbinding pathways in CYP2E1. The results show that channel 2c and 2a are most likely the unbinding channels of CYP2E1. The former channel is located between helices G and I and the B-C loop, and the latter resides between the region formed by the F-G loop, the B-C loop and the β1 sheet. Phe298 and Phe478 act as the gate keeper during indazole unbinding along channel 2c and 2a, respectively. Previous site-directed mutagenesis experiments also supported these findings.
PLoS ONE 01/2012; 7(3):e33500. · 4.09 Impact Factor
-
Chinese Journal of Chemistry 01/2012; 30(12):2752-2758. · 0.75 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Cytochrome P450 inhibitory promiscuity of a drug has potential effects on the occurrence of clinical drug-drug interactions. Understanding how a molecular property is related to the P450 inhibitory promiscuity could help to avoid such adverse effects. In this study, an entropy-based index was defined to quantify the P450 inhibitory promiscuity of a compound based on a comprehensive data set, containing more than 11,500 drug-like compounds with inhibition against five major P450 isoforms, 1A2, 2C9, 2C19, 2D6, and 3A4. The results indicated that the P450 inhibitory promiscuity of a compound would have a moderate correlation with molecular aromaticity, a minor correlation with molecular lipophilicity, and no relations with molecular complexity, hydrogen bonding ability, and TopoPSA. We also applied an index to quantify the susceptibilities of different P450 isoforms to inhibition based on the same data set. The results showed that there was a surprising level of P450 inhibitory promiscuity even for substrate specific P450, susceptibility to inhibition follows the rank-order: 1A2 > 2C19 > 3A4 > 2C9 > 2D6. There was essentially no correlation between P450 inhibitory potency and specificity and minor negative trade-offs between P450 inhibitory promiscuity and catalytic promiscuity. In addition, classification models were built to predict the P450 inhibitory promiscuity of new chemicals using support vector machine algorithm with different fingerprints. The area under the receiver operating characteristic curve of the best model was about 0.9, evaluated by 5-fold cross-validation. These findings would be helpful for understanding the mechanism of P450 inhibitory promiscuity and improving the P450 inhibitory selectivity of new chemicals in drug discovery.
Journal of Chemical Information and Modeling 08/2011; 51(10):2482-95. · 4.68 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The human cytochrome P450 2A13 (CYP2A13) and P450 2A6 (CYP2A6) are 94% identical in amino acid sequence, but they metabolize many substrates with different efficiencies. Previous experimental results have shown that CYP2A13 exhibited catalytic activity that was more than 300-fold higher than CYP2A6 toward 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), a carcinogen present in tobacco products. At present, however, the structural determinants accounting for the differential catalytic activities of these two isozymes toward NNK remain unclear. In the present study, molecular docking combined with molecular dynamics simulation and binding free energy calculation was performed to investigate the above issue. The results demonstrate that NNK was able to form a hydrogen bond with Asn297 in either CYP2A13 or CYP2A6. The hydrogen-bond acceptor was the pyridine nitrogen of NNK in the CYP2A13 complex, but it changed to the carbonyl oxygen in the CYP2A6 complex. NNK interacted with the residues in helix I and the K-β2 loop in CYP2A13, whereas it preferred to contact with the phenylalanine cluster in CYP2A6. The residues in helix I and the K-β2 loop of CYP2A13 played a vital role in keeping NNK in a more stable binding state. The binding free energies calculated by MM-GBSA were in agreement with the experimental results.
Journal of molecular graphics & modelling 05/2011; 30:1-9. · 2.17 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP) inhibition is an important consideration, especially, during the research phase of drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP isoform. In this study, inhibitor predicting models were developed for five major CYP isoforms, namely 1A2, 2C9, 2C19, 2D6, and 3A4, using a combined classifier algorithm on a large data set containing more than 24,700 unique compounds, extracted from PubChem. The combined classifiers algorithm is an ensemble of different independent machine learning classifiers including support vector machine, C4.5 decision tree, k-nearest neighbor, and naïve Bayes, fused by a back-propagation artificial neural network (BP-ANN). All developed models were validated by 5-fold cross-validation and a diverse validation set composed of about 9000 diverse unique compounds. The range of the area under the receiver operating characteristic curve (AUC) for the validation sets was 0.764 to 0.815 for CYP1A2, 0.837 to 0.861 for CYP2C9, 0.793 to 0.842 for CYP2C19, 0.839 to 0.886 for CYP2D6, and 0.754 to 0.790 for CYP3A4, respectively, using the new developed combined classifiers. The overall performance of the combined classifiers fused by BP-ANN was superior to that of three classic fusion techniques (Mean, Maximum, and Multiply). The chemical spaces of data sets were explored by multidimensional scaling plots, and the use of applicability domain improved the prediction accuracies of models. In addition, some representative substructure fragments differentiating CYP inhibitors and noninhibitors were characterized by the substructure fragment analysis. These classification models are applicable for virtual screening of the five major CYP isoforms inhibitors or can be used as simple filters of potential chemicals in drug discovery.
Journal of Chemical Information and Modeling 04/2011; · 4.68 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods.
Chemosphere 03/2011; 82(11):1636-43. · 3.21 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Agonists of β(3)-adrenergic receptor (AR) have been thought as potential drugs for the treatment of obesity, type II diabetes, and overactive bladder. In order to clarify the essential structure-activity relationship and the detailed binding modes of β(3)-AR agonists as well as to identify new lead compounds activating β(3)-AR, ligand-based and receptor-based methods were applied. The pharmacophore models were developed based on 144 β(3)-AR agonists. Meanwhile, the homology model of the β(3)-AR was built based on the crystal structure of β(2)-AR. The pharmacophore model and the homology model mapped with each other very well, and some important information was obtained from the docking result. For example, agonists formed similar hydrogen-bonding interactions with residues Asp117, Arg315, and Asn332, π-π stacking interaction with residues Phe308, and hydrophobic interactions with residues Val118, Val121, Ala197, Phe198, Ala199, Phe309, and Phe328 of β(3)-AR. And the major difference about binding mode from the crystal structures of β(1)- and β(2)-ARs is the hydrogen-bonding interaction with the residue Arg315, which corresponds to the residue Asn313 of β(1)-AR and the residue His296 of β(2)-AR, respectively. Our findings may be crucial for the design and development of novel selective and potent β(3)-AR agonists.
Molecular Diversity 03/2011; 15(4):817-31. · 3.15 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Type 2 histamine receptor (H(2)R) is widely distributed in the body. Its main function is modulating the secretion of gastric acid. Most gastric acid-related diseases are closely associated with it. In this study, a combination of pharmacophore modeling, homology modeling, molecular docking and molecular dynamics methods were performed on human H(2)R and its agonists to investigate interaction details between them. At first, a pharmacophore model of H(2)R agonists was developed, which was then validated by QSAR and database searching. Afterwards, a model of the H(2)R was built utilizing homology modeling method. Then, a reference agonist was docked into the receptor model by induced fit docking. The 'induced' model can dramatically improve the recovery ratio from 46.8% to 69.5% among top 10% of the ranked database in the simulated virtual screening. The pharmocophore model and the receptor model matched very well each other, which provided valuable information for future studies. Asp98, Asp186 and Tyr190 played key roles in the binding of H(2)R agonists, and direct interactions were observed between the three residues and agonists. Residue Tyr250 could also form a hydrogen bond with H(2)R agonists. These findings would be very useful for the discovery of novel and potent H(2)R agonists.
Journal of molecular graphics & modelling 02/2011; 29(5):693-701. · 2.17 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Dipeptidyl peptidase IV (DPP4) is an important target for the treatment of Type II diabetes mellitus. The crystal structure of DPP4 demonstrates that there are two possible pathways to the active site, a side opening and a β propeller opening. However, it still lacks quantitative evidence to illustrate which pathway is more favorable for inhibitor to enter into or release from the active site. In this study, conventional and steered molecular dynamics simulations were performed to explore the details of inhibitor Q448 release from the active site of DPP4 via the two potential pathways. The comparisons of force and work together with potentials of mean force results suggested that the side opening might be more favorable for the inhibitor to pass through. Moreover, Glu205-Glu206 and Phe357 were recognized as two "key residues" in the active site for inhibitor binding. Accordingly, suggestions for further inhibitor design were provided.
Proteins Structure Function and Bioinformatics 02/2011; 79(6):1800-9. · 3.39 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 5-hydroxytryptamine-2c (5-HT2c) receptor antagonists have clinical utility in the management of nervous system. In this work, ligand-based and receptor-based methods were used to investigate the binding mode of h5-HT2c receptor antagonists. First, the pharmacophore modeling of the h5-HT2c receptor antagonists was carried out by CATALYST. Then, the h5-HT2c antagonists were docked to the h5-HT2c receptor model. Subsequently, the comprehensive analysis of the pharmacophore and docking results revealed the structure-activity relationship of 5-HT2c receptor antagonists and the key residues involved in the interactions. For example, three hydrophobic points in the ligands corresponded to the region surrounded by Val135, Val208, Phe214, Ala222, Phe327, Phe328 and Val354 of the h5-HT2c receptor. The carbonyl group of compound 1 formed a hydrogen bond with Asn331. The nitrogen atom in the piperidine of compound 1 corresponding to the positive ionizable position of the best pharmacophore formed the electrostatic interactions with the carbonyl of Asp134, Asn331 and Val354, and with the hydroxyl group of Ser334. In addition, a predictive CoMFA model was developed based on the 24 compounds that were used as the training set in the pharmacophore modeling. Our results were not only useful to explore the detailed mechanism of the interactions between the h5-HT2c receptor and antagonists, but also provided suggestions in the discovery of novel 5-HT2c receptor antagonists.
Journal of Molecular Modeling 01/2011; 17(10):2513-23. · 1.80 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Cytochrome P450 (CYP) is deeply involved in the metabolism of chemicals including pharmaceuticals. Therefore, polymorphisms of this enzyme have been widely studied to avoid unfavorable side effects of drugs in chemotherapy. In this work, we performed computational analysis of the mechanism of the decrease in enzymatic activity for three typical polymorphisms in CYP 2C9 species: *2, *3, and *5. Based on the equilibrated structure obtained by molecular dynamics simulation, the volume of the binding pocket and the fluctuation of amino residues responsible for substrate holding were compared between the wild type and the three variants. Further docking simulation was carried out to evaluate the appropriateness of the binding pocket to accommodate substrate chemicals. Every polymorphic variant was suggested to be inferior to the wild type in enzymatic ability from the structural viewpoint. F-G helices were obviously displaced outward in CYP2C9*2. Expansion of the binding pocket, especially the space near F' helix, was remarkable in CYP2C9*3. Disappearance of the hydrogen bond between K helix and β4 loop was observed in CYP2C9*5. The reduction of catalytic activity of those variants can be explained from the deformation of the binding pocket and the consequent change in binding mode of substrate chemicals. The computational approach is effective for predicting the enzymatic activity of polymorphic variants of CYP. This prediction will be helpful for advanced drug design because calculations forecast unexpected change in drug efficacy for individuals.
Journal of Computational Chemistry 11/2010; 31(15):2746-58. · 4.58 Impact Factor
-
Zhimin Huang,
Liang Zhu,
Yan Cao,
Geng Wu,
Xinyi Liu,
Yingyi Chen,
Qi Wang,
Ting Shi,
Yaxue Zhao,
Yuefei Wang, Weihua Li,
Yixue Li,
Haifeng Chen,
Guoqiang Chen,
Jian Zhang
[show abstract]
[hide abstract]
ABSTRACT: Allostery is the most direct, rapid and efficient way of regulating protein function, ranging from the control of metabolic mechanisms to signal-transduction pathways. However, an enormous amount of unsystematic allostery information has deterred scientists who could benefit from this field. Here, we present the AlloSteric Database (ASD), the first online database that provides a central resource for the display, search and analysis of structure, function and related annotation for allosteric molecules. Currently, ASD contains 336 allosteric proteins from 101 species and 8095 modulators in three categories (activators, inhibitors and regulators). Proteins are annotated with a detailed description of allostery, biological process and related diseases, and modulators with binding affinity, physicochemical properties and therapeutic area. Integrating the information of allosteric proteins in ASD should allow for the identification of specific allosteric sites of a given subtype among proteins of the same family that can potentially serve as ideal targets for experimental validation. In addition, modulators curated in ASD can be used to investigate potent allosteric targets for the query compound, and also help chemists to implement structure modifications for novel allosteric drug design. Therefore, ASD could be a platform and a starting point for biologists and medicinal chemists for furthering allosteric research. ASD is freely available at http://mdl.shsmu.edu.cn/ASD/.
Nucleic Acids Research 11/2010; 39(Database issue):D663-9. · 8.03 Impact Factor
