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1.
Brief Bioinform ; 2020 Jan 31.
Artigo em Inglês | MEDLINE | ID: mdl-32003790

RESUMO

Moonlighting proteins provide more options for cells to execute multiple functions without increasing the genome and transcriptome complexity. Although there have long been calls for computational methods for the prediction of moonlighting proteins, no method has been designed for determining moonlighting long noncoding ribonucleicacidz (RNAs) (mlncRNAs). Previously, we developed an algorithm MoonFinder for the identification of mlncRNAs at the genome level based on the functional annotation and interactome data of lncRNAs and proteins. Here, we update MoonFinder to MoonFinder v2.0 by providing an extensive framework for the detection of protein modules and the establishment of RNA-module associations in human. A novel measure, moonlighting coefficient, was also proposed to assess the confidence of an ncRNA acting in a moonlighting manner. Moreover, we explored the expression characteristics of mlncRNAs in sepsis, in which we found that mlncRNAs tend to be upregulated and differentially expressed. Interestingly, the mlncRNAs are mutually exclusive in terms of coexpression when compared to the other lncRNAs. Overall, MoonFinder v2.0 is dedicated to the prediction of human mlncRNAs and thus bears great promise to serve as a valuable R package for worldwide research communities (https://cran.r-project.org/web/packages/MoonFinder/index.html). Also, our analyses provide the first attempt to characterize mlncRNA expression and coexpression properties in adult sepsis patients, which will facilitate the understanding of the interaction and expression patterns of mlncRNAs.

2.
BMC Bioinformatics ; 20(Suppl 26): 628, 2019 Dec 16.
Artigo em Inglês | MEDLINE | ID: mdl-31839008

RESUMO

BACKGROUND: Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters. CONCLUSIONS: The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.


Assuntos
Biologia Computacional , Reposicionamento de Medicamentos , Análise por Conglomerados , Biologia Computacional/métodos , Reposicionamento de Medicamentos/métodos , Humanos , Aprendizado de Máquina
3.
BMC Bioinformatics ; 20(1): 408, 2019 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-31357929

RESUMO

BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures. RESULTS: In this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant (R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works (Ammad-Ud-Din et al., J Chem Inf Model 54:2347-9, 2014), (Haider et al., PLoS ONE 10:0144490, 2015), (Menden et al., PLoS ONE 8:61318, 2013). However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0. CONCLUSIONS: Our approach is able to predict the drug effects on cancer cell lines with high accuracy, and its performance remains stable with less but high-quality data, and with fewer features for the cancer cell lines. tCNNS can also solve the problem of outliers in other feature space. Besides achieving high scores in these statistical metrics, tCNNS also provides some insights into the phenotypic screening. However, the performance of tCNNS drops in the blind test.


Assuntos
Antineoplásicos/uso terapêutico , Aprendizado Profundo , Neoplasias/tratamento farmacológico , Antineoplásicos/farmacologia , Linhagem Celular Tumoral , Bases de Dados Factuais , Genômica , Humanos , Concentração Inibidora 50 , Especificidade de Órgãos/efeitos dos fármacos , Fenótipo , Análise de Regressão
4.
Bioinformatics ; 35(20): 3989-3995, 2019 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-30873528

RESUMO

MOTIVATION: Studies have shown that the accuracy of random forest (RF)-based scoring functions (SFs), such as RF-Score-v3, increases with more training samples, whereas that of classical SFs, such as X-Score, does not. Nevertheless, the impact of the similarity between training and test samples on this matter has not been studied in a systematic manner. It is therefore unclear how these SFs would perform when only trained on protein-ligand complexes that are highly dissimilar or highly similar to the test set. It is also unclear whether SFs based on machine learning algorithms other than RF can also improve accuracy with increasing training set size and to what extent they learn from dissimilar or similar training complexes. RESULTS: We present a systematic study to investigate how the accuracy of classical and machine-learning SFs varies with protein-ligand complex similarities between training and test sets. We considered three types of similarity metrics, based on the comparison of either protein structures, protein sequences or ligand structures. Regardless of the similarity metric, we found that incorporating a larger proportion of similar complexes to the training set did not make classical SFs more accurate. In contrast, RF-Score-v3 was able to outperform X-Score even when trained on just 32% of the most dissimilar complexes, showing that its superior performance owes considerably to learning from dissimilar training complexes to those in the test set. In addition, we generated the first SF employing Extreme Gradient Boosting (XGBoost), XGB-Score, and observed that it also improves with training set size while outperforming the rest of SFs. Given the continuous growth of training datasets, the development of machine-learning SFs has become very appealing. AVAILABILITY AND IMPLEMENTATION: https://github.com/HongjianLi/MLSF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

5.
Chem Biol Drug Des ; 94(1): 1390-1401, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30916462

RESUMO

Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

6.
BMC Bioinformatics ; 20(1): 23, 2019 Jan 14.
Artigo em Inglês | MEDLINE | ID: mdl-30642247

RESUMO

BACKGROUND: Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells. RESULTS: In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL. CONCLUSION: Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.


Assuntos
Algoritmos , Biologia Computacional/métodos , Bases de Dados de Proteínas , Proteínas de Neoplasias/metabolismo , Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas , Proteínas de Saccharomyces cerevisiae/metabolismo , Humanos , Proteínas de Neoplasias/química , Proteínas de Saccharomyces cerevisiae/química
7.
Sci Rep ; 8(1): 15186, 2018 10 12.
Artigo em Inglês | MEDLINE | ID: mdl-30315195

RESUMO

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.


Assuntos
Epistasia Genética , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Polimorfismo de Nucleotídeo Único , Psoríase/genética , Alelos , Estudos de Casos e Controles , Biologia Computacional/métodos , Redes Reguladoras de Genes , Genótipo , Haplótipos , Humanos , Fenótipo
8.
Oncol Rep ; 40(3): 1592-1600, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-29956794

RESUMO

Since cyclin­dependent kinases 4/6 (CDK4/6) play pivotal roles in cell cycle regulation and are overexpressed in human skin cancers, CDK4/6 inhibitors are potentially effective drugs for skin cancer. In the present study, we present a mixed computational and experimental study attempting to repurpose approved small­molecule drugs as dual CDK4/6 inhibitors for skin cancer treatment. We performed structure­based virtual screening using the docking software idock, targeting an ensemble of CDK4/6 structures. We identified and selected nine compounds with significant predicted scores, and evaluated their cytotoxic effects in vitro in A375 and A431 human skin cancer cell lines. Rafoxanide was found to exhibit the highest cytotoxic effects (IC50: 1.09 µM for A375 and 1.31 µM for A431 cells). Consistent with the expected properties of CDK4/6 inhibitors, rafoxanide significantly increased the G1 phase population. Notably, we revealed that rafoxanide specifically decreased the expression of CDK4/6, cyclin D, retinoblastoma protein (Rb) and the phosphorylation of CDK4/6 and Rb. Furthermore, the anticancer effect of rafoxanide was demonstrated in vivo in BALB/C nude mice subcutaneously xenografted with human skin cancer A375 cells. Rafoxanide (40 mg/kg, i.p.) exhibited significant antitumor activity, comparable to that of oxaliplatin (5 mg/kg, i.p.). The combined administration of rafoxanide and oxaliplatin produced a synergistic therapeutic effect. To the best of our knowledge, the present study is the first to indicate that rafoxanide inhibits CDK4/6 activity and is a potential candidate drug for the treatment of human skin cancer.


Assuntos
Biomarcadores Tumorais/metabolismo , Quinase 4 Dependente de Ciclina/antagonistas & inibidores , Quinase 6 Dependente de Ciclina/antagonistas & inibidores , Inibidores de Proteínas Quinases/farmacologia , Rafoxanida/farmacologia , Neoplasias Cutâneas/tratamento farmacológico , Bibliotecas de Moléculas Pequenas/farmacologia , Animais , Antinematódeos/farmacologia , Antineoplásicos/farmacologia , Apoptose/efeitos dos fármacos , Proliferação de Células/efeitos dos fármacos , Descoberta de Drogas , Feminino , Regulação Enzimológica da Expressão Gênica/efeitos dos fármacos , Ensaios de Triagem em Larga Escala , Humanos , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Nus , Neoplasias Cutâneas/enzimologia , Neoplasias Cutâneas/patologia , Células Tumorais Cultivadas , Ensaios Antitumorais Modelo de Xenoenxerto
9.
Bioinformatics ; 34(20): 3519-3528, 2018 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-29771280

RESUMO

Motivation: Moonlighting proteins are a class of proteins having multiple distinct functions, which play essential roles in a variety of cellular and enzymatic functioning systems. Although there have long been calls for computational algorithms for the identification of moonlighting proteins, research on approaches to identify moonlighting long non-coding RNAs (lncRNAs) has never been undertaken. Here, we introduce a novel methodology, MoonFinder, for the identification of moonlighting lncRNAs. MoonFinder is a statistical algorithm identifying moonlighting lncRNAs without a priori knowledge through the integration of protein interactome, RNA-protein interactions and functional annotation of proteins. Results: We identify 155 moonlighting lncRNA candidates and uncover that they are a distinct class of lncRNAs characterized by specific sequence and cellular localization features. The non-coding genes that transcript moonlighting lncRNAs tend to have shorter but more exons and the moonlighting lncRNAs have a variable localization pattern with a high chance of residing in the cytoplasmic compartment in comparison to the other lncRNAs. Moreover, moonlighting lncRNAs and moonlighting proteins are rather mutually exclusive in terms of both their direct interactions and interacting partners. Our results also shed light on how the moonlighting candidates and their interacting proteins implicated in the formation and development of cancers and other diseases. Availability and implementation: The code implementing MoonFinder is supplied as an R package in the supplementary material. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Proteínas/genética , RNA Longo não Codificante/genética , Éxons , Genômica , Humanos , Análise de Sequência de RNA/métodos
10.
Biomolecules ; 8(1)2018 03 14.
Artigo em Inglês | MEDLINE | ID: mdl-29538331

RESUMO

It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.


Assuntos
Aprendizado de Máquina , Simulação de Acoplamento Molecular/normas , Mapeamento de Interação de Proteínas/métodos , Análise de Sequência de Proteína/normas , Mapeamento de Interação de Proteínas/normas
11.
IET Syst Biol ; 12(2): 55-61, 2018 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-29533218

RESUMO

Computational clustering methods help identify functional modules in protein-protein interaction (PPI) network, in which proteins participate in the same biological pathways or specific functions. Subcellular localisation is crucial for proteins to implement biological functions and each compartment accommodates specific portions of the protein interaction structure. However, the importance of protein subcellular localisation is often neglected in the studies of module identification. In this study, the authors propose a novel procedure, subcellular module identification with localisation expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments. These super modules identified by SMILE are more functionally diverse and have been verified to be more associated with known protein complexes and biological pathways compared with the modules identified from the global PPI networks in both the compartmentalised PPI and InWeb_InBioMap datasets. The authors' results reveal that subcellular localisation is a principal feature of functional modules and offers important guidance in detecting biologically meaningful results.


Assuntos
Análise por Conglomerados , Mapas de Interação de Proteínas , Algoritmos , Mapeamento de Interação de Proteínas , Proteínas
12.
J Mol Cell Biol ; 10(2): 130-138, 2018 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-29390072

RESUMO

Subcellular localization is pivotal for RNAs and proteins to implement biological functions. The localization diversity of protein interactions has been studied as a crucial feature of proteins, considering that the protein-protein interactions take place in various subcellular locations. Nevertheless, the localization diversity of non-coding RNA (ncRNA) target proteins has not been systematically studied, especially its characteristics in cancers. In this study, we provide a new algorithm, non-coding RNA target localization coefficient (ncTALENT), to quantify the target localization diversity of ncRNAs based on the ncRNA-protein interaction and protein subcellular localization data. ncTALENT can be used to calculate the target localization coefficient of ncRNAs and measure how diversely their targets are distributed among the subcellular locations in various scenarios. We focus our study on long non-coding RNAs (lncRNAs), and our observations reveal that the target localization diversity is a primary characteristic of lncRNAs in different biotypes. Moreover, we found that lncRNAs in multiple cancers, differentially expressed cancer lncRNAs, and lncRNAs with multiple cancer target proteins are prone to have high target localization diversity. Furthermore, the analysis of gastric cancer helps us to obtain a better understanding that the target localization diversity of lncRNAs is an important feature closely related to clinical prognosis. Overall, we systematically studied the target localization diversity of the lncRNAs and uncovered its association with cancer.


Assuntos
Regulação Neoplásica da Expressão Gênica , Neoplasias/genética , Proteínas/genética , RNA não Traduzido/genética , Algoritmos , Animais , Genômica , Humanos , Neoplasias/patologia , Proteínas/análise , Neoplasias Gástricas/genética , Neoplasias Gástricas/patologia
13.
Sci Rep ; 7(1): 17987, 2017 12 21.
Artigo em Inglês | MEDLINE | ID: mdl-29269744

RESUMO

The phosphatidylinositol-3-kinase (PI3K)/AKT signaling pathway plays a pivotal role in many cellular processes, including the proliferation, survival and differentiation of lung cancer cells. Thus, PI3K is a promising therapeutic target for lung cancer treatment. In this study, we applied free and open-source protein-ligand docking software, screened 3167 FDA-approved small molecules, and identified putative PI3Kα inhibitors. Among them, econazole nitrate, an antifungal agent, exhibited the highest activity in decreasing cell viability in pathological types of NSCLC cell lines, including H661 (large cell lung cancer) and A549 (adenocarcinoma). Econazole decreased the protein levels of p-AKT and Bcl-2, but had no effect on the phosphorylation level of ERK. It inhibited cell growth and promote apoptosis in a dose-dependent manner. Furthermore, the combination of econazole and cisplatin exhibited additive and synergistic effects in the H661 and A549 lung cancer cell lines, respectively. Finally, we demonstrated that econazole significantly suppressed A549 tumor growth in nude mice. Our findings suggest that econazole is a new PI3K inhibitor and a potential drug that can be used in lung cancer treatment alone or in combination with cisplatin.


Assuntos
Antineoplásicos/uso terapêutico , Apoptose/efeitos dos fármacos , Econazol/uso terapêutico , Neoplasias Pulmonares/tratamento farmacológico , Células A549 , Animais , Linhagem Celular Tumoral , Humanos , Masculino , Camundongos Endogâmicos BALB C , Camundongos Nus , Transplante de Neoplasias , Proteína Oncogênica v-akt/metabolismo , Proteínas Proto-Oncogênicas c-bcl-2/metabolismo , Transdução de Sinais/efeitos dos fármacos
14.
Front Oncol ; 7: 288, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29238696

RESUMO

In advanced lung cancer, epidermal growth factor tyrosine kinase inhibitors (EGFR TKIs) have extraordinary clinical efficacy. However, their usefulness is severely compromised by drug resistance mediated by various mechanisms, the most important of which is the secondary EGFR T790M mutation. The mutation blocks the binding of EGFR TKIs to the receptor kinase, thereby abolishing the therapeutic efficacy. In this study, we used our free and open-source protein-ligand docking software idock to screen worldwide approved small-molecule drugs against EGFR T790M. The computationally selected drug candidates were evaluated in vitro in resistant non-small cell lung cancer (NSCLC) cell lines. The specificity of the drugs toward the mutant EGFR was demonstrated by cell-free kinase inhibition assay. The inhibition of EGFR kinase activity and its downstream signaling pathways in NSCLC cells was shown by immunoblot analysis. The positive hints were revealed to be indacaterol, canagliflozin, and cis-flupenthixol, all of which were shown to induce apoptosis in NSCLC cells harboring the EGFR T790M mutation. Moreover, the combination of indacaterol with gefitinib was also found to produce synergistic anticancer effect in NSCLC cells bearing EGFR T790M. The observed synergistic effect was likely contributed by the enhanced inhibition of EGFR and its downstream signaling molecules.

15.
Sensors (Basel) ; 18(1)2017 Dec 22.
Artigo em Inglês | MEDLINE | ID: mdl-29271952

RESUMO

Urban air pollution has caused public concern globally because it seriously affects human life. Modern monitoring systems providing pollution information with high spatio-temporal resolution have been developed to identify personal exposures. However, these systems' hardware specifications and configurations are usually fixed according to the applications. They can be inconvenient to maintain, and difficult to reconfigure and expand with respect to sensing capabilities. This paper aims at tackling these issues by adopting the proposed Modular Sensor System (MSS) architecture and Universal Sensor Interface (USI), and modular design in a sensor node. A compact MSS sensor node is implemented and evaluated. It has expandable sensor modules with plug-and-play feature and supports multiple Wireless Sensor Networks (WSNs). Evaluation results show that MSS sensor nodes can easily fit in different scenarios, adapt to reconfigurations dynamically, and detect low concentration air pollution with high energy efficiency and good data accuracy. We anticipate that the efforts on system maintenance, adaptation, and evolution can be significantly reduced when deploying the system in the field.


Assuntos
Poluição do Ar/análise , Computadores , Tecnologia sem Fio
16.
J Pharmacol Sci ; 135(3): 114-120, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-29132796

RESUMO

Hyperuricemia, a long-term purine metabolic disorder, is a well-known risk factor for gout, hypertension and diabetes. In maintaining normal whole-body purine levels, xanthine oxidase (XOD) is a key enzyme in the purine metabolic pathway, as it catalyzes the oxidation of hypoxanthine to xanthine and finally to uric acid. Here we used the protein-ligand docking software idock to virtually screen potential XOD inhibitors from 3167 approved small compounds/drugs. The inhibitory activities of the ten compounds with the highest scores were tested on XOD in vitro. Interestingly, all the ten compounds inhibited the activity of XOD at certain degrees. Particularly, the anti-ulcerative-colitis drug olsalazine sodium demonstrated a great inhibitory activity for XOD (IC50 = 3.4 mg/L). Enzymatic kinetic studies revealed that the drug was a hybrid-type inhibitor of xanthine oxidase. Furthermore, the drug strikingly decreased serum urate levels, serum/hepatic activities of XOD at a dose-dependent manner in vivo. Thus, we demonstrated a successful hunting process of compounds/drugs for hyperuricemia through virtual screening, supporting a potential usage of olsalazine sodium in the treatment of hyperuricemia.


Assuntos
Ácidos Aminossalicílicos/farmacologia , Antiulcerosos/farmacologia , Ácido Úrico/sangue , Xantina Desidrogenase/antagonistas & inibidores , Xantina Desidrogenase/metabolismo , Ácidos Aminossalicílicos/uso terapêutico , Animais , Relação Dose-Resposta a Droga , Avaliação Pré-Clínica de Medicamentos , Hiperuricemia/tratamento farmacológico , Técnicas In Vitro , Masculino , Camundongos , Relação Estrutura-Atividade
17.
J Proteome Res ; 16(8): 3019-3029, 2017 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-28707887

RESUMO

Spatial-temporal regulation among proteins forms dynamic networks in cells. Coexistence in common cell compartments can improve biological reliability of the protein-protein interactions. However, this is usually overlooked by most proteomic studies and leads to unrealistic discoveries. In this paper, we systematically characterize the interaction localization diversity in the human protein interactome using the localization coefficient, a novel metric proposed for assessing how diversely the interactions localize among cell compartments. Our analysis reveals the following: (1) the subcellular networks of the nucleus, cytosol, and mitochondrion are dense but the interactions tend to localize in specific cell compartments, whereas the subnetworks of the secretory-pathway, membrane, and extracellular region are sparse but the interactions are diversely localized; (2) the housekeeping proteins tend to appear in multiple compartments, while the tissue-specific proteins present a relatively flat profile of localization breadth; (3) the autophagy proteins tend to diversely localize in multiple compartments, especially those with high connectivity, compared with the apoptosis proteins; (4) the proteins targeted by small-molecule drugs show no preference for compartments, whereas the proteins directed by antibody-based drugs tend to belong to transmembrane regions with a strong diversity. In summary, our analysis provides a comprehensive view of the subcellular localization for interacting proteins, demonstrates that localization diversity is an important feature of protein interactions, and shows its ability to highlight meaningful biological functions.


Assuntos
Compartimento Celular , Mapas de Interação de Proteínas/fisiologia , Proteoma/análise , Frações Subcelulares/química , Humanos , Espaço Intracelular/química , Mapeamento de Interação de Proteínas , Proteômica/métodos , Análise Espaço-Temporal , Frações Subcelulares/fisiologia
18.
Chem Biol Drug Des ; 89(4): 505-513, 2017 04.
Artigo em Inglês | MEDLINE | ID: mdl-27664399

RESUMO

Bladder carcinoma (BC) is the ninth most common cause of cancer worldwide. Surgical resection and conventional chemotherapy and radiotherapy will ultimately fail due to tumor recurrence and resistance. Thus, the development of novel treatment is urgently needed. Fibroblast growth factor receptor 3 (FGFR3) is an important and well-established target for BC treatment. In this study, we utilized the free and open-source protein-ligand docking software idock to prospectively identify potential inhibitors of FGFR3 from 3,167 worldwide approved small-molecule drugs using a repositioning strategy. Six high-scoring compounds were purchased and tested in vitro. Among them, the acaricide drug fluazuron exhibited the highest antiproliferative effect in human BC cell lines RT112 and RT4. We further demonstrated that fluazuron treatment significantly increased the percentage of apoptosis cells, and decreased the phosphorylation level of FGFR3 and its downstream proteins FRS2-α, AKT, and ERK. We also investigated the anticancer effect of fluazuron in vivo in BALB/C nude mice subcutaneously xenografted with RT112 cells. Our results showed that oral treatment with fluazuron (80 mg/kg) significantly inhibited tumor growth. These results suggested for the first time that fluazuron is a potential inhibitor of FGFR3 and a candidate anticancer drug for the treatment of BC.


Assuntos
Acaricidas/farmacologia , Antineoplásicos/farmacologia , Compostos de Fenilureia/farmacologia , Receptor Tipo 3 de Fator de Crescimento de Fibroblastos/antagonistas & inibidores , Neoplasias da Bexiga Urinária/tratamento farmacológico , Acaricidas/química , Antineoplásicos/química , Apoptose/efeitos dos fármacos , Linhagem Celular Tumoral , Cristalografia por Raios X , Humanos , Técnicas In Vitro , Simulação de Acoplamento Molecular , Compostos de Fenilureia/química , Fosforilação , Receptor Tipo 3 de Fator de Crescimento de Fibroblastos/química , Receptor Tipo 3 de Fator de Crescimento de Fibroblastos/metabolismo , Transdução de Sinais , Neoplasias da Bexiga Urinária/patologia
19.
Artigo em Inglês | MEDLINE | ID: mdl-26336137

RESUMO

Understanding binding cores is of fundamental importance in deciphering Protein-DNA (TF-TFBS) binding and gene regulation. Limited by expensive experiments, it is promising to discover them with variations directly from sequence data. Although existing computational methods have produced satisfactory results, they are one-to-one mappings with no site-specific information on residue/nucleotide variations, where these variations in binding cores may impact binding specificity. This study presents a new representation for modeling binding cores by incorporating variations and an algorithm to discover them from only sequence data. Our algorithm takes protein and DNA sequences from TRANSFAC (a Protein-DNA Binding Database) as input; discovers from both sets of sequences conserved regions in Aligned Pattern Clusters (APCs); associates them as Protein-DNA Co-Occurring APCs; ranks the Protein-DNA Co-Occurring APCs according to their co-occurrence, and among the top ones, finds three-dimensional structures to support each binding core candidate. If successful, candidates are verified as binding cores. Otherwise, homology modeling is applied to their close matches in PDB to attain new chemically feasible binding cores. Our algorithm obtains binding cores with higher precision and much faster runtime ( ≥ 1,600x) than that of its contemporaries, discovering candidates that do not co-occur as one-to-one associated patterns in the raw data. AVAILABILITY: http://www.pami.uwaterloo.ca/~ealee/files/tcbbPnDna2015/Release.zip.


Assuntos
Análise por Conglomerados , Biologia Computacional/métodos , Proteínas de Ligação a DNA/química , DNA/química , Alinhamento de Sequência/métodos , Algoritmos , DNA/análise , DNA/genética , DNA/metabolismo , Proteínas de Ligação a DNA/genética , Proteínas de Ligação a DNA/metabolismo , Mineração de Dados , Ligação Proteica , Análise de Sequência de DNA , Análise de Sequência de Proteína
20.
Mol Biosyst ; 12(10): 3057-66, 2016 10 20.
Artigo em Inglês | MEDLINE | ID: mdl-27452923

RESUMO

The global increase of gene expression has been frequently established in cancer microarray studies. However, many genes may not deliver informative signals for a given experiment, due to insufficient expression or even non-expression, despite the DNA microarrays massively measuring genes in parallel. Hence the informative gene set, rather than the whole genome, should be more reasonable to represent the genome expression level. We observed that the trend of over-expression for informative genes is more obvious in human cancers, which is to some extent masked using the whole genome without any filtering. Accordingly we proposed a novel normalization method, Informative CrossNorm (ICN), which performs the cross normalization (CrossNorm) on the expression matrix merely containing the informative genes. ICN outperforms other methods with a consistently high precision, F-score, and Matthews correlation coefficient as well as an acceptable recall based on three available spiked-in datasets with ground truth. In addition, nine potential therapeutic target genes for esophageal squamous cell carcinoma (ESCC) were identified using ICN integrated with a protein-protein interaction network, which biologically demonstrates that ICN shows superior performance. Consequently, it is expected that ICN could be applied routinely in cancer microarray studies.


Assuntos
Biologia Computacional/métodos , Perfilação da Expressão Gênica/métodos , Expressão Gênica , Carcinoma de Células Escamosas/genética , Bases de Dados Genéticas , Neoplasias Esofágicas/genética , Carcinoma de Células Escamosas do Esôfago , Redes Reguladoras de Genes , Humanos , Neoplasias/genética , Neoplasias/metabolismo , Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas , Reprodutibilidade dos Testes , Fluxo de Trabalho
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