Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Predictive compound accumulation rules yield a broad-spectrum antibiotic

Nature volume 545, pages 299–304 (2017)Cite this article

Subjects

Abstract

Most small molecules are unable to rapidly traverse the outer membrane of Gram-negative bacteria and accumulate inside these cells, making the discovery of much-needed drugs against these pathogens challenging. Current understanding of the physicochemical properties that dictate small-molecule accumulation in Gram-negative bacteria is largely based on retrospective analyses of antibacterial agents, which suggest that polarity and molecular weight are key factors. Here we assess the ability of over 180 diverse compounds to accumulate in Escherichia coli. Computational analysis of the results reveals major differences from the retrospective studies, namely that the small molecules that are most likely to accumulate contain an amine, are amphiphilic and rigid, and have low globularity. These guidelines were then applied to convert deoxynybomycin, a natural product that is active only against Gram-positive organisms, into an antibiotic with activity against a diverse panel of multi-drug-resistant Gram-negative pathogens. We anticipate that these findings will aid in the discovery and development of antibiotics against Gram-negative bacteria.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Small-molecule accumulation in E. coli MG1655.
Figure 2: Primary amines aid small-molecule accumulation in E. coli.
Figure 3: Properties affecting small-molecule accumulation in E. coli.
Figure 4: Flexibility and globularity are important for accumulation in E. coli.
Figure 5: Conversion of a Gram-positive-only antibacterial into a compound that accumulates in E. coli and kills Gram-negative bacteria.

Similar content being viewed by others

References

  1. Payne, D. J., Gwynn, M. N., Holmes, D. J. & Pompliano, D. L. Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat. Rev. Drug Discov. 6, 29–40 (2007)

    CAS  PubMed  Google Scholar 

  2. Fischbach, M. A. & Walsh, C. T. Antibiotics for emerging pathogens. Science 325, 1089–1093 (2009)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Boucher, H. W. et al. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin. Infect. Dis. 48, 1–12 (2009)

    PubMed  Google Scholar 

  4. Lewis, K. Platforms for antibiotic discovery. Nat. Rev. Drug Discov. 12, 371–387 (2013)

    CAS  PubMed  Google Scholar 

  5. Nikaido, H. Prevention of drug access to bacterial targets: permeability barriers and active efflux. Science 264, 382–388 (1994)

    ADS  CAS  PubMed  Google Scholar 

  6. Silver, L. L. Challenges of antibacterial discovery. Clin. Microbiol. Rev. 24, 71–109 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Tommasi, R., Brown, D. G., Walkup, G. K., Manchester, J. I. & Miller, A. A. ESKAPEing the labyrinth of antibacterial discovery. Nat. Rev. Drug Discov. 14, 529–542 (2015)

    CAS  PubMed  Google Scholar 

  8. Nikaido, H. Molecular basis of bacterial outer membrane permeability revisited. Microbiol. Mol. Biol. Rev. 67, 593–656 (2003)

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Carpenter, T. S., Parkin, J. & Khalid, S. The free energy of small solute permeation through the Escherichia coli outer membrane has a distinctly asymmetric profile. J. Phys. Chem. Lett. 7, 3446–3451 (2016)

    CAS  PubMed  Google Scholar 

  10. Cowan, S. W. et al. Crystal structures explain functional properties of two E. coli porins. Nature 358, 727–733 (1992)

    ADS  CAS  PubMed  Google Scholar 

  11. O’Shea, R. & Moser, H. E. Physicochemical properties of antibacterial compounds: implications for drug discovery. J. Med. Chem. 51, 2871–2878 (2008)

    PubMed  Google Scholar 

  12. Brown, D. G., May-Dracka, T. L., Gagnon, M. M. & Tommasi, R. Trends and exceptions of physical properties on antibacterial activity for Gram-positive and Gram-negative pathogens. J. Med. Chem. 57, 10144–10161 (2014)

    CAS  PubMed  Google Scholar 

  13. Bazile, S., Moreau, N., Bouzard, D. & Essiz, M. Relationships among antibacterial activity, inhibition of DNA gyrase, and intracellular accumulation of 11 fluoroquinolones. Antimicrob. Agents Chemother. 36, 2622–2627 (1992)

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Davis, T. D., Gerry, C. J. & Tan, D. S. General platform for systematic quantitative evaluation of small-molecule permeability in bacteria. ACS Chem. Biol. 9, 2535–2544 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Nikaido, H., Rosenberg, E. Y. & Foulds, J. Porin channels in Escherichia coli: studies with β-lactams in intact cells. J. Bacteriol. 153, 232–240 (1983)

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhou, Y. et al. Thinking outside the ‘bug’: a unique assay to measure intracellular drug penetration in Gram-negative bacteria. Anal. Chem. 87, 3579–3584 (2015)

    CAS  PubMed  Google Scholar 

  17. Brown, D. M. & Acred, P. ‘Penbritin’—a new broad-spectrum antibiotic. BMJ 2, 197–198 (1961)

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Cai, H., Rose, K., Liang, L. H., Dunham, S. & Stover, C. Development of a liquid chromatography/mass spectrometry-based drug accumulation assay in Pseudomonas aeruginosa. Anal. Biochem. 385, 321–325 (2009)

    CAS  PubMed  Google Scholar 

  19. Vaara, M. Agents that increase the permeability of the outer membrane. Microbiol. Rev. 56, 395–411 (1992)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Huigens, R. W. III et al. A ring-distortion strategy to construct stereochemically complex and structurally diverse compounds from natural products. Nat. Chem. 5, 195–202 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Rafferty, R. J., Hicklin, R. W., Maloof, K. A. & Hergenrother, P. J. Synthesis of complex and diverse compounds through ring distortion of abietic acid. Angew. Chem. Int. Ed. 53, 220–224 (2014)

    CAS  Google Scholar 

  22. Garcia, A., Drown, B. S. & Hergenrother, P. J. Access to a structurally complex compound collection via ring distortion of the alkaloid sinomenine. Org. Lett. 18, 4852–4855 (2016)

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hicklin, R. W., López Silva, T. L. & Hergenrother, P. J. Synthesis of bridged oxafenestranes from pleuromutilin. Angew. Chem. Int. Ed. 53, 9880–9883 (2014)

    CAS  Google Scholar 

  24. Molecular Operating Environment (MOE) (Chemical Computing Group Inc., 2015)

  25. Kuenemann, M. A., Labbé, C. M., Cerdan, A. H. & Sperandio, O. Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors. Sci. Rep. 6, 23815 (2016)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kuenemann, M. A., Bourbon, L. M. L., Labbé, C. M., Villoutreix, B. O. & Sperandio, O. Which three-dimensional characteristics make efficient inhibitors of protein-protein interactions? J. Chem. Inf. Model. 54, 3067–3079 (2014)

    CAS  PubMed  Google Scholar 

  27. Mannhold, R ., Kubinyi, H ., Folkers, G . & Cruciani, G. Molecular Interaction Fields: Applications in Drug Discovery and ADME Prediction, Vol. 27 (John Wiley & Sons, 2006)

  28. Silver, L. L. A Gestalt approach to Gram-negative entry. Bioorg. Med. Chem. 24, 6379–6389 (2016)

    CAS  PubMed  Google Scholar 

  29. Yoshimura, F. & Nikaido, H. Diffusion of β-lactam antibiotics through the porin channels of Escherichia coli K-12. Antimicrob. Agents Chemother. 27, 84–92 (1985)

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Massey, E. H., Kitchell, B. S., Martin, L. D. & Gerzon, K. Antibacterial activity of 9(S)-erythromycylamine-aldehyde condensation products. J. Med. Chem. 17, 105–107 (1974)

    CAS  PubMed  Google Scholar 

  31. Hiramatsu, K. et al. Curing bacteria of antibiotic resistance: reverse antibiotics, a novel class of antibiotics in nature. Int. J. Antimicrob. Agents 39, 478–485 (2012)

    CAS  PubMed  Google Scholar 

  32. Parkinson, E. I. et al. Deoxynybomycins inhibit mutant DNA gyrase and rescue mice infected with fluoroquinolone-resistant bacteria. Nat. Commun. 6, 6947 (2015)

    ADS  CAS  PubMed  Google Scholar 

  33. Blattner, F. R. et al. The complete genome sequence of Escherichia coli K-12. Science 277, 1453–1462 (1997)

    CAS  PubMed  Google Scholar 

  34. Williams, K. J. & Piddock, L. J. Accumulation of rifampicin by Escherichia coli and Staphylococcus aureus. J. Antimicrob. Chemother. 42, 597–603 (1998)

    CAS  PubMed  Google Scholar 

  35. Kumarasamy, K. K. et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study. Lancet Infect. Dis. 10, 597–602 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Adler, M., Anjum, M., Andersson, D. I. & Sandegren, L. Influence of acquired β-lactamases on the evolution of spontaneous carbapenem resistance in Escherichia coli. J. Antimicrob. Chemother. 68, 51–59 (2013)

    CAS  PubMed  Google Scholar 

  37. Thanassi, D. G., Suh, G. S. & Nikaido, H. Role of outer membrane barrier in efflux-mediated tetracycline resistance of Escherichia coli. J. Bacteriol. 177, 998–1007 (1995)

  38. Chapman, J. S. & Georgopapadakou, N. H. Routes of quinolone permeation in Escherichia coli. Antimicrob. Agents Chemother. 32, 438–442 (1988)

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Mortimer, P. G. & Piddock, L. J. The accumulation of five antibacterial agents in porin-deficient mutants of Escherichia coli. J. Antimicrob. Chemother. 32, 195–213 (1993)

    CAS  PubMed  Google Scholar 

  40. Weiss, R. L. Protoplast formation in Escherichia coli. J. Bacteriol. 128, 668–670 (1976)

    CAS  PubMed  Google Scholar 

  41. Greenwood, J. R., Calkins, D., Sullivan, A. P. & Shelley, J. C. Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J. Comput. Aided Mol. Des. 24, 591–604 (2010)

    ADS  CAS  PubMed  Google Scholar 

  42. Shelley, J. C. et al. Epik: a software program for pKa prediction and protonation state generation for drug-like molecules. J. Comput. Aided Mol. Des. 21, 681–691 (2007)

    ADS  CAS  PubMed  Google Scholar 

  43. Labute, P. LowModeMD—implicit low-mode velocity filtering applied to conformational search of macrocycles and protein loops. J. Chem. Inf. Model. 50, 792–800 (2010)

    CAS  PubMed  Google Scholar 

  44. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001)

    MATH  Google Scholar 

  45. Firth, N. C., Brown, N. & Blagg, J. Plane of best fit: a novel method to characterize the three-dimensionality of molecules. J. Chem. Inf. Model. 52, 2516–2525 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Sastry, M., Lowrie, J. F., Dixon, S. L. & Sherman, W. Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J. Chem. Inf. Model. 50, 771–784 (2010)

    CAS  PubMed  Google Scholar 

  47. Sauer, W. H. & Schwarz, M. K. Molecular shape diversity of combinatorial libraries: a prerequisite for broad bioactivity. J. Chem. Inf. Comput. Sci. 43, 987–1003 (2003)

    CAS  PubMed  Google Scholar 

  48. Ziervogel, B. K. & Roux, B. The binding of antibiotics in OmpF porin. Structure 21, 76–87 (2013)

    CAS  PubMed  Google Scholar 

  49. Wu, E. L. et al. CHARMM-GUI Membrane Builder toward realistic biological membrane simulations. J. Comput. Chem. 35, 1997–2004 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Lee, J. et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405–413 (2016)

    CAS  PubMed  Google Scholar 

  51. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008)

    CAS  PubMed  Google Scholar 

  52. Lugtenberg, E. J. & Peters, R. Distribution of lipids in cytoplasmic and outer membranes of Escherichia coli K12. Biochim. Biophys. Acta 441, 38–47 (1976)

    CAS  PubMed  Google Scholar 

  53. Im, W. & Roux, B. Ion permeation and selectivity of OmpF porin: a theoretical study based on molecular dynamics, Brownian dynamics, and continuum electrodiffusion theory. J. Mol. Biol. 322, 851–869 (2002)

    CAS  PubMed  Google Scholar 

  54. Im, W. & Roux, B. Ions and counterions in a biological channel: a molecular dynamics simulation of OmpF porin from Escherichia coli in an explicit membrane with 1 M KCl aqueous salt solution. J. Mol. Biol. 319, 1177–1197 (2002)

    CAS  PubMed  Google Scholar 

  55. Varma, S., Chiu, S. W. & Jakobsson, E. The influence of amino acid protonation states on molecular dynamics simulations of the bacterial porin OmpF. Biophys. J. 90, 112–123 (2006)

    ADS  CAS  PubMed  Google Scholar 

  56. Feller, S. E., Yin, D., Pastor, R. W. & MacKerell, A. D., Jr. Molecular dynamics simulation of unsaturated lipid bilayers at low hydration: parameterization and comparison with diffraction studies. Biophys. J. 73, 2269–2279 (1997)

    CAS  PubMed  PubMed Central  Google Scholar 

  57. MacKerell, A. D. et al. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102, 3586–3616 (1998)

    CAS  PubMed  Google Scholar 

  58. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983)

    ADS  CAS  Google Scholar 

  59. Vanommeslaeghe, K. et al. CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996)

    CAS  PubMed  Google Scholar 

  61. Slama, J. T., Aboul-Ela, N. & Jacobson, M. K. Mechanism of inhibition of poly(ADP-ribose) glycohydrolase by adenosine diphosphate (hydroxymethyl)pyrrolidinediol. J. Med. Chem. 38, 4332–4336 (1995)

    CAS  PubMed  Google Scholar 

  62. Slama, J. T. et al. Specific inhibition of poly(ADP-ribose) glycohydrolase by adenosine diphosphate (hydroxymethyl)pyrrolidinediol. J. Med. Chem. 38, 389–393 (1995)

    CAS  PubMed  Google Scholar 

  63. Hajjar, E. et al. Bridging timescales and length scales: from macroscopic flux to the molecular mechanism of antibiotic diffusion through porins. Biophys. J. 98, 569–575 (2010)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  64. Danelon, C., Suenaga, A., Winterhalter, M. & Yamato, I. Molecular origin of the cation selectivity in OmpF porin: single channel conductances vs. free energy calculation. Biophys. Chem. 104, 591–603 (2003)

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank L. Li (Metabolomics Center, Roy J. Carver Biotechnology Center, UIUC) for LC–MS/MS analysis, L. Zechiedrich (Baylor College of Medicine) for the E. coli clinical isolates, C. Vanderpool (UIUC) for E. coli MG1655, and W. van der Donk (UIUC) for E. cloacae. We also thank K. Hull, S. Denmark, K. Morrison, R. Hicklin, H. Roth, B. Nakamura, A. Deets and A. Keyes for providing valuable compounds for the test set, and we thank M. Lambrecht for NMR expertise. This work was funded by the UIUC, including funds obtained through the Office of Technology Management Proof-of-Concept award. M.F.R. is a NSF predoctoral fellow. M.F.R., A.G., and R.L.S. are members of the NIH Chemistry-Biology Interface Training Grant (NRSA 1-T32-GM070421). A.P.R. is an NIH postdoctoral fellow. T.S. was supported by the Kao Corporation. Computer time was provided by the Texas Advanced Computing Center through Grant TG-CHE160050 funded by the NSF.

Author information

Authors and Affiliations

  1. Department of Chemistry and Institute for Genomic Biology, University of Illinois, Urbana, 61801, Illinois, USA

    Michelle F. Richter, Bryon S. Drown, Andrew P. Riley, Alfredo Garcia, Tomohiro Shirai, Riley L. Svec & Paul J. Hergenrother

Authors
  1. Michelle F. Richter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bryon S. Drown
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew P. Riley
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alfredo Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tomohiro Shirai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Riley L. Svec
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paul J. Hergenrother
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.J.H., M.F.R. and B.S.D. conceived the study. M.F.R. performed accumulation analyses. B.S.D. performed the computational analyses. M.F.R., B.S.D, A.P.R., A.G., T.S. and R.L.S. synthesized compounds in the test set. A.P.R. synthesized and tested DNM derivatives. P.J.H. supervised this research and wrote this manuscript with the assistance of M.F.R., B.S.D. and A.P.R.

Corresponding author

Correspondence to Paul J. Hergenrother.

Ethics declarations

Competing interests

The University of Illinois has filed patents on compounds related to this work.

Additional information

Reviewer Information Nature thanks P. A. Clemons, S. Khalid, S. L. Schreiber, O. Verho, G. D. Wright and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 ClogD7.4 and molecular weight do not correlate with accumulation of primary amines.

a, b, The accumulation of 68 primary amine containing compounds in E. coli MG1655 compared to ClogD7.4 (a) and molecular weight (b). High-accumulating controls (black x) = tetracycline, ciprofloxacin and chloramphenicol; low-accumulating controls (open circles) = novobiocin, erythromycin, rifampicin, clindamycin, mupirocin and fusidic acid, clindamycin and ampicillin. Statistical significance was determined by using a two-sample Welch’s t-test (one-tailed test, assuming unequal variance) relative to the negative controls. P values are relative to the average of the low-accumulating controls. *P < 0.05, **P < 0.01. Structures of all 68 compounds are shown in Supplementary Table 4. All experiments were performed in biological triplicate and s.e.m. values are provided in Supplementary Table 4.

Extended Data Figure 2 Training and performance of random forest classification model.

a, ROC plot comparing cross-validation methods in training classification model. b, Tuning plot optimizing for number of selected predictors. c, Confusion matrix of classification model using repeated tenfold cross-validation (n = 20). d, Relative importance of top 15 predictors. Predictors related to flexibility, shape, and amphiphilicity are highlighted in red.

Extended Data Figure 3 Accumulation of paired set of compounds containing one or two primary amines.

Error bars equal s.e.m. Statistical significance was determined by using a two-sample Student’s t-test (two-tailed test, assuming equal variance). *P < 0.05, **P < 0.01. All experiments were performed in biological triplicate.

Extended Data Figure 4 Additional scatter plots of small-molecule bacterial accumulators based on molecular shape descriptors.

a, Scatter plot of primary amines (compounds from Fig. 4a) according to rotatable bond count and average distance to the plane-of-best-fit (PBF)45. b, Scatter plot of primary amines (compounds from Fig. 4a) according to rotatable bond count and normalized principal moment of inertia (PMI1/molecular weight)47. c, Scatter plot of antibiotics (same compounds from Fig. 4b, plus β-lactams that are active against Gram-negative bacteria) according to rotatable bond count and PBF45. d, Scatter plot of antibiotics (same compounds from Fig. 4b, plus β-lactams that are active against Gram-negative bacteria) according to rotatable bond count and normalized principal moment of inertia (PMI1/molecular weight). e, Same plot as Fig. 4b, but this time the β-lactams that are active against Gram-negative bacteria are included. Structures of all antibiotics are in Supplementary Tables 6, 7.

Extended Data Figure 5 Mechanistic studies.

a, SDS–PAGE analysis of outer membrane proteins of E. coli BW25113 and E. coli BW25113 ∆ompR from the KEIO collection. E. coli outer membrane proteins were separated using SDS-12% PAGE, and visualized with Coomassie stain. Lane 1, protein ladder; lane 2, E. coli BW25113; lane 3, E. coli BW25113 ∆ompR. Representative of three replicates. b, Accumulation comparison of 12 compounds for E. coli ∆ompR vs parental strain E. coli BW25113. c, Small-molecule accumulation in protoplasts prepared from E. coli MG1655. Test compounds and known antibiotics are able to accumulate in the protoplast. ADP-HPD61,62, a non-cell permeable pyrophosphate, was used as a negative control. Compounds are indicated by the number assigned in the manuscript or the Supplementary Tables. For the compounds in the Supplementary Tables (whose structures are not shown in the manuscript), the structures are shown below the graph. Statistical significance was determined by using a two-sample Student’s t-test (two-tailed test, assuming equal variance). *P < 0.05, **P < 0.01, ***P < 0.001. All experiments were performed in biological triplicate. Error bars represent s.e.m.

Extended Data Figure 6

a, Distance of primary amine of compound 1 and constriction site residues in course of simulation. Stabilized distance at ~2 Å is indicative of hydrogen bonding. In repeated simulations molecules adopted similar pathways for traversing the porin while often hydrogen bonding with different hydrophilic residues. b, Displacement of Arg42 by compound 1 relative to initial coordinates was measured during course of simulation. c, Displacement of Arg42 by compound 13. d, Distance of primary amine of 6DNM-NH3 and constriction site residues in course of simulation. e, The displacement of Asp113 by 6DNM-NH3. f, Displacement of Asp113 by 6DNM.

Extended Data Figure 7 Snapshots of SMD simulation of compound translocation through OmpF.

a, Compound 1 is capable of making a key interaction with Asp113 (purple) that assists in movement past constriction site. This finding is in accord with previous reports of the importance of Asp113 in producing the cation selectivity of OmpF63,64. b, Same pose as a, viewed from above. c, Compound 13 makes no interactions with Asp113 and thus faces additional barriers to penetrance. d, Same pose as c, viewed from above. e, 6DNM-NH3 makes key H-bonding interactions with Asp113 (purple) that assists in the molecule’s passage through the constriction site. f, Same pose as e, viewed from above. g, 6DNM requires larger movement by peptide backbone in order to pass through constriction site. Asp113 is appreciably displaced by 6DNM. h, Same pose as g, viewed from above. In all simulations, the largest barrier to translocation is a series of stacked arginine residues (Arg 42, 82, and 132) that are depicted in blue.

Extended Data Figure 8

a, Gram-positive-only antibiotics with proper rotatable bond and globularity scores can be converted to broad-spectrum drugs via strategic placement of a primary amine, as demonstrated by ampicillin. However, for drugs that do not possess the proper flexibility and three-dimensionality parameters, such as erythromycin, addition of the amine does not broaden the antibacterial spectrum. b, Strategic placement of a primary amine (but not an acid or amide) on 6DNM leads to 6DNM-NH3, a broad-spectrum antibacterial. 6DNM and three derivatives were synthesized and evaluated against a panel of Gram-positive and Gram-negative organisms; ciprofloxacin was also evaluated. Flexibility (as measured by the number of rotatable bonds, RB), globularity (Glob), molecular weight, ClogD7.4, and E. coli accumulation (in nmol per 1012 CFUs) is provided. The s.e.m. for accumulation values are reported. MIC values were determined using the micro-dilution broth method as outlined by the Clinical and Laboratory Standards Institute and are listed in μg ml−1. All experiments were performed in biological triplicate.

Extended Data Figure 9 Some FDA-approved drugs or compounds in phase II or III clinical trials that are good candidates for derivative synthesis and expansion to broad-spectrum agents.

Placement of a primary amine at a position that does not alter interaction with the biological target is predicted to provide compounds that accumulate in and are active against Gram-negative bacteria.

Extended Data Figure 10 Primary amines are largely absent from many screening collections.

Comparison of the abundance of primary amines, secondary amines, tertiary amines, and carboxylic acids in the natural products subset of the ZINC database, the Molecular Libraries Small-molecule Repository – Natural Products (MLSMR-NP), and the Chembridge Microformat library.

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data – see contents page for details. (PDF 4059 kb)

Supplementary Tables

This file contains Supplementary Tables 1-4, 6, and 7. (PDF 2156 kb)

Supplementary Tables

This file contains Supplementary Table 5. (XLSX 257 kb)

Supplementary Information

This file contains the full legends for Supplementary Tables 1-7. (PDF 38 kb)

Supplementary Data

This file contains NMR spectra. (PDF 6979 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Richter, M., Drown, B., Riley, A. et al. Predictive compound accumulation rules yield a broad-spectrum antibiotic. Nature 545, 299–304 (2017). https://doi.org/10.1038/nature22308

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature22308

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing