Metal–ligand interactions in drug design

Abstract

The fast-growing body of experimental data on metalloenzymes and organometallic compounds is fostering the exploitation of metal–ligand interactions for the design of new drugs. Atomistic understanding of the metal–ligand interactions will help us identify potent metalloenzyme inhibitors and metallodrugs. Static docking calculations have proved effective in identifying hit compounds that target metalloproteins. However, the flexibility, dynamics and electronic structure of metal-centred complexes pose difficult challenges for shaping metal–ligand interactions in structure-based drug design. In this respect, once-prohibitive quantum mechanics-based strategies and extensive molecular simulations are rapidly becoming practical approaches for fast-paced drug discovery. These methods account for ligand exchange and structural flexibility at metal-centred complexes and provide good estimates of the thermodynamics and kinetics of metal-aided drug binding. This Perspective examines the successes, limitations and new avenues for modelling metalloenzyme inhibitors and metallodrugs to further explore and expand the unconventional chemical space of these distinctive drugs.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Metal–ligand interactions in metallodrugs and metalloenzyme inhibitors.
Fig. 2: Metal–ligand interactions in SBDD.
Fig. 3: Methods to model metal–ligand interactions in structure-based drug design.
Fig. 4: Structures of metalloenzyme inhibitors complexed to their target.
Fig. 5: Binding free energy profile of two sulfonamide inhibitors of the zinc CAII metalloenzyme.
Fig. 6: Structure of a nucleosome core particle bound to metallodrugs.

References

  1. 1.

    Mjos, K. D. & Orvig, C. Metallodrugs in medicinal inorganic chemistry. Chem. Rev. 114, 4540–4563 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. 2.

    Meggers, E. Exploring biologically relevant chemical space with metal complexes. Curr. Opin. Chem. Biol. 11, 287–292 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. 3.

    Rosenberg, B., Van Camp, L. & Krigas, T. Inhibition of cell division in Escherichia coli by electrolysis products from a platinum electrode. Nature 205, 698–699 (1965).

    Article  CAS  Google Scholar 

  4. 4.

    Johnstone, T. C., Suntharalingam, K. & Lippard, S. J. The next generation of platinum drugs: targeted Pt(II) agents, nanoparticle delivery, and Pt(IV) prodrugs. Chem. Rev. 116, 3436–3486 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kelland, L. The resurgence of platinum-based cancer chemotherapy. Nat. Rev. Cancer 7, 573–584 (2007).

    Article  CAS  PubMed  Google Scholar 

  6. 6.

    Messori, L. & Merlino, A. Cisplatin binding to proteins: a structural perspective. Coord. Chem. Rev. 315, 67–89 (2016).

    Article  CAS  Google Scholar 

  7. 7.

    Jamieson, E. R. & Lippard, S. J. Structure, recognition, and processing of cisplatin–DNA adducts. Chem. Rev. 99, 2467–2498 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Dörr, M. & Meggers, E. Metal complexes as structural templates for targeting proteins. Curr. Opin. Chem. Biol. 19, 76–81 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. 9.

    Dyson, P. J. & Sava, G. Metal-based antitumour drugs in the post genomic era. Dalton Trans. 2006, 1929–1933 (2006).

    Article  CAS  Google Scholar 

  10. 10.

    Hambley, T. W. Developing new metal-based therapeutics: Challenges and opportunities. Dalton Trans. 2007, 4929–4937 (2007).

    Article  CAS  Google Scholar 

  11. 11.

    Haas, K. L. & Franz, K. J. Application of metal coordination chemistry to explore and manipulate cell biology. Chem. Rev. 109, 4921–4960 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Barry, N. P. E. & Sadler, P. J. Exploration of the medical periodic table: towards new targets. Chem. Commun. 49, 5106–5131 (2013).

    Article  CAS  Google Scholar 

  13. 13.

    Sasmal, P. K., Streu, C. N. & Meggers, E. Metal complex catalysis in living biological systems. Chem. Commun. 49, 1581–1587 (2013).

    Article  CAS  Google Scholar 

  14. 14.

    Soldevila-Barreda, J. J. & Sadler, P. J. Approaches to the design of catalytic metallodrugs. Curr. Opin. Chem. Biol. 25, 172–183 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. 15.

    Cohen, S. M. A bioinorganic approach to fragment-based drug discovery targeting metalloenzymes. Acc. Chem. Res. 50, 2007–2016 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Yang, Y. et al. Metalloprotein inhibitors for the treatment of human diseases. Curr. Top. Med. Chem. 16, 384–396 (2015).

    Article  CAS  Google Scholar 

  17. 17.

    Martin, D. P., Puerta, D. T. & Cohen, S. M. in Ligand Design in Medicinal Inorganic Chemistry (ed. Storr, T.) 375–403 (Wiley, Chichester, 2014).

  18. 18.

    Supuran, C. T. Advances in structure-based drug discovery of carbonic anhydrase inhibitors. Expert Opin. Drug Discov. 12, 61–88 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. 19.

    Alterio, V., Di Fiore, A., D’Ambrosio, K., Supuran, C. T. & De Simone, G. Multiple binding modes of inhibitors to carbonic anhydrases: how to design specific drugs targeting 15 different isoforms? Chem. Rev. 112, 4421–4468 (2012).

    Article  CAS  PubMed  Google Scholar 

  20. 20.

    Levin, M., Udi, Y., Solomonov, I. & Sagi, I. Next generation matrix metalloproteinase inhibitors — novel strategies bring new prospects. Biochim. Biophys. Acta Mol. Cell Res. 1864, 1927–1939 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. 21.

    Hu, J., Van den Steen, P. E., Sang, Q.-X. A. & Opdenakker, G. Matrix metalloproteinase inhibitors as therapy for inflammatory and vascular diseases. Nat. Rev. Drug Discov. 6, 480–498 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. 22.

    Cathcart, J., Pulkoski-Gross, A. & Cao, J. Targeting matrix metalloproteinases in cancer: bringing new life to old ideas. Genes Dis. 2, 26–34 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Coates, D. The angiotensin converting enzyme (ACE). Int. J. Biochem. Cell Biol. 35, 769–773 (2003).

    Article  CAS  PubMed  Google Scholar 

  24. 24.

    Hai, Y. & Christianson, D. W. Histonedeacetylase 6 structure and molecular basis of catalysis and inhibition. Nat. Chem. Biol. 12, 741–747 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Xu, W. S., Parmigiani, R. B. & Marks, P. A. Histone deacetylase inhibitors: molecular mechanisms of action. Oncogene 26, 5541–5552 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. 26.

    Gryder, B. E., Sodji, Q. H. & Oyelere, A. K. Targeted cancer therapy: giving histone deacetylase inhibitors all they need to succeed. Future Med. Chem. 4, 505–524 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Di Santo, R. Inhibiting the HIV integration process: past, present, and the future. J. Med. Chem. 57, 539–566 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. 28.

    Quashie, P. K., Sloan, R. D. & Wainberg, M. A. Novel therapeutic strategies targeting HIV integrase. BMC Med. 10, 34 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Anzellotti, A. I. & Farrell, N. P. Zinc metalloproteins as medicinal targets. Chem. Soc. Rev. 37, 1629–1651 (2008).

    Article  CAS  PubMed  Google Scholar 

  30. 30.

    Pommier, Y., Johnson, A. A. & Marchand, C. Integrase inhibitors to treat HIV/Aids. Nat. Rev. Drug Discov. 4, 236–248 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. 31.

    Russo, N. Salahub, D. R. (eds). Metal–Ligand Interactions (Springer, Netherlands, 1996).

    Google Scholar 

  32. 32.

    Thompson, K. H. Boon and bane of metal ions in medicine. Science 300, 936–939 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. 33.

    Adhireksan, Z. et al. Allosteric cross-talk in chromatin can mediate drug-drug synergy. Nat. Commun. 8, 14860 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Exell, J. C. et al. Cellularly active N-hydroxyurea FEN1 inhibitors block substrate entry to the active site. Nat. Chem. Biol. 12, 815–821 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Summa, V. et al. Discovery of Raltegravir, a potent, selective orally bioavailable HIV-integrase inhibitor for the treatment of HIV-AIDS infection. J. Med. Chem. 51, 5843–5855 (2008).

    Article  CAS  PubMed  Google Scholar 

  36. 36.

    Leonard, P. G. et al. SF2312 is a natural phosphonate inhibitor of enolase. Nat. Chem. Biol. 12, 1053–1058 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Johansson, C. et al. Structural analysis of human KDM5B guides histone demethylase inhibitor development. Nat. Chem. Biol. 12, 539–545 (2016).

    Article  CAS  PubMed  Google Scholar 

  38. 38.

    Puerta, D. T., Schames, J. R., Henchman, R. H., McCammon, J. A. & Cohen, S. M. From model complexes to metalloprotein inhibition: a synergistic approach to structure-based drug discovery. Angew. Chemie Int. Edn 42, 3772–3774 (2003).

    Article  CAS  Google Scholar 

  39. 39.

    Jorgensen, W. L. The many roles of computation in drug discovery. Science 303, 1813–1818 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. 40.

    De Vivo, M., Masetti, M., Bottegoni, G. & Cavalli, A. The role of molecular dynamics and related methods in drug discovery. J. Med. Chem. 59, 4035–4061 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. 41.

    Kitchen, D. B., Decornez, H., Furr, J. R. & Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935–949 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. 42.

    Bruno, E. et al. Probing molecular interactions between human carbonic anhydrases (hCAs) and a novel class of benzenesulfonamides. J. Med. Chem. 60, 4316–4326 (2017).

    Article  CAS  PubMed  Google Scholar 

  43. 43.

    Choi, J. Y. et al. Structure-based design and synthesis of potent and selective matrix metalloproteinase 13 inhibitors. J. Med. Chem. 60, 5816–5825 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. 44.

    Vernekar, S. K. V. et al. Double-winged 3-hydroxypyrimidine-2,4-diones: Potent and selective inhibition against HIV-1 RNase H with significant antiviral activity. J. Med. Chem. 60, 5045–5056 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    De Vivo, M. & Cavalli, A. Recent advances in dynamic docking for drug discovery. Wiley Interdiscip. Rev. Comput. Mol. Sci. 7, e1320 (2017).

    Article  CAS  Google Scholar 

  46. 46.

    De Vivo, M. Bridging quantum mechanics and structure-based drug design. Front. Biosci. (Landmark Edn) 16, 1619–1633 (2011).

    Article  Google Scholar 

  47. 47.

    Meggers, E. Targeting proteins with metal complexes. Chem. Commun. 2009, 1001–1010 (2009).

    Article  CAS  Google Scholar 

  48. 48.

    Louie, A. Y. & Meade, T. J. Metal complexes as enzyme inhibitors. Chem. Rev. 99, 2711–2734 (1999).

    Article  CAS  PubMed  Google Scholar 

  49. 49.

    Meggers, E. Exploiting octahedral stereocenters: From enzyme inhibition to asymmetric photoredox catalysis. Angew. Chemie Int. Edn 56, 5668–5675 (2017).

    Article  CAS  Google Scholar 

  50. 50.

    Guo, Z. & Sadler, P. J. Metals in Medicine. Angew. Chemie Int. Edn 38, 1512–1531 (1999).

    Article  CAS  Google Scholar 

  51. 51.

    Ellahioui, Y., Prashar, S. & Gómez-Ruiz, S. Anticancer applications and recent investigations of metallodrugs based on gallium, tin and titanium. Inorganics 5, 4 (2017).

    Article  CAS  Google Scholar 

  52. 52.

    Bullock, A. N. et al. Crystal structure of the PIM2 kinase in complex with an organoruthenium inhibitor. PLOS ONE 4, e7112 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Bregman, H., Carroll, P. J. & Meggers, E. Rapid access to unexplored chemical space by ligand scanning around a ruthenium center: discovery of potent and selective protein kinase inhibitors. J. Am. Chem. Soc. 128, 877–884 (2006).

    Article  CAS  PubMed  Google Scholar 

  54. 54.

    Maksimoska, J. et al. Similar biological activities of two isostructural ruthenium and osmium complexes. Chem. A Eur. J. 14, 4816–4822 (2008).

    Article  CAS  Google Scholar 

  55. 55.

    Patra, M. & Gasser, G. The medicinal chemistry of ferrocene and its derivatives. Nat. Rev. Chem. 1, 66 (2017).

    Article  CAS  Google Scholar 

  56. 56.

    Salmon, A. J., Williams, M. L., Hofmann, A. & Poulsen, S. Protein crystal structures with ferrocene and ruthenocene-based enzyme inhibitors. Chem. Commun. 48, 2328 (2012).

    Article  CAS  Google Scholar 

  57. 57.

    Trondl, R. et al. NKP-1339, the first ruthenium-based anticancer drug on the edge to clinical application. Chem. Sci. 5, 2925–2932 (2014).

    Article  CAS  Google Scholar 

  58. 58.

    Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W. & Jhoti, H. Twenty years on: The impact of fragments on drug discovery. Nat. Rev. Drug Discov. 15, 605–619 (2016).

    Article  CAS  PubMed  Google Scholar 

  59. 59.

    Jacobsen, J. A., Fullagar, J. L., Miller, M. T. & Cohen, S. M. Identifying chelators for metalloprotein inhibitors using a fragment-based approach. J. Med. Chem. 54, 591–602 (2011).

    Article  CAS  PubMed  Google Scholar 

  60. 60.

    Martin, D. P., Hann, Z. S. & Cohen, S. M. Metalloprotein–inhibitor binding: human carbonic anhydrase II as a model for probing metal–ligand interactions in a metalloprotein active site. Inorg. Chem. 52, 12207–12215 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. 61.

    Congreve, M., Chessari, G., Tisi, D. & Woodhead, A. J. Recent developments in fragment-based drug discovery. J. Med. Chem. 51, 3661–3680 (2008).

    Article  CAS  PubMed  Google Scholar 

  62. 62.

    Li, J. et al. Capzimin is a potent and specific inhibitor of proteasome isopeptidase Rpn11. Nat. Chem. Biol. 13, 486–493 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Perez, C. et al. Discovery of an inhibitor of the proteasome subunit Rpn11. J. Med. Chem. 60, 1343–1361 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Day, J. A. & Cohen, S. M. Investigating the selectivity of metalloenzyme inhibitors. J. Med. Chem. 56, 7997–8007 (2013).

    Article  CAS  PubMed  Google Scholar 

  65. 65.

    Chen, Y. & Cohen, S. M. Investigating the selectivity of metalloenzyme inhibitors in the presence of competing metalloproteins. ChemMedChem 10, 1733–1738 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Martin, D. P., Blachly, P. G., McCammon, J. A. & Cohen, S. M. Exploring the influence of the protein environment on metal-binding pharmacophores. J. Med. Chem. 57, 7126–7135 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Supuran, C. T. How many carbonic anhydrase inhibition mechanisms exist? J. Enzyme Inhib. Med. Chem. 31, 345–360 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. 68.

    Cadoni, R. et al. Exploring heteroaryl-pyrazole carboxylic acids as human carbonic anhydrase XII inhibitors. ACS Med. Chem. Lett. 8, 941–946 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Forli, S. & Olson, A. J. A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking. J. Med. Chem. 55, 623–638 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Hsu, K. et al. Novel class IIa-selective histone deacetylase inhibitors discovered using an in silico virtual screening approach. Sci. Rep 7, 3228 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Negmeldin, A. T., Padige, G., Bieliauskas, A. V. & Pflum, M. K. H. Structural requirements of HDAC inhibitors: SAHA analogues modified at the C2 position display HDAC6/8 selectivity. ACS Med. Chem. Lett. 8, 281–286 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Carcelli, M. et al. N-Acylhydrazone inhibitors of influenza virus PA endonuclease with versatile metal binding modes. Sci. Rep. 6, 31500 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Liénard, B. M. R. et al. Structural basis for the broad-spectrum inhibition of metallo-β-lactamases by thiols. Org. Biomol. Chem. 6, 2282 (2008).

    Article  CAS  PubMed  Google Scholar 

  74. 74.

    Liu, X.-L., Shi, Y., Kang, J. S., Oelschlaeger, P. & Yang, K.-W. Amino acid thioester derivatives: a highly promising scaffold for the development of metallo-β-lactamase L1 inhibitors. ACS Med. Chem. Lett. 6, 660–664 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Yang, S.-K., Kang, J. S., Oelschlaeger, P. & Yang, K.-W. Azolylthioacetamide: a highly promising scaffold for the development of metallo-β-lactamase inhibitors. ACS Med. Chem. Lett. 6, 455–460 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Chang, Y.-N. et al. Carbamylmethyl mercaptoacetate thioether: a novel scaffold for the development of L1 metallo-β-lactamase inhibitors. ACS Med. Chem. Lett. 8, 527–532 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Cain, R. et al. In silico fragment-based design identifies subfamily B1 metallo-β-lactamase inhibitors. J. Med. Chem. 61, 1255–1260 (2018).

    Article  CAS  PubMed  Google Scholar 

  78. 78.

    Hu, X. & Shelver, W. H. Docking studies of matrix metalloproteinase inhibitors: zinc parameter optimization to improve the binding free energy prediction. J. Mol. Graph. Model. 22, 115–126 (2003).

    Article  CAS  PubMed  Google Scholar 

  79. 79.

    Irwin, J. J., Raushel, F. M. & Shoichet, B. K. Virtual screening against metalloenzymes for inhibitors and substrates. Biochemistry 44, 12316–12328 (2005).

    Article  CAS  PubMed  Google Scholar 

  80. 80.

    Chen, D. et al. Accounting for ligand-bound metal ions in docking small molecules on adenylyl cyclase toxins. Proteins 67, 593–605 (2007).

    Article  CAS  PubMed  Google Scholar 

  81. 81.

    Santos-Martins, D., Forli, S., Ramos, M. J. & Olson, A. J. AutoDock4Zn: an improved AutoDock force field for small-molecule docking to zinc metalloproteins. J. Chem. Inf. Model. 54, 2371–2379 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Tamames, B., Sousa, S. F., Tamames, J., Fernandes, P. A. & Ramos, M. J. Analysis of zinc-ligand bond lengths in metalloproteins: trends and patterns. Proteins 69, 466–475 (2007).

    Article  CAS  PubMed  Google Scholar 

  83. 83.

    Röhrig, U. F., Grosdidier, A., Zoete, V. & Michielin, O. Docking to heme proteins. J. Comput. Chem. 28, 2305–2315 (2009).

    Google Scholar 

  84. 84.

    Caporuscio, F., Rastelli, G., Imbriano, C. & Del Rio, A. Structure-based design of potent aromatase inhibitors by high-throughput docking. J. Med. Chem. 54, 4006–4017 (2011).

    Article  CAS  PubMed  Google Scholar 

  85. 85.

    Zheng, Z. & Merz, K. M. Ligand identification scoring algorithm (LISA). J. Chem. Inf. Model. 51, 1296–1306 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Bai, F. et al. An accurate metalloprotein-specific scoring function and molecular docking program devised by a dynamic sampling and iteration optimization strategy. J. Chem. Inf. Model. 55, 833–847 (2015).

    Article  CAS  PubMed  Google Scholar 

  87. 87.

    Adeniyi, A. A. & Soliman, M. E. S. Implementing QM in docking calculations: is it a waste of computational time? Drug Discov. Today 22, 1216–1223 (2017).

    Article  CAS  PubMed  Google Scholar 

  88. 88.

    Raha, K. et al. The role of quantum mechanics in structure-based drug design. Drug Discov. Today 12, 725–731 (2007).

    Article  CAS  PubMed  Google Scholar 

  89. 89.

    Dick, B. L., Patel, A., McCammon, J. A. & Cohen, S. M. Effect of donor atom identity on metal-binding pharmacophore coordination. JBIC J. Biol. Inorg. Chem. 22, 605–613 (2017).

    Article  CAS  PubMed  Google Scholar 

  90. 90.

    Sousa, S. F., Fernandes, P. A. & Ramos, M. J. The carboxylate shift in zinc enzymes: a computational study. J. Am. Chem. Soc. 129, 1378–1385 (2007).

    Article  CAS  PubMed  Google Scholar 

  91. 91.

    Laitaoja, M., Valjakka, J. & Jänis, J. Zinc coordination spheres in protein structures. Inorg. Chem. 52, 10983–10991 (2013).

    Article  CAS  PubMed  Google Scholar 

  92. 92.

    Ribeiro, A. J. M., Ramos, M. J. & Fernandes, P. A. The catalytic mechanism of HIV-1 integrase for DNA 3ʹ-end processing established by QM/MM calculations. J. Am. Chem. Soc. 134, 13436–13447 (2012).

    Article  CAS  PubMed  Google Scholar 

  93. 93.

    Cavalli, A., De Vivo, M. & Recanatini, M. Density functional study of the enzymatic reaction catalyzed by a cyclin-dependent kinase. Chem. Commun. 0, 1308–1309 (2003).

    Article  CAS  Google Scholar 

  94. 94.

    Sousa, S. F. et al. Application of quantum mechanics/molecular mechanics methods in the study of enzymatic reaction mechanisms. Wiley Interdiscip. Rev. Comput. Mol. Sci. 7, e1281 (2017).

    Article  CAS  Google Scholar 

  95. 95.

    Van Der Kamp, M. W. & Mulholland, A. J. Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry 52, 2708–2728 (2013).

    Article  CAS  PubMed  Google Scholar 

  96. 96.

    Dreyer, J. et al. in Simulating Enzyme Reactivity (eds Tunon, I. & Moliner, V.) 294–339 (Royal Society of Chemistry, London, 2016).

  97. 97.

    Schramm, V. L. Transition states and transition state analogue interactions with enzymes. Acc. Chem. Res. 48, 1032–1039 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Roston, D. & Cui, Q. QM/MM analysis of transition states and transition state analogues in metalloenzymes. Methods Enzymol. 21, 213–250 (2016).

    Article  Google Scholar 

  99. 99.

    Raha, K. & Merz, K. M. A quantum mechanics-based scoring function: study of zinc ion-mediated ligand binding. J. Am. Chem. Soc. 126, 1020–1021 (2004).

    Article  CAS  PubMed  Google Scholar 

  100. 100.

    Cho, A. E. & Rinaldo, D. Extension of QM/MM docking and its applications to metalloproteins. J. Comput. Chem. 30, 2609–2616 (2009).

    Article  CAS  PubMed  Google Scholar 

  101. 101.

    Hayik, S. A., Dunbrack, R. & Merz, K. M. Mixed quantum mechanics/molecular mechanics scoring function to predict protein–ligand binding affinity. J. Chem. Theory Comput. 6, 3079–3091 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Chaskar, P., Zoete, V. & Röhrig, U. F. Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function. J. Chem. Inf. Model. 54, 3137–3152 (2014).

    Article  CAS  PubMed  Google Scholar 

  103. 103.

    Chaskar, P., Zoete, V. & Röhrig, U. F. On-the-fly QM/MM docking with attracting cavities. J. Chem. Inf. Model. 57, 73–84 (2017).

    Article  CAS  PubMed  Google Scholar 

  104. 104.

    Pecina, A. et al. The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein–ligand interactions and implicit COSMO solvation. Chem. Commun. 52, 3312–3315 (2016).

    Article  CAS  Google Scholar 

  105. 105.

    Schwarz, G., Mendel, R. R. & Ribbe, M. W. Molybdenum cofactors, enzymes and pathways. Nature 460, 839–847 (2009).

    Article  CAS  PubMed  Google Scholar 

  106. 106.

    Khandelwal, A. et al. A Combination of docking, QM/MM methods, and MD simulation for binding affinity estimation of metalloprotein ligands. J. Med. Chem. 48, 5437–5447 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Martin, D. P. et al. ‘Unconventional’ coordination chemistry by metal chelating fragments in a metalloprotein active site. J. Am. Chem. Soc. 136, 5400–5406 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Adeniyi, A. A. & Ajibade, P. A. Comparing the suitability of autodock, gold and glide for the docking and predicting the possible targets of Ru(II)-based complexes as anticancer agents. Molecules 18, 3760–3778 (2013).

    Article  CAS  PubMed  Google Scholar 

  109. 109.

    Vyas, N. A. et al. Ruthenium(II) polypyridyl complexes with hydrophobic ancillary ligand as Aβ aggregation inhibitors. Eur. J. Med. Chem. 121, 793–802 (2016).

    Article  CAS  PubMed  Google Scholar 

  110. 110.

    Sciortino, G. et al. Elucidation of binding site and chiral specificity of oxidovanadium drugs with lysozyme through theoretical calculations. Inorg. Chem. 56, 12938–12951 (2017).

    Article  CAS  PubMed  Google Scholar 

  111. 111.

    Yang, C. et al. Anticancer osmium complex inhibitors of the HIF-1α and p300 protein-protein interaction. Sci. Rep. 7, 42860 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. 112.

    Bradford, S. S., Ross, M. J., Fidai, I. & Cowan, J. A. Insight into the recognition, binding, and reactivity of catalytic metallodrugs targeting stem loop IIb of hepatitis C IRES RNA. ChemMedChem 9, 1275–1285 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. 113.

    Casini, A. et al. Emerging protein targets for anticancer metallodrugs: inhibition of thioredoxin reductase and cathepsin B by antitumor ruthenium(II)–arene compounds. J. Med. Chem. 51, 6773–6781 (2008).

    Article  CAS  PubMed  Google Scholar 

  114. 114.

    Ortega-Carrasco, E., Lledós, A. & Maréchal, J.-D. Assessing protein-ligand docking for the binding of organometallic compounds to proteins. J. Comput. Chem. 35, 192–198 (2014).

    Article  CAS  PubMed  Google Scholar 

  115. 115.

    Sciortino, G., Rodríguez-Guerra Pedregal, J., Lledós, A., Garribba, E. & Maréchal, J.-D. Prediction of the interaction of metallic moieties with proteins: an update for protein-ligand docking techniques. J. Comput. Chem. 39, 42–51 (2018).

    Article  CAS  PubMed  Google Scholar 

  116. 116.

    Karplus, M. Development of multiscale models for complex chemical systems: from H+H2 to biomolecules (Nobel lecture). Angew. Chemie Int. Edn 53, 9992–10005 (2014).

    Article  CAS  Google Scholar 

  117. 117.

    Levitt, M. Birth and future of multiscale modeling for macromolecular systems (Nobel lecture). Angew. Chemie Int. Edn 53, 10006–10018 (2014).

    Article  CAS  Google Scholar 

  118. 118.

    Warshel, A. Multiscale modeling of biological functions: From enzymes to molecular machines (Nobel lecture). Angew. Chemie Int. Edn 53, 10020–10031 (2014).

    Article  CAS  Google Scholar 

  119. 119.

    Jorgensen, W. L. Foundations of biomolecular modeling. Cell 155, 1199–1202 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    McGeagh, J. D., Ranaghan, K. E. & Mulholland, A. J. Protein dynamics and enzyme catalysis: insights from simulations. Biochim. Biophys. Acta Proteins Proteom. 1814, 1077–1092 (2011).

    Article  CAS  Google Scholar 

  121. 121.

    Lonsdale, R., Rouse, S. L., Sansom, M. S. P. & Mulholland, A. J. A multiscale approach to modelling drug metabolism by membrane-bound cytochrome P450 enzymes. PLOS Comput. Biol. 10, e1003714 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Cournia, Z., Allen, B. & Sherman, W. Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J. Chem. Inf. Model. 57, 2911–2937 (2017).

    Article  CAS  PubMed  Google Scholar 

  123. 123.

    Amaral, M. et al. Protein conformational flexibility modulates kinetics and thermodynamics of drug binding. Nat. Commun. 8, 2276 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Buch, I., Giorgino, T. & De Fabritiis, G. Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc. Natl Acad. Sci. USA 108, 10184–10189 (2011).

    Article  PubMed  Google Scholar 

  125. 125.

    Shan, Y. et al. How does a drug molecule find its target binding site? J. Am. Chem. Soc. 133, 9181–9183 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Gaspari, R. et al. Kinetic and structural insights into the mechanism of binding of sulfonamides to human carbonic anhydrase by computational and experimental studies. J. Med. Chem. 59, 4245–4256 (2016).

    Article  CAS  PubMed  Google Scholar 

  127. 127.

    Taylor, P. W., King, R. W. & Burgen, A. S. V. Kinetics of complex formation between human carbonic anhydrases and aromatic sulfonamides. Biochemistry 9, 2638–2645 (1970).

    Article  CAS  PubMed  Google Scholar 

  128. 128.

    Gao, J., Qiao, S. & Whitesides, G. M. Increasing binding constants of ligands to carbonic anhydrase by using ‘greasy tails’. J. Med. Chem. 38, 2292–2301 (1995).

    Article  CAS  PubMed  Google Scholar 

  129. 129.

    Mecinovic, J. et al. Fluoroalkyl and alkyl chains have similar hydrophobicities in binding to the ‘hydrophobic wall’ of carbonic anhydrase. J. Am. Chem. Soc. 133, 14017–14026 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Galindo-Murillo, R. et al. Intercalation processes of copper comlexes in DNA. Nucleic Acids Res. 43, 5364–5376 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. 131.

    Ma, Z. et al. An organometallic compound which exhibits a DNA topology-dependent one-stranded intercalation mode. Angew. Chemie Int. Edn 55, 7441–7444 (2016).

    Article  CAS  Google Scholar 

  132. 132.

    Meier-Menches, S. M., Gerner, C., Berger, W., Hartinger, C. G. & Keppler, B. K. Structure–activity relationships for ruthenium and osmium anticancer agents – towards clinical development. Chem. Soc. Rev. 47, 909–928 (2018).

    Article  CAS  PubMed  Google Scholar 

  133. 133.

    Parsonage, D. et al. X-ray structures of thioredoxin and thioredoxin reductase from Entamoeba histolytica and prevailing hypothesis of the mechanism of Auranofin action. J. Struct. Biol. 194, 180–190 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Palermo, G. et al. Fighting cancer with transition metal complexes: from naked DNA to protein and chromatin targeting strategies. ChemMedChem 11, 1199–1210 (2016).

    Article  CAS  PubMed  Google Scholar 

  135. 135.

    Weber, D. K. et al. Membrane insertion of a dinuclear polypyridylruthenium(II) complex revealed by solid-state NMR and molecular dynamics simulation: Implications for selective antibacterial activity. J. Am. Chem. Soc. 138, 15267–15277 (2016).

    Article  CAS  PubMed  Google Scholar 

  136. 136.

    Laio, A. & Parrinello, M. Escaping free-energy minima. Proc. Natl Acad. Sci. USA 99, 12562–12566 (2002).

    Article  CAS  PubMed  Google Scholar 

  137. 137.

    Ensing, B., De Vivo, M., Liu, Z., Moore, P. & Klein, M. L. Metadynamics as a tool for exploring free energy landscapes of chemical reactions. Acc. Chem. Res. 39, 73–81 (2006).

    Article  CAS  PubMed  Google Scholar 

  138. 138.

    Jorgensen, W. L. & Thomas, L. L. Perspective on free-energy perturbation calculations for chemical equilibria. J. Chem. Theory Comput. 4, 869–876 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. 139.

    Durrant, J. D. & McCammon, J. A. Molecular dynamics simulations and drug discovery. BMC Biol. 9, 71 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. 140.

    Zhang, B., D’Erasmo, M. P., Murelli, R. P. & Gallicchio, E. Free energy-based virtual screening and optimization of RNase H inhibitors of HIV-1 reverse transcriptase. ACS Omega 1, 435–447 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. 141.

    Adhireksan, Z. et al. Ligand substitutions between ruthenium–cymene compounds can control protein versus DNA targeting and anticancer activity. Nat. Commun. 5, 3462 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. 142.

    Czapla-Masztafiak, J. et al. Direct determination of metal complexes’ interaction with DNA by atomic telemetry and multiscale molecular dynamics. J. Phys. Chem. Lett. 8, 805–811 (2017).

    Article  CAS  PubMed  Google Scholar 

  143. 143.

    Gkionis, K, Mutter, S. T. & Platts, J.a. QM/MM description of platinum–DNA interactions: comparison of binding and DNA distortion of five drugs. RSC Adv. 3, 4066–4073 (2013).

    Article  CAS  Google Scholar 

  144. 144.

    Calandrini, V. et al. Structural biology of cisplatin complexes with cellular targets: the adduct with human copper chaperone Atox1 in aqueous solution. Chem. A Eur. J. 20, 11719–11725 (2014).

    Article  CAS  Google Scholar 

  145. 145.

    Calandrini, V., Rossetti, G., Arnesano, F., Natile, G. & Carloni, P. Computational metallomics of the anticancer drug cisplatin. J. Inorg. Biochem. 153, 231–238 (2015).

    Article  CAS  PubMed  Google Scholar 

  146. 146.

    Spinello, A. & Magistrato, A. An omics perspective to the molecular mechanisms of anticancer metallo-drugs in the computational microscope era. Expert Opin. Drug Discov. 8, 813–825 (2017).

    Google Scholar 

  147. 147.

    Lisa, M. et al. A general reaction mechanism for carbapenem hydrolysis by mononuclear and binuclear metallo-β-lactamases. Nat. Commun. 8, 538 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. 148.

    Dal Peraro, M., Ruggerone, P., Raugei, S., Gervasio, F. L. & Carloni, P. Investigating biological systems using first principles Car–Parrinello molecular dynamics simulations. Curr. Opin. Struct. Biol. 17, 149–156 (2007).

    Article  CAS  PubMed  Google Scholar 

  149. 149.

    Dal Peraro, M., Vila, A. J., Carloni, P. & Klein, M. L. Role of zinc content on the catalytic efficiency of B1 metallo β-lactamases. J. Am. Chem. Soc. 129, 2808–2816 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Dal Peraro, M., Llarrull, L. I., Rothlisberger, U., Vila, A. J. & Carloni, P. Water-assisted reaction mechanism of monozinc β-lactamases. J. Am. Chem. Soc. 126, 12661–12668 (2004).

    Article  CAS  PubMed  Google Scholar 

  151. 151.

    Brem, J. et al. Structural basis of metallo-β-lactamase, serine-β-lactamase and penicillin-binding protein inhibition by cyclic boronates. Nat. Commun. 7, 12406 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. 152.

    Palermo, G., Stenta, M., Cavalli, A., Dal Peraro, M. & De Vivo, M. Molecular simulations highlight the role of metals in catalysis and inhibition of type II topoisomerase. J. Chem. Theory Comput. 9, 857–862 (2013).

    Article  CAS  PubMed  Google Scholar 

  153. 153.

    Genna, V., Vidossich, P., Ippoliti, E., Carloni, P. & De Vivo, M. A self-activated mechanism for nucleic acid polymerization catalyzed by DNA/RNA polymerases. J. Am. Chem. Soc. 138, 14592–14598 (2016).

    Article  CAS  PubMed  Google Scholar 

  154. 154.

    Genna, V., Carloni, P. & De Vivo, M. A strategically located Arg/Lys residue promotes correct base paring during nucleic acid biosynthesis in polymerases. J. Am. Chem. Soc. 140, 3312–3321 (2018).

    Article  CAS  PubMed  Google Scholar 

  155. 155.

    Pavlin, M., Rossetti, G., De Vivo, M. & Carloni, P. Carnosine and homocarnosine degradation mechanisms by the human carnosinase enzyme CN1: insights from multiscale simulations. Biochemistry 55, 2772–2784 (2016).

    Article  CAS  PubMed  Google Scholar 

  156. 156.

    Vidossich, P. & Magistrato, A. QM/MM molecular dynamics studies of metal binding proteins. Biomolecules 4, 616–645 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. 157.

    Ho, M.-H., De Vivo, M., Dal Peraro, M. & Klein, M. L. Understanding the effect of magnesium ion concentration on the catalytic activity of ribonuclease H through computation: does a third metal binding site modulate endonuclease catalysis? J. Am. Chem. Soc. 132, 13702–13712 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. 158.

    Ho, M.-H., De Vivo, M., Dal Peraro, M. & Klein, M. L. Unraveling the catalytic pathway of metalloenzyme farnesyltransferase through QM/MM computation. J. Chem. Theory Comput. 5, 1657–1666 (2009).

    Article  CAS  PubMed  Google Scholar 

  159. 159.

    Rojas-Cervellera, V., Raich, L., Akola, J. & Rovira, C. The molecular mechanism of the ligand exchange reaction of an antibody against a glutathione-coated gold cluster. Nanoscale 9, 3121–3127 (2017).

    Article  CAS  PubMed  Google Scholar 

  160. 160.

    Ryde, U. & Söderhjelm, P. Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev. 116, 5520–5566 (2016).

    Article  CAS  PubMed  Google Scholar 

  161. 161.

    Ciancetta, A., Genheden, S. & Ryde, U. A. QM/MM study of the binding of RAPTA ligands to cathepsin B. J. Comput. Aided Mol. Des. 25, 729–742 (2011).

    Article  CAS  PubMed  Google Scholar 

  162. 162.

    Waldron, K. J., Rutherford, J. C., Ford, D. & Robinson, N. J. Metalloproteins and metal sensing. Nature 460, 823–830 (2009).

    Article  CAS  PubMed  Google Scholar 

  163. 163.

    Sinharay, S. & Pagel, M. D. Advances in magnetic resonance imaging contrast agents for biomarker detection. Annu. Rev. Anal. Chem. 9, 95–115 (2016).

    Article  Google Scholar 

  164. 164.

    Morrow, J. R. & Tóth, É. Next-generation magnetic resonance imaging contrast agents. Inorg. Chem. 56, 6029–6034 (2017).

    Article  CAS  PubMed  Google Scholar 

  165. 165.

    Caravan, P., Ellison, J. J., McMurry, T. J. & Lauffer, R. B. Gadolinium(III) chelates as MRI contrast agents: structure, dynamics, and applications. Chem. Rev. 99, 2293–2352 (1999).

    Article  CAS  PubMed  Google Scholar 

  166. 166.

    Pollet, R. & Marx, D. Ab initio simulation of a gadolinium-based magnetic resonance imaging contrast agent in aqueous solution. J. Chem. Phys. 126, 181102 (2007).

    Article  CAS  PubMed  Google Scholar 

  167. 167.

    Jeanvoine, Y., Miró, P., Martelli, F., Cramer, C. J. & Spezia, R. Electronic structure and bonding of lanthanoid(III) carbonates. Phys. Chem. Chem. Phys. 14, 14822–14831 (2012).

    Article  CAS  PubMed  Google Scholar 

  168. 168.

    Kotov, N. A. Inorganic nanoparticles as protein mimics. Science 330, 188–189 (2010).

    Article  CAS  PubMed  Google Scholar 

  169. 169.

    Su, S. et al. Design and applications of gold nanoparticle conjugates by exploiting biomolecule–gold nanoparticle interactions. Nanoscale 5, 2589–2599 (2013).

    Article  CAS  PubMed  Google Scholar 

  170. 170.

    Giljohann, D. A. et al. Gold nanoparticles for biology and medicine. Angew. Chemie Int. Edn 49, 3280–3294 (2010).

    Article  CAS  Google Scholar 

  171. 171.

    Mancin, F., Scrimin, P. & Tecilla, P. Progress in artificial metallonucleases. Chem. Commun. 48, 5545–5559 (2012).

    Article  CAS  Google Scholar 

  172. 172.

    Riccardi, L. et al. Nanoparticle-based receptors mimic protein-ligand recognition. Chem 3, 92–109 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. 173.

    Lu, Y., Berry, S. M. & Pfister, T. D. Engineering novel metalloproteins: design of metal-binding sites into native protein scaffolds. Chem. Rev. 101, 3047–3080 (2001).

    Article  CAS  PubMed  Google Scholar 

  174. 174.

    Arnold, F. H. Directed evolution: bringing new chemistry to life. Angew. Chemie Int. Edn 56, 2–8 (2017).

    Article  CAS  Google Scholar 

  175. 175.

    Yu, F. et al. Protein design: toward functional metalloenzymes. Chem. Rev. 114, 3495–3578 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. 176.

    Muñoz Robles, V. et al. Toward the computational design of artificial metalloenzymes: from protein-ligand docking to multiscale approaches. ACS Catal. 5, 2469–2480 (2015).

    Article  CAS  Google Scholar 

  177. 177.

    Huang, P.-S., Boyken, S. E. & Baker, D. The coming of age of de novo protein design. Nature 537, 320–327 (2016).

    Article  CAS  PubMed  Google Scholar 

  178. 178.

    Tobin, P., Richards, D., Callender, R. & Wilson, C. Protein engineering: a new frontier for biological therapeutics. Curr. Drug Metab. 15, 743–756 (2015).

    Article  CAS  Google Scholar 

  179. 179.

    Drienovská, I. et al. Design of an enantioselective artificial metallo-hydratase enzyme containing an unnatural metal-binding amino acid. Chem. Sci. 8, 7228–7235 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  180. 180.

    Bozkurt, E., Perez, M. A. S., Hovius, R., Browning, N. J. & Rothlisberger, U. Genetic algorithm based design and experimental characterization of a highly thermostable metalloprotein. J. Am. Chem. Soc. 140, 4517–4521 (2018).

    Article  CAS  PubMed  Google Scholar 

  181. 181.

    Amaro, R. E. & Mulholland, A. J. Multiscale methods in drug design bridge chemical and biological complexity in the search for cures. Nat. Rev. Chem. 2, 148 (2018).

    Article  CAS  Google Scholar 

  182. 182.

    Liu, J. & Wang, R. Classification of current scoring functions. J. Chem. Inf. Model. 55, 475–482 (2015).

    Article  CAS  PubMed  Google Scholar 

  183. 183.

    Nichols, S. E., Baron, R. & McCammon, J. A. in Computational Drug Discovery and Design (ed. Baron, R.) 93–103 (Springer, New York, NY, 2012).

  184. 184.

    Grübmuller, H., Heymann, B. & Tavan, P. Ligand binding: molecular mechanics calculation of the streptavidin biotin rupture force. Science 271, 997–999 (1996).

    Article  PubMed  Google Scholar 

  185. 185.

    Colizzi, F., Perozzo, R., Scapozza, L., Recanatini, M. & Cavalli, A. Single-molecule pulling simulations can discern ctive from inactive enzyme inhibitors. J. Am. Chem. Soc. 132, 7361–7371 (2010).

    Article  CAS  PubMed  Google Scholar 

  186. 186.

    Gervasio, F. L., Laio, A. & Parrinello, M. Flexible docking in solution using metadynamics. J. Am. Chem. Soc. 127, 2600–2607 (2005).

    Article  CAS  PubMed  Google Scholar 

  187. 187.

    Nechay, M. R., Valdez, C. E. & Alexandrova, A. N. Computational treatment of metalloproteins. J. Phys. Chem. B 119, 5945–5956 (2015).

    Article  CAS  PubMed  Google Scholar 

  188. 188.

    Dal Peraro, M. et al. Modeling the charge distribution at metal sites in proteins for molecular dynamics simulations. J. Struct. Biol. 157, 444–453 (2007).

    Article  CAS  PubMed  Google Scholar 

  189. 189.

    Li, P. & Merz, K. M. Metal ion modeling using classical mechanics. Chem. Rev. 117, 1564–1686 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. 190.

    Rasmussen, H. S. & McCann, P. P. Matrix metalloproteinase inhibition as a novel anticancer strategy: a review with special focus on batimastat and marimastat. Pharmacol. Ther. 75, 69–75 (1997).

    Article  CAS  PubMed  Google Scholar 

  191. 191.

    Rademaker-Lakhai, J. M. A phase I and pharmacological study with imidazolium-trans-DMSO-imidazole-tetrachlororuthenate, a novel ruthenium anticancer agent. Clin. Cancer Res. 10, 3717–3727 (2004).

    Article  CAS  PubMed  Google Scholar 

  192. 192.

    Hartinger, C. G. et al. KP1019, a new redox-active anticancer agent - preclinical development and results of a clinical phase I study in tumor patients. Chem. Biodivers. 5, 2140–2155 (2008).

    Article  CAS  PubMed  Google Scholar 

  193. 193.

    Antonarakis, E. S. & Emadi, A. Ruthenium-based chemotherapeutics: are they ready for prime time? Cancer Chemother. Pharmacol. 66, 1–9 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. 194.

    Hare, S. et al. Molecular mechanisms of retroviral integrase inhibition and the evolution of viral resistance. Proc. Natl Acad. Sci. USA 107, 20057–20062 (2010).

    Article  PubMed  Google Scholar 

  195. 195.

    Pochetti, G. et al. Structural insight into the stereoselective inhibition of MMP-8 by enantiomeric sulfonamide phosphonates. J. Med. Chem. 49, 923–931 (2006).

    Article  CAS  PubMed  Google Scholar 

  196. 196.

    Lauffer, B. E. L. et al. Histone deacetylase (HDAC) inhibitor kinetic rate constants correlate with cellular histone acetylation but not transcription and cell viability. J. Biol. Chem. 288, 26926–26943 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. 197.

    Natesh, R., Schwager, S. L. U., Sturrock, E. D. & Acharya, K. R. Crystal structure of the human angiotensin-converting enzyme–lisinopril complex. Nature 421, 551–554 (2003).

    Article  CAS  PubMed  Google Scholar 

  198. 198.

    Natesh, R., Schwager, S. L. U., Evans, H. R., Sturrock, E. D. & Acharya, K. R. Structural details on the binding of antihypertensive drugs captopril and enalaprilat to human testicular angiotensin I-converting enzyme. Biochemistry 43, 8718–8724 (2004).

    Article  CAS  PubMed  Google Scholar 

  199. 199.

    Fisher, S. Z., Aggarwal, M., Kovalevsky, A. Y., Silverman, D. N. & McKenna, R. Neutron diffraction of acetazolamide-bound human carbonic anhydrase II reveals atomic details of drug binding. J. Am. Chem. Soc. 134, 14726–14729 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  200. 200.

    Casini, A., Temperini, C., Gabbiani, C., Supuran, C. T. & Messori, L. The X-ray structure of the adduct between NAMI-A and carbonic anhydrase provides insights into the reactivity of this metallodrug with proteins. ChemMedChem 5, 1989–1994 (2010).

    Article  CAS  PubMed  Google Scholar 

  201. 201.

    Mast, N. et al. In silico and intuitive predictions of CYP46A1 inhibition by marketed drugs with subsequent enzyme crystallization in complex with fluvoxamine. Mol. Pharmacol. 82, 824–834 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. 202.

    Ummat, A. et al. Structural basis for cisplatin DNA damage tolerance by human polymerase η during cancer chemotherapy. Nat. Struct. Mol. Biol. 19, 628–632 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. 203.

    Streib, M. et al. An organometallic inhibitor for the human repair enzyme 7,8-dihydro-8-oxoguanosine triphosphatase. Angew. Chemie Int. Edn 53, 305–309 (2014).

    Article  CAS  Google Scholar 

  204. 204.

    Ang, W. H. et al. Rational design of an organometallic glutathione transferase inhibitor. Angew. Chemie Int. Edn 48, 3854–3857 (2009).

    Article  CAS  Google Scholar 

  205. 205.

    Towbin, H. et al. Proteomics-based target identification. J. Biol. Chem. 278, 52964–52971 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. 206.

    Ha, N.-C. et al. Supramolecular assembly and acid resistance of Helicobacter pylori urease. Nat. Struct. Biol. 8, 505–509 (2001).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

M.D.V. thanks the Italian Association for Cancer Research (AIRC) for financial support (IG 18883).

Author information

Affiliations

Authors

Contributions

All authors researched data and contributed equally to the discussion of the content of the article. L.R. and M.D.V. wrote the article.

Corresponding author

Correspondence to Marco De Vivo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Riccardi, L., Genna, V. & De Vivo, M. Metal–ligand interactions in drug design. Nat Rev Chem 2, 100–112 (2018). https://doi.org/10.1038/s41570-018-0018-6

Download citation

Further reading