Artículo

Radusky, L.; Defelipe, L.A.; Lanzarotti, E.; Luque, J.; Barril, X.; Marti, M.A.; Turjanski, A.G. "TuberQ: A Mycobacterium tuberculosis protein druggability database" (2014) Database. 2014
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Abstract:

In 2012 an estimated 8.6 million people developed tuberculosis (TB) and 1.3 million died from the disease [including 320 000 deaths among human immunodeficiency virus (HIV)-positive people]. There is an urgent need for new anti-TB drugs owing to the following: the fact that current treatments have severe side effects, the increasing emergence of multi-drug-resistant strains of Mycobacterium tuberculosis (Mtb), the negative drug-drug interactions with certain HIV (or other disease) treatments and the ineffectiveness against dormant Mtb. In this context we present here the TuberQ database, a novel resource for all researchers working in the field of drug development in TB. The main feature of TuberQ is to provide a druggability analysis of Mtb proteins in a consistent and effective manner, contributing to a better selection of potential drug targets for screening campaigns and the analysis of targets for structure-based drug design projects. The structural druggability analysis is combined with features related to the characteristics of putative inhibitor binding pockets and with functional and biological data of proteins. The structural analysis is performed on all available unique Mtb structures and high-quality structural homology-based models. This information is shown in an interactive manner, depicting the protein structure, the pockets and the associated characteristics for each protein. TuberQ also provides information about gene essentiality information, as determined from whole cell-based knockout experiments, and expression information obtained from microarray experiments done in different stress-related conditions. We hope that TuberQ will be a powerful tool for researchers working in TB and eventually will lead to the identification of novel putative targets and progresses in therapeutic activities. © The Author(s) 2014.

Registro:

Documento: Artículo
Título:TuberQ: A Mycobacterium tuberculosis protein druggability database
Autor:Radusky, L.; Defelipe, L.A.; Lanzarotti, E.; Luque, J.; Barril, X.; Marti, M.A.; Turjanski, A.G.
Filiación:Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón II, Buenos Aires, C1428EHA, Argentina
INQUIMAE/UBA-CONICET, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellón II, Buenos Aires, C1428EHA, Argentina
Department of Physical Chemistry, Faculty of Pharmacy, Institute of Biomedicine (IBUB), University of Barcelona, Campus de l'Alimentació Torribera, Avgda. Prat de la Riba 171, Santa Coloma de Gramenet, 08921, Spain
Department of Physical Chemistry, Faculty of Pharmacy, Institute of Biomedicine (IBUB), University of Barcelona, Avgda. Diagonal 643, Barcelona, 08028, Spain
Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain
Palabras clave:bacterial protein; tuberculostatic agent; article; chemistry; data base; Internet; metabolism; Mycobacterium tuberculosis; protein database; protein tertiary structure; Antitubercular Agents; Bacterial Proteins; Database Management Systems; Databases, Protein; Internet; Mycobacterium tuberculosis; Protein Structure, Tertiary
Año:2014
Volumen:2014
DOI: http://dx.doi.org/10.1093/database/bau035
Título revista:Database
Título revista abreviado:Database
ISSN:17580463
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_17580463_v2014_n_p_Radusky

Referencias:

  • (2013) Global Tuberculosis Report, 2013
  • Caminero, J.A., Sotgiu, G., Zumla, A., Best drug treatment for multidrug-resistant and extensively drug-resistant tuberculosis (2010) Lancet Infec. Dis., 10, pp. 621-629
  • Koul, A., Arnoult, E., Lounis, N., The challenge of new drug discovery for tuberculosis (2011) Nature, 469, pp. 483-490
  • Russell, D.G., Barry, C.E., Flynn, J.L., Tuberculosis: What we don't know can, and does, hurt us (2010) Science, 328, pp. 852-856
  • Lew, J.M., Kapopoulou, A., Jones, L.M., TubercuList-10 years after (2011) Tuberculosis, 91, pp. 1-7
  • Reddy, T., Riley, R., Wymore, F., TB database: An integrated platform for tuberculosis research (2009) Nucleic Acids Res., 37, pp. D499-D508
  • Aguero, F., Al-Lazikani, B., Aslett, M., Genomic-scale prioritization of drug targets: The TDR targets database (2008) Nat. Rev. Drug Discov., 7, pp. 900-907
  • Schilling, C.H., Schuster, S., Palsson, B.O., Metabolic pathway analysis: Basic concepts and scientific applications in the post-genomic era (1999) Biotechnol. Prog., 15, pp. 296-303
  • Schmidtke, P., Barril, X., Understanding and predicting druggability. A high-throughput method for detection of drug binding sites (2010) J. Med. Chem., 53, pp. 5858-5867
  • Hopkins, A.L., Groom, C.R., The druggable genome (2002) Nat. Rev. Drug Discov., 1, pp. 727-730
  • Cheng, A.C., Coleman, R.G., Smyth, K.T., Structure-based maximal affinity model predicts small-molecule druggability (2007) Nat. Biotech., 25, pp. 71-75
  • Sheridan, R.P., Maiorov, V.N., Holloway, M.K., Drug-like density: A method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the protein data bank (2010) J. Chem. Inf. Model., 50, pp. 2029-2040
  • Davis, F.P., Barkan, D.T., Eswar, N., Host-pathogen protein interactions predicted by comparative modeling (2007) Protein Sci., 16, pp. 2585-2596
  • Kinnings, S.L., Xie, L., Fung, K.H., The mycobacterium tuberculosis drugome and its polypharmacological implications (2010) PLoS Comput. Biol., 6
  • Xie, L., Bourne, P.E., A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites (2007) BMC Bioinformatics, 8, p. S9
  • Halgren, T.A., Identifying and characterizing binding sites and assessing druggability (2009) J. Chem. Inf. Model., 49, pp. 377-389
  • Sassetti, C.M., Boyd, D.H., Rubin, E.J., Genes required for mycobacterial growth defined by high density mutagenesis (2003) Mol. Microbiol., 48, pp. 77-84
  • Sassetti, C.M., Rubin, E.J., Genetic requirements for mycobacterial survival during infection (2003) Proc. Natl Acad. Sci. USA, 100, pp. 12989-12994
  • Rengarajan, J., Bloom, B.R., Rubin, E.J., Genome-wide requirements for mycobacterium tuberculosis adaptation and survival in macrophages (2005) Proc. Natl Acad. Sci. USA, 102, pp. 8327-8332
  • Jamshidi, N., Palsson, B.O., Investigating the metabolic capabilities of mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets (2007) BMC Syst. Biol., 1, p. 26
  • Raman, K., Rajagopalan, P., Chandra, N., Flux balance analysis of mycolic acid pathway: Targets for anti-tubercular drugs (2005) PLoS Comput. Biol., 1, p. e46
  • Hasan, S., Daugelat, S., Rao, P.S.S., Prioritizing genomic drug targets in pathogens: Application to mycobacterium tuberculosis (2006) PLoS Comput. Biol., 2, pp. 0539-0550
  • Voskuil, M.I., Bartek, I.L., Visconti, K., The response of mycobacterium tuberculosis to reactive oxygen and nitrogen species (2011) Front. Microbiol., 2, p. 105
  • Betts, J.C., Lukey, P.T., Robb, L.C., Evaluation of a nutrient starvation model of mycobacterium tuberculosis persistence by gene and protein expression profiling (2002) Mol. Microbiol., 43, pp. 717-731
  • Hampshire, T., Soneji, S., Bacon, J., Stationary phase gene expression of mycobacterium tuberculosis following a progressive nutrient depletion: A model for persistent organisms? (2004) Tuberculosis, 84, pp. 228-238
  • Muttucumaru, D.G.N., Roberts, G., Hinds, J., Gene expression profile of mycobacterium tuberculosis in a non-replicating state (2004) Tuberculosis, 84, pp. 239-246
  • Boshoff, H.I., Barry, C.E., Tuberculosis - Metabolism and respiration in the absence of growth (2005) Nat. Rev. Microbiol., 3, pp. 70-80
  • Bairoch, A., Apweiler, R., Wu, C.H., The universal protein resource (UniProt) (2005) Nucleic Acids Res., 33, pp. D154-D159
  • Johnson, L.S., Eddy, S., Portugaly, E., Hidden Markov model speed heuristic and iterative HMM search procedure (2010) BMC Bioinformatics, 11, p. 431
  • Altschul, S.F., Gish, W., Miller, W., Basic local alignment search tool (1990) J. Mol. Biol., 215, pp. 403-410
  • Berman, H.M., Battistuz, T., Bhat, T.N., The protein data bank (2002) Acta Crystallogr. D. Biol. Crystallogr., 58, pp. 899-907
  • Cole, S.T., Brosch, R., Parkhill, J., Deciphering the biology of mycobacterium tuberculosis from the complete genome sequence (1998) Nature, 393, pp. 537-544
  • Murphy, D.J., Brown, J.R., Identification of gene targets against dormant phase mycobacterium tuberculosis infections (2007) BMC Infect. Dis., 7, p. 84
  • Griffin, J.E., Gawronski, J.D., DeJesus, M.A., Highresolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism (2011) PLoS Pathog., 7
  • Li, W., Godzik, A., Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences (2006) Bioinformatics, 22, pp. 1658-1659
  • Eswar, N., Eramian, D., Webb, B., Protein structure modeling with MODELLER (2008) Methods Mol. Biol., 426, pp. 145-159
  • Altschul, S.F., Madden, T.L., Schäffer, A.A., Gapped BLAST and PSI-BLAST: A new generation of protein database search programs (1997) Nucleic Acids Res., 25, pp. 3389-3402
  • Melo, F., Sali, A., Fold assessment for comparative protein structure modeling (2007) Protein Sci., 16, pp. 2412-2426
  • Benkert, P., Tosatto, S.C., Schomburg, D., QMEAN: A comprehensive scoring function for model quality assessment (2008) Proteins, 71, pp. 261-277
  • Le Guilloux, V., Schmidtke, P., Tuffery, P., Fpocket: An open source platform for ligand pocket detection (2009) BMC Bioinformatics, 10, p. 168
  • Porter, C.T., Bartlett, G.J., Thornton, J.M., The catalytic site atlas: A resource of catalytic sites and residues identified in enzymes using structural data (2004) Nucleic Acids Res., 32, pp. D129-D133
  • Bateman, A., Coin, L., Durbin, R., The pfam protein families database (2004) Nucleic Acids Res., 32, pp. D138-D141
  • Humphrey, W., Dalke, A., Schulten, K., VMD: Visual molecular dynamics (1996) J. Mol. Graph., 14, pp. 33-38
  • DeLano, W.L., (2002) The PyMOL Molecular Graphics System
  • Barril, X., Druggability predictions: Methods, limitations, and applications (2012) Wiley Interdiscip. Rev. Comput. Mol. Sci., 3, pp. 327-338
  • Krasowski, A., Muthas, D., Sarkar, A., DrugPred: A structure-based approach to predict protein druggability developed using an extensive nonredundant data set (2011) J. Chem. Inf. Model., 51, pp. 2829-2842
  • Henrich, S., Salo-Ahen, O.M., Huang, B., Computational approaches to identifying and characterizing protein binding sites for ligand design (2010) J. Mol. Recognit., 23, pp. 209-219
  • Volkamer, A., Griewel, A., Grombacher, T., Analyzing the topology of active sites: On the prediction of pockets and subpockets (2010) J. Chem. Inf. Model., 50, pp. 2041-2052
  • Pérot, S., Sperandio, O., Miteva, M.A., Druggable pockets and binding site centric chemical space: A paradigm shift in drug discovery (2010) Drug Discov. Today, 15, pp. 656-667
  • Volkamer, A., Kuhn, D., Grombacher, T., Combining global and local measures for structure-based druggability predictions (2012) J. Chem. Inf. Model., 52, pp. 360-372
  • Desaphy, J.R.M., Azdimousa, K., Kellenberger, E., Comparison and druggability prediction of protein-ligand binding sites from pharmacophore-annotated cavity shapes (2012) J. Chem. Inf. Model., 52, pp. 2287-2299
  • Perola, E., Herman, L., Weiss, J., Development of a rule-based method for the assessment of protein druggability (2012) J. Chem. Inf. Model., 52, pp. 1027-1038
  • Volkamer, A., Kuhn, D., Rippmann, F., DoGSiteScorer: A web server for automatic binding site prediction, analysis and druggability assessment (2012) Bioinformatics, 28, pp. 2074-2075
  • Raman, K., Yeturu, K., Chandra, N., TargetTB: A target identification pipeline for mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis (2008) BMC Syst. Biol., 2, p. 109
  • Jamshidi, N., Palsson, B.O., Investigating the metabolic capabilities of mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets (2007) BMC Syst. Biol., 1, p. 26
  • Kushwaha, S.K., Shakya, M., Protein interaction network analysis - Approach for potential drug target identification in mycobacterium tuberculosis (2010) J. Theor. Biol., 262, pp. 284-294
  • Joshi, K.R., Dhiman, H., Scaria, V., Tbvar: A comprehensive genome variation resource for mycobacterium tuberculosis (2013) Database, 2013

Citas:

---------- APA ----------
Radusky, L., Defelipe, L.A., Lanzarotti, E., Luque, J., Barril, X., Marti, M.A. & Turjanski, A.G. (2014) . TuberQ: A Mycobacterium tuberculosis protein druggability database. Database, 2014.
http://dx.doi.org/10.1093/database/bau035
---------- CHICAGO ----------
Radusky, L., Defelipe, L.A., Lanzarotti, E., Luque, J., Barril, X., Marti, M.A., et al. "TuberQ: A Mycobacterium tuberculosis protein druggability database" . Database 2014 (2014).
http://dx.doi.org/10.1093/database/bau035
---------- MLA ----------
Radusky, L., Defelipe, L.A., Lanzarotti, E., Luque, J., Barril, X., Marti, M.A., et al. "TuberQ: A Mycobacterium tuberculosis protein druggability database" . Database, vol. 2014, 2014.
http://dx.doi.org/10.1093/database/bau035
---------- VANCOUVER ----------
Radusky, L., Defelipe, L.A., Lanzarotti, E., Luque, J., Barril, X., Marti, M.A., et al. TuberQ: A Mycobacterium tuberculosis protein druggability database. Database. 2014;2014.
http://dx.doi.org/10.1093/database/bau035