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ABSTRACT: Berberine is an isoquinoline alkaloid that has drawn extensive attention since it possesses various biological activities. Several mechanisms have been proposed to interpret the anticancer activity of berberine. However, these explanations are mostly based on its downstream-regulated genes or proteins, information on the direct target proteins that mediate the antiproliferative action of berberine remain unclear. In the present study, a computational pipeline based on a ligand-protein inverse docking program and mining of the "Connectivity MAP" data was adopted to explore the potential target proteins for berberine. The results showed that four proteins, i.e., calmodulin, cytochrome P450 3A4, sex hormone-binding globulin and carbonic anhydrase II, were suggested to be the potential targets of berberine. The anti-calmodulin property of berberine was demonstrated with an in vitro phosphodiesterase activity assay. Flow cytometric analysis found that G1 cell cycle arrest induced by berberine in Bel7402 cells was enhanced by cotreatment with calmodulin inhibitors. Western blotting results indicated that berberine treatment decreased phosphorylation of calmodulin kinase II and blocked subsequent MEK1 activation as well as p27 protein degradation. These results suggested that calmodulin might play crucial roles in berberine-induced cell cycle arrest in cancer cells. © 2013 John Wiley & Sons A/S.
Chemical Biology & Drug Design 02/2013; · 2.28 Impact Factor
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ABSTRACT: The success of RNA interference (RNAi) depends on the interaction between short interference RNAs (siRNAs) and mRNAs. Design of highly efficient and specific siRNAs has become a challenging issue in applications of RNAi. Here, we present a detailed survey on the state-of-the-art siRNAs design, focusing on several key issues with the current in silico RNAi studies, including: (i) inconsistencies among the proposed guidelines for siRNAs design and the incomplete list of siRNAs features, (ii) improper integration of the heterogeneous cross-platform siRNAs data, (iii) inadequate consideration of the binding specificity of the target mRNAs and (iv) reduction in the 'off-target' effect in siRNAs design. With these considerations, the popular in silico siRNAs design rules are reexamined and several inconsistent viewpoints toward siRNAs feature identifications are clarified. In addition, novel computational models for siRNAs design using state-of-art machine learning techniques are discussed, which focus on heterogeneous data integration, joint feature selection and customized siRNAs screening toward highly specific targets. We believe that addressing such issues in siRNA study will provide new clues for further improved design of more efficient and specific siRNAs in RNAi.
Briefings in Bioinformatics 12/2012; · 5.20 Impact Factor
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ABSTRACT: G protein-coupled receptors (GPCRs) are the most frequently addressed drug targets in the pharmaceutical industry. However, achieving highly safety and efficacy in designing of GPCR drugs are quite challenging since their primary amino acid sequences show fairly high homology. Systematic study on the interaction spectra of inhibitors with multiple human GPCRs will shed light on how to design the inhibitors for different diseases which are related to GPCRs. To reach this goal, several proteochemometric models were constructed based on different combinations of two protein descriptors, two ligand descriptors and one ligand-receptor cross-term by two kinds of statistical learning techniques. Our results show that Support Vector Regression (SVR) performs better than Gaussian Processes (GP) for most combinations of descriptors. The Transmembrane (TM) Identity descriptors have more powerful ability than the Z-scale descriptors in the characterization of GPCRs. Furthermore, the performance of our PCM models was not improved by introducing the cross-terms. Finally, based on the TM Identity descriptors and 28-dimensional Drug-Like Index, two best PCM models with GP and SVR (GP-S-DLI: R(2)=0.9345, Q(2)(test)=0.7441; SVR-S-DLI: R(2)=1.0000, Q(2)(test)=0.7423) were derived respectively. The area of ROC curve was 0.8940 when an external test set was used, which indicates that our PCM model obtained a powerful capability for predicting new interactions between GPCRs and ligands. Our results indicate that the derived best model has a high predictive ability for human GPCR-inhibitor interactions. It can be potentially used to discover novel multi-target or specific inhibitors of GPCRs with higher efficacy and fewer side effects.
Gene 12/2012; · 2.34 Impact Factor
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ABSTRACT: BACKGROUND: Histone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study. RESULTS: The results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the characterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similarity descriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors. CONCLUSIONS: Our best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect.
BMC Bioinformatics 08/2012; 13(1):212. · 2.75 Impact Factor
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ABSTRACT: In post-genomic era, the study of transcriptional regulation is pivotal to decode genetic information. Transcription factors (TFs) are central proteins for transcriptional regulation, and interactions between TFs and their DNA targets (TFBSs) are important for downstream genes' expression. However, the lack of knowledge about interactions between TFs and TFBSs is still baffling people to investigate the mechanism of transcription.
To expand the knowledge about interactions between TFs and TFBSs, three biological features (sequence feature, structure feature, and evolution feature) were utilized to build TFBS identification models for studying binding preference between TFs and their DNA targets in mammals. Results show that each feature does have fairly well performance to capture TFBSs, and the hybrid model combined all three features is more robust for TFBS identification. Subsequently, correspondence between TFs and their TFBSs was investigated to explore interactions among them in mammals. Results indicate that TFs and TFBSs are reciprocal in sequence, structure, and evolution level.
Our work demonstrates that, to some extent, TFs and TFBSs have developed a coevolutionary relationship in order to keep their physical binding and maintain their regulatory functions. In summary, our work will help understand transcriptional regulation and interpret binding mechanism between proteins and DNAs.
BMC Genomics 08/2012; 13:388. · 4.07 Impact Factor
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ABSTRACT: With the growth of aging population all over the world, a rising incidence of Alzheimer's disease (AD) has been recently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredients and multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action (MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the current knowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their corresponding target proteins were systematically reviewed together with applicable bioinformatics resources and methodologies. Based on above information and resources, we present a systematically target network analysis framework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to the binding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbs also interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation, cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, pathways of Ca(2+) equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by the anti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the pathogenesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues for the future disease modifying strategies.
Briefings in Bioinformatics 08/2012; · 5.20 Impact Factor
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ABSTRACT: Leptospira interrogans serovar Lai is a pathogenic bacterium that causes a spirochetal zoonosis in humans and some animals. With its complete genome
sequence available, it is possible to analyze protein-protein interactions from a whole-genome standpoint. Here we combine
four recently developed computational approaches (gene fusion method, gene neighbor method, phylogenetic profiles method,
and operon method) to predict protein-protein interaction networks of Leptospira interrogans strain Lai. Through comprehensive analysis on interactions among proteins of motility and chemotaxis system, signal transduction,
lipopolysaccaride biosynthesis and a series of proteins related to adhesion and invasion, we provided information for further
studying on its pathogenic mechanism. In addition, we also assigned 203 previously uncharacterized proteins with possible
functions based on the known functions of its interacting partners. This work is helpful for further investigating L. interrogans strain Lai.
Chinese Science Bulletin 04/2012; 51(11):1296-1305. · 1.32 Impact Factor
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ABSTRACT: RNA interference via exogenous small interference RNAs (siRNA) is a powerful tool in gene function study and disease treatment. Designing efficient and specific siRNA on target gene remains the key issue in RNAi. Although various in silico models have been proposed for rational siRNA design, most of them focus on the efficiencies of selected siRNAs, while limited effort has been made to improve their specificities targeted on specific mRNAs, which is related to reducing off-target effects (OTEs) in RNAi. In our study, we propose for the first time that the enhancement of target specificity of siRNA design can be achieved computationally by domain transfer in heterogeneous data sources from different siRNA targets. A transfer learning based method i.e., heterogeneous regression (HEGS) is presented for target-specific siRNA efficacy modeling and feature selection. Based on the model, (1) the target regression model can be built by extracting information from related data in other targets/experiments, thus increasing the target specificity in siRNA design with the help of information from siRNAs binding to other homologous genes, and (2) the potential features correlated to the current siRNA design can be identified even when there is lack of experimental validated siRNA affinity data on this target. In summary, our findings present useful instructions for a better target-specific siRNA design, with potential applications in genome-wide high-throughput screening of effective siRNA, and will provide further insights on the mechanism of RNAi.
PLoS ONE 01/2012; 7(12):e50697. · 4.09 Impact Factor
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ABSTRACT: Inferring transcriptional regulatory networks from high-throughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed TReNGO (Transcriptional Regulatory Networks reconstruction based on Global Optimization), a global and threshold-free algorithm with simulated annealing for inferring regulatory networks by the integration of ChIP-chip and expression data. Superior to existing methods, TReNGO was expected to find the optimal structure of transcriptional regulatory networks without any arbitrary thresholds or predetermined number of transcriptional modules (TMs). TReNGO was applied to both synthetic data and real yeast data in the rapamycin response. In these applications, we demonstrated an improved functional coherence of TMs and TF (transcription factor)- target predictions by TReNGO when compared to GRAM, COGRIM or to analyzing ChIP-chip data alone. We also demonstrated the ability of TReNGO to discover unexpected biological processes that TFs may be involved in and to also identify interesting novel combinations of TFs.
Current Protein and Peptide Science 08/2011; 12(7):631-42. · 2.89 Impact Factor
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ABSTRACT: HIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.
In our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the L-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.
The framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.
BMC Bioinformatics 07/2011; 12:294. · 2.75 Impact Factor
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ABSTRACT: The toxicity of melamine has attracted much attention since the outbreak of nephrolithiasis among children ingesting melamine-contaminated infant formula in China. However, there is little knowledge about the molecular mechanisms underlying the melamine-induced toxicity. In this paper, a ligand-protein docking method (INVDOCK) was applied to predict the toxicity-related target proteins for melamine and its metabolite, cyanuric acid. As a result, 23 and 35 proteins were finally identified as the potential target proteins for melamine and cyanuric acid, respectively. Through an enrichment analysis, it was found that nephrotoxicity and lung toxicity might be the most significant toxicities induced by melamine and cyanuric acid. Four target proteins (glutathione peroxidase 1, beta-hexosaminidase subunit beta, L-lactate dehydrogenase and lysozyme C) may be related to the molecular basis of the nephrotoxicity induced by melamine except for known kidney crystals formation. After mapping all these toxicity-related target proteins onto cellular pathways, it was indicated that the toxicities of melamine and cyanuric acid might also be caused by breaking down redox balance, perturbing the arginine and proline metabolism and damaging the homeostasis of energy production system. To further explore the mechanisms underlying the toxicities of melamine and cyanuric acid, a biological signal cascades network constructed by some of the toxicity-related target proteins was discussed.
Toxicology 03/2011; 283(2-3):96-100. · 3.68 Impact Factor
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Hao Ye,
Li Ye,
Hong Kang,
Duanfeng Zhang,
Lin Tao,
Kailin Tang,
Xueping Liu,
Ruixin Zhu, Qi Liu,
Y Z Chen,
Yixue Li,
Zhiwei Cao
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ABSTRACT: The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients' Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.
Nucleic Acids Research 01/2011; 39(Database issue):D1055-9. · 8.03 Impact Factor
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ABSTRACT: As an important risk factor for Hepatocellular Carcinoma (HCC), Hepatitis C Virus (HCV) infection can induce cirrhosis and HCC. But, the molecular mechanisms of HCV-induced transformation remain largely unknown. In this study, first, we identified the dysfunctional protein interaction networks in cirrhosis and HCC based on the gene expression profiles of 19 normal, 58 cirrhotic and 47 HCC liver tissues. Then, the relationship between dysfunctional networks and HCV infection was analysed. Our results may help understand the mechanisms of HCV-induced malignant transformation and provide clues to cirrhosis and HCC therapy or prevention.
International Journal of Computational Biology and Drug Design 01/2011; 4(1):5-18.
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ABSTRACT: The hedgehog signal pathway is an essential agent in developmental patterning, wherein the local concentration of the Hedgehog morphogens directs cellular differentiation and expansion. Furthermore, the Hedgehog pathway has been implicated in tumor/stromal interaction and cancer stem cell. Nowadays searching novel inhibitors for Hedgehog Signal Pathway is drawing much more attention by biological, chemical and pharmological scientists. In our study, a solid computational model is proposed which incorporates various statistical analysis methods to perform a Quantitative Structure-Activity Relationship (QSAR) study on the inhibitors of Hedgehog signaling. The whole QSAR data contain 93 cyclopamine derivatives as well as their activities against four different cell lines (NCI-H446, BxPC-3, SW1990 and NCI-H157). Our extensive testing indicated that the binary classification model is a better choice for building the QSAR model of inhibitors of Hedgehog signaling compared with other statistical methods and the corresponding in silico analysis provides three possible ways to improve the activity of inhibitors by demethylation, methylation and hydroxylation at specific positions of the compound scaffold respectively. From these, demethylation is the best choice for inhibitor structure modifications. Our investigation also revealed that NCI-H466 served as the best cell line for testing the activities of inhibitors of Hedgehog signal pathway among others.
International Journal of Molecular Sciences 01/2011; 12(5):3018-33. · 2.60 Impact Factor
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Cell Research 10/2010; 20(11):1276-8. · 8.19 Impact Factor
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Chinese Journal of Chemistry 08/2010; 28(7):1284 - 1290. · 0.75 Impact Factor
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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, BCB 2010, Niagara Falls, NY, USA, August 2-4, 2010; 01/2010
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ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010; 01/2010
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ABSTRACT: Inference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods.
We developed a TF-centered downstream gene set enrichment analysis approach to identify potential causal regulators responsible for expression changes. We constructed hierarchical and multi-layer regulation models to derive possible downstream gene sets of a TF using not only TF-DNA interactions, but also, for the first time, post-translational modifications (PTM) information. We verified our method in one expression dataset of large-scale TF knockout and another dataset involving both TF knockout and TF overexpression. Compared with the flat model using TF-DNA interactions alone, our method correctly identified five more actual perturbed TFs in large-scale TF knockout data and six more perturbed TFs in overexpression data. Potential regulatory pathways downstream of three perturbed regulators- SNF1, AFT1 and SUT1 -were given to demonstrate the power of multilayer regulation models integrating TF-DNA interactions and PTM information. Additionally, our method successfully identified known important TFs and inferred some novel potential TFs involved in the transition from fermentative to glycerol-based respiratory growth and in the pheromone response. Downstream regulation pathways of SUT1 and AFT1 were also supported by the mRNA and/or phosphorylation changes of their mediating TFs and/or "modulator" proteins.
The results suggest that in addition to direct transcription, indirect transcription and post-translational regulation are also responsible for the effects of TFs perturbation, especially for TFs overexpression. Many TFs inferred by our method are supported by literature. Multiple TF regulation models could lead to new hypotheses for future experiments. Our method provides a valuable framework for analyzing gene expression data to identify causal regulators in the context of TF-DNA interactions and PTM information.
BMC Bioinformatics 01/2010; 11 Suppl 11:S5. · 2.75 Impact Factor
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ABSTRACT: How to combine heterogeneous data sources for reliable prediction of transcriptional regulation is a challenge. Here we present an easy but powerful method to integrate Chromatin immunoprecipitation (ChIP)-chip and knock-out data. Since these two types of data provide complementary (physical and functional) information about transcription, the method combining them is expected to achieve high detection rates and very low false positive rates. We try to seek the optimal integration of these two data using hyper-geometric distribution. We evaluate our method on yeast data and compare our predictions with YEASTRACT, high-quality ChIP-chip data, and literature. The results show that even using low-quality ChIP-chip data, our method uncovers more relations than those inferred before from high-quality data. Furthermore our method achieves a low false positive rate. We find experimental and computational evidence in literature for most transcription factor (TF)-gene relations uncovered by our method.
Bioinformatics and biology insights 01/2009; 3:129-40.
