TB Mobile: a mobile app for anti-tuberculosis molecules with known targets

BackgroundAn increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs.ResultsIn order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active.ConclusionsTB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts.

[1]  G. Besra,et al.  Identification of Novel Mt-Guab2 Inhibitor Series Active against M. tuberculosis , 2012, PloS one.

[2]  Sean Ekins,et al.  Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. , 2009, Drug discovery today.

[3]  Andreas Bender,et al.  Fishing the target of antitubercular compounds: in silico target deconvolution model development and validation. , 2009, Journal of proteome research.

[4]  Lynn Rasmussen,et al.  High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv. , 2012, Tuberculosis.

[5]  David Beer,et al.  A High-Throughput Screen To Identify Inhibitors of ATP Homeostasis in Non-replicating Mycobacterium tuberculosis , 2012, ACS chemical biology.

[6]  Peter M Woollard,et al.  Characterization of a Mycobacterium tuberculosis H37Rv transposon library reveals insertions in 351 ORFs and mutants with altered virulence. , 2002, Microbiology.

[7]  C. Nathan,et al.  Nonsteroidal anti-inflammatory drug sensitizes Mycobacterium tuberculosis to endogenous and exogenous antimicrobials , 2012, Proceedings of the National Academy of Sciences.

[8]  Ying Zhang,et al.  Pyrazinamide Inhibits Trans-Translation in Mycobacterium tuberculosis , 2011, Science.

[9]  Sean Ekins,et al.  A collaborative database and computational models for tuberculosis drug discovery. , 2010, Molecular bioSystems.

[10]  Lynn Rasmussen,et al.  High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv. , 2009, Tuberculosis.

[11]  Sean Ekins,et al.  Combining Cheminformatics Methods and Pathway Analysis to Identify Molecules with Whole-Cell Activity Against Mycobacterium Tuberculosis , 2012, Pharmaceutical Research.

[12]  Damiano Banfi,et al.  Leads for antitubercular compounds from kinase inhibitor library screens. , 2010, Tuberculosis.

[13]  Rebecca Voelker,et al.  MDR-TB has new drug foe after fast-track approval. , 2013, JAMA.

[14]  Christian Stolte,et al.  TB database: an integrated platform for tuberculosis research , 2008, Nucleic Acids Res..

[15]  W. Bishai,et al.  Accelerated detection of Mycobacterium tuberculosis genes essential for bacterial survival in guinea pigs, compared with mice. , 2007, The Journal of infectious diseases.

[16]  Harry E. Pence,et al.  Smart Phones, a Powerful Tool in the Chemistry Classroom. , 2011 .

[17]  Hinrich W. H. Göhlmann,et al.  A Diarylquinoline Drug Active on the ATP Synthase of Mycobacterium tuberculosis , 2005, Science.

[18]  P. Watnick,et al.  A High-Throughput Screen Identifies a New Natural Product with Broad-Spectrum Antibacterial Activity , 2012, PloS one.

[19]  D. Pompliano,et al.  Drugs for bad bugs: confronting the challenges of antibacterial discovery , 2007, Nature Reviews Drug Discovery.

[20]  Philippe Glaziou,et al.  Global tuberculosis control: lessons learnt and future prospects , 2012, Nature Reviews Microbiology.

[21]  Joel S. Freundlich,et al.  Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery. , 2011, Trends in Microbiology.

[22]  James C. Sacchettini,et al.  Drugs versus bugs: in pursuit of the persistent predator Mycobacterium tuberculosis , 2008, Nature Reviews Microbiology.

[23]  C. Ball,et al.  TB database 2010: overview and update. , 2010, Tuberculosis.

[24]  Sharmila Anishetty,et al.  Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis , 2005, Comput. Biol. Chem..

[25]  Lynn Rasmussen,et al.  Antituberculosis activity of the molecular libraries screening center network library. , 2009, Tuberculosis.

[26]  K. Kuhen,et al.  A chemical genetic screen in Mycobacterium tuberculosis identifies carbon-source-dependent growth inhibitors devoid of in vivo efficacy , 2010, Nature Communications.

[27]  Antony J. Williams,et al.  Cheminformatics workflows using mobile apps , 2013 .

[28]  Stewart T. Cole,et al.  High Content Screening Identifies Decaprenyl-Phosphoribose 2′ Epimerase as a Target for Intracellular Antimycobacterial Inhibitors , 2009, PLoS pathogens.

[29]  Christopher M. Sassetti,et al.  Genetic requirements for mycobacterial survival during infection , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Sean Ekins,et al.  Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. , 2010, Molecular bioSystems.

[31]  George Karypis,et al.  Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods , 2009, J. Chem. Inf. Model..

[32]  Kalidas Yeturu,et al.  targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis , 2008, BMC Systems Biology.

[33]  W. Yew,et al.  Current development and future prospects in chemotherapy of tuberculosis , 2010, Respirology.

[34]  R. Reynolds,et al.  High Throughput Screening for Inhibitors of Mycobacterium tuberculosis H 37 Rv , 2012 .

[35]  Takushi Kaneko,et al.  Challenges and opportunities in developing novel drugs for TB. , 2011, Future medicinal chemistry.

[36]  William R. Jacobs,et al.  Pyrazinamide inhibits the eukaryotic-like fatty acid synthetase I (FASI) of Mycobacterium tuberculosis , 2000, Nature Medicine.

[37]  Nicholas A. Be,et al.  Genetic requirements for the survival of tubercle bacilli in primates. , 2010, The Journal of infectious diseases.

[38]  Alex M. Clark,et al.  Basic primitives for molecular diagram sketching , 2010, J. Cheminformatics.

[39]  E. Rubin,et al.  Genes required for mycobacterial growth defined by high density mutagenesis , 2003, Molecular microbiology.

[40]  C. Bewley,et al.  Inhibition and kinetics of mycobacterium tuberculosis and mycobacterium smegmatis mycothiol-S-conjugate amidase by natural product inhibitors. , 2003, Bioorganic & medicinal chemistry.

[41]  R. Koski,et al.  New Classes of Alanine Racemase Inhibitors Identified by High-Throughput Screening Show Antimicrobial Activity against Mycobacterium tuberculosis , 2011, PloS one.

[42]  Alex M. Clark,et al.  Incorporating Green Chemistry Concepts into Mobile Chemistry Applications and Their Potential Uses , 2013 .

[43]  D. Bojanic,et al.  Impact of high-throughput screening in biomedical research , 2011, Nature Reviews Drug Discovery.

[44]  S. Cole,et al.  Towards a new tuberculosis drug: pyridomycin – nature's isoniazid , 2012, EMBO molecular medicine.

[45]  Eric Arnoult,et al.  The challenge of new drug discovery for tuberculosis , 2011, Nature.

[46]  Noriaki Iwase,et al.  Identification of novel inhibitors of M. tuberculosis growth using whole cell based high-throughput screening. , 2012, ACS chemical biology.

[47]  Sean Ekins,et al.  Redefining Cheminformatics with Intuitive Collaborative Mobile Apps , 2012, Molecular informatics.

[48]  D. Sherman,et al.  High-throughput Screening and Sensitized Bacteria Identify an M. tuberculosis Dihydrofolate Reductase Inhibitor with Whole Cell Activity , 2012, PloS one.

[49]  Sean Ekins,et al.  Mobile apps for chemistry in the world of drug discovery. , 2011, Drug discovery today.

[50]  W. Bishai,et al.  Designer Arrays for Defined Mutant Analysis To Detect Genes Essential for Survival of Mycobacterium tuberculosis in Mouse Lungs , 2005, Infection and Immunity.

[51]  R. Angeletti,et al.  Proteome-wide profiling of isoniazid targets in Mycobacterium tuberculosis. , 2006, Biochemistry.