A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium

Bacteria are increasingly resistant to existing antibiotics, which target a narrow range of pathways. New methods are needed to identify targets, including repositioning targets among distantly related species. We developed a novel combination of systems and structural modeling and bioinformatics to reposition known antibiotics and targets to new species. We applied this approach to Mycoplasma genitalium, a common cause of urethritis. First, we used quantitative metabolic modeling to identify enzymes whose expression affects the cellular growth rate. Second, we searched the literature for inhibitors of homologs of the most fragile enzymes. Next, we used sequence alignment to assess that the binding site is shared by M. genitalium, but not by humans. Lastly, we used molecular docking to verify that the reported inhibitors preferentially interact with M. genitalium proteins over their human homologs. Thymidylate kinase was the top predicted target and piperidinylthymines were the top compounds. Further work is needed to experimentally validate piperidinylthymines. In summary, combined systems and structural modeling is a powerful tool for drug repositioning.

[1]  Charlotte A Gaydos,et al.  Mycoplasma genitalium , 2017, The Journal of infectious diseases.

[2]  A. Varshavsky,et al.  In vivo half-life of a protein is a function of its amino-terminal residue. , 1986, Science.

[3]  Cédric Merlot,et al.  Computational toxicology--a tool for early safety evaluation. , 2010, Drug discovery today.

[4]  Jason A. Papin,et al.  Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease , 2012, BMC Systems Biology.

[5]  Desmond S. Lun,et al.  Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production , 2009, PLoS Comput. Biol..

[6]  Doo-Ho Cho,et al.  PDB-Ligand: a ligand database based on PDB for the automated and customized classification of ligand-binding structures , 2005, Nucleic Acids Res..

[7]  Atefeh Mousavi,et al.  Mycoplasma genitalium and cancer: a brief review. , 2013, Asian Pacific journal of cancer prevention : APJCP.

[8]  B. Stiles,et al.  Recent perspectives in the diagnosis and evidence-based treatment of Mycoplasma genitalium , 2012, Expert review of anti-infective therapy.

[9]  Alain Blanchard,et al.  Mycoplasmas and their host: emerging and re-emerging minimal pathogens. , 2013, Trends in microbiology.

[10]  W. Delano The PyMOL Molecular Graphics System , 2002 .

[11]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[12]  Stephan Wickles,et al.  Structural basis for TetM-mediated tetracycline resistance , 2012, Proceedings of the National Academy of Sciences.

[13]  Hinrich W. H. Göhlmann,et al.  Transcription profiles of the bacterium Mycoplasma pneumoniae grown at different temperatures. , 2003, Nucleic acids research.

[14]  Sunil Sethi,et al.  Mycoplasma genitalium: An emerging sexually transmitted pathogen , 2012, The Indian journal of medical research.

[15]  Piet Herdewijn,et al.  Synthesis and Evaluation of Thymidine-5′-O-monophosphate Analogues as Inhibitors of Mycobacterium tuberculosis Thymidylate Kinase. , 2003 .

[16]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[17]  島田 康司,et al.  Emergence of clinical strains of Mycoplasma genitalium harbouring alterations in ParC associated with fluoroquinolone resistance , 2011 .

[18]  Daniel Machado,et al.  Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism , 2014, PLoS Comput. Biol..

[19]  C. Hutchison,et al.  Essential genes of a minimal bacterium. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[20]  A. Charron,et al.  Tetracycline Resistance in Ureaplasma spp. and Mycoplasma hominis: Prevalence in Bordeaux, France, from 1999 to 2002 and Description of Two tet(M)-Positive Isolates of M. hominis Susceptible to Tetracyclines , 2007, Antimicrobial Agents and Chemotherapy.

[21]  S. Cole,et al.  Who will develop new antibacterial agents? , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[22]  Q. Cui,et al.  Thymidylate kinase: an old topic brings new perspectives. , 2013, Current medicinal chemistry.

[23]  L. Manhart,et al.  Mycoplasma genitalium: An emergent sexually transmitted disease? , 2013, Infectious disease clinics of North America.

[24]  Lei Shi,et al.  SABIO-RK—database for biochemical reaction kinetics , 2011, Nucleic Acids Res..

[25]  Diane Joseph-McCarthy,et al.  In vivo validation of thymidylate kinase (TMK) with a rationally designed, selective antibacterial compound. , 2012, ACS chemical biology.

[26]  Diane Joseph-McCarthy,et al.  Discovery of selective and potent inhibitors of gram-positive bacterial thymidylate kinase (TMK). , 2012, Journal of medicinal chemistry.

[27]  I. Khanna,et al.  Drug discovery in pharmaceutical industry: productivity challenges and trends. , 2012, Drug discovery today.

[28]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[29]  Staffan Eriksson,et al.  Synthesis and Biological Evaluation of Inhibitors of Thymidine Monophosphate Kinase from Bacillus Anthracis , 2008, Nucleosides, nucleotides & nucleic acids.

[30]  Markus J. Herrgård,et al.  Integrating high-throughput and computational data elucidates bacterial networks , 2004, Nature.

[31]  F. Pammolli,et al.  The productivity crisis in pharmaceutical R&D , 2011, Nature Reviews Drug Discovery.

[32]  E. Ruppin,et al.  Predicting selective drug targets in cancer through metabolic networks , 2011, Molecular systems biology.

[33]  Antje Chang,et al.  BRENDA in 2015: exciting developments in its 25th year of existence , 2014, Nucleic Acids Res..

[34]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[35]  J. Jensen,et al.  Mycoplasma genitalium testing pattern and macrolide resistance: a Danish nationwide retrospective survey. , 2014, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[36]  Nan Xiao,et al.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli , 2008, Bioinform..

[37]  Lei Xie,et al.  Structure-based systems biology for analyzing off-target binding. , 2011, Current opinion in structural biology.

[38]  Narayanan Eswar,et al.  Protein structure modeling with MODELLER. , 2008, Methods in molecular biology.

[39]  José Correa-Basurto,et al.  The importance of employing computational resources for the automation of drug discovery , 2015, Expert opinion on drug discovery.

[40]  A. Barabasi,et al.  Blueprint for antimicrobial hit discovery targeting metabolic networks , 2010, Proceedings of the National Academy of Sciences.

[41]  S. Lee,et al.  Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery , 2011, Molecular systems biology.

[42]  Paolo Di Tommaso,et al.  T-Coffee: Tree-based consistency objective function for alignment evaluation. , 2014, Methods in molecular biology.

[43]  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.

[44]  Tomer Shlomi,et al.  Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters , 2012, PLoS Comput. Biol..

[45]  David B Jackson,et al.  Drug profiling: knowing where it hits. , 2010, Drug discovery today.

[46]  T. Keating,et al.  Antibacterial inhibitors of Gram-positive thymidylate kinase: structure-activity relationships and chiral preference of a new hydrophobic binding region. , 2014, Journal of medicinal chemistry.

[47]  E. Cox,et al.  Antibacterial Drug Development: Challenges, Recent Developments, and Future Considerations , 2014, Clinical pharmacology and therapeutics.

[48]  Corey Nislow,et al.  Recent advances and method development for drug target identification. , 2010, Trends in pharmacological sciences.

[49]  Gerhard Hessler,et al.  Predictive in silico off-target profiling in drug discovery. , 2014, Future medicinal chemistry.

[50]  T. Keating,et al.  Sulfonylpiperidines as novel, antibacterial inhibitors of Gram-positive thymidylate kinase (TMK). , 2013, Bioorganic & medicinal chemistry letters.

[51]  Patrick Aloy,et al.  Structural systems pharmacology: the role of 3D structures in next-generation drug development. , 2013, Chemistry & biology.

[52]  Vinay Satish Kumar,et al.  A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189 , 2009, PLoS Comput. Biol..

[53]  C. Woese,et al.  The chemical composition and submicroscopic morphology of Mycoplasma gallisepticum, avian PPLO 5969. , 1962, Journal of molecular biology.

[54]  Eytan Ruppin,et al.  iMAT: an integrative metabolic analysis tool , 2010, Bioinform..

[55]  N. Price,et al.  Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis , 2010, Proceedings of the National Academy of Sciences.

[56]  D. Taylor-Robinson,et al.  Diagnosis and antimicrobial treatment of Mycoplasma genitalium infection: sobering thoughts , 2014, Expert review of anti-infective therapy.

[57]  S. Harbarth,et al.  Antibiotic research and development: business as usual? , 2015, The Journal of antimicrobial chemotherapy.

[58]  S. Tabrizi,et al.  Azithromycin treatment failure in Mycoplasma genitalium-positive patients with nongonococcal urethritis is associated with induced macrolide resistance. , 2008, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[59]  Jun Yong Choi,et al.  Structure guided development of novel thymidine mimetics targeting Pseudomonas aeruginosa thymidylate kinase: from hit to lead generation. , 2012, Journal of medicinal chemistry.

[60]  H. Lichtenthaler,et al.  Allicin, a naturally occurring antibiotic from garlic, specifically inhibits acetyl‐CoA synthetase , 1990, FEBS letters.