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Overcoming universal restrictions on metal selectivity by protein design

Nature volume 603, pages 522–527 (2022)Cite this article

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Abstract

Selective metal coordination is central to the functions of metalloproteins:1,2 each metalloprotein must pair with its cognate metallocofactor to fulfil its biological role3. However, achieving metal selectivity solely through a three-dimensional protein structure is a great challenge, because there is a limited set of metal-coordinating amino acid functionalities and proteins are inherently flexible, which impedes steric selection of metals3,4. Metal-binding affinities of natural proteins are primarily dictated by the electronic properties of metal ions and follow the Irving–Williams series5 (Mn2+ < Fe2+ < Co2+ < Ni2+ < Cu2+ > Zn2+) with few exceptions6,7. Accordingly, metalloproteins overwhelmingly bind Cu2+ and Zn2+ in isolation, regardless of the nature of their active sites and their cognate metal ions1,3,8. This led organisms to evolve complex homeostatic machinery and non-equilibrium strategies to achieve correct metal speciation1,3,8,9,10. Here we report an artificial dimeric protein, (AB)2, that thermodynamically overcomes the Irving–Williams restrictions in vitro and in cells, favouring the binding of lower-Irving–Williams transition metals over Cu2+, the most dominant ion in the Irving–Williams series. Counter to the convention in molecular design of achieving specificity through structural preorganization, (AB)2 was deliberately designed to be flexible. This flexibility enabled (AB)2 to adopt mutually exclusive, metal-dependent conformational states, which led to the discovery of structurally coupled coordination sites that disfavour Cu2+ ions by enforcing an unfavourable coordination geometry. Aside from highlighting flexibility as a valuable element in protein design, our results illustrate design principles for constructing selective metal sequestration agents.

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Fig. 1: Design and characterization of the (AB)2 scaffold.
Fig. 2: Co2+ and Ni2+ selectivity of (AB)2.
Fig. 3: Primary metal coordination spheres of central and peripheral sites in (AB)2.
Fig. 4: Selective Co2+ and Ni2+ binding of (AB)2 in the E. coli periplasmic space.

Data availability

The principal data supporting the findings of this work are available within the figures and the Supplementary Information. Additional data that support the findings of this study are available from the corresponding author on request. Crystallographic data for protein structures (coordinates and structure factors) have been deposited into the RCSB PDB under the following accession codes: 7MK4, 7LRV, 7LV1, 7N4G, 7N4F, 7LR5, 7LRA, 7LRB and 7LRR. The model structure used for molecular replacement is available in the PDB (2BC5).

References

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

    Article  CAS  PubMed  ADS  Google Scholar 

  2. Gray, H. B., Stiefel, E. I., Valentine, J. S. & Bertini, I. Biological Inorganic Chemistry: Structure and Reactivity (University Science Books, 2007).

  3. Waldron, K. J. & Robinson, N. J. How do bacterial cells ensure that metalloproteins get the correct metal? Nat. Rev. Microbiol. 7, 25–35 (2009).

    Article  CAS  PubMed  Google Scholar 

  4. Dudev, T. & Lim, C. Competition among metal ions for protein binding sites: determinants of metal ion selectivity in proteins. Chem. Rev. 114, 538–556 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Frausto da Silva, J. J. R. & Williams, R. J. P. The Biological Chemistry of the Elements (Oxford University Press, 2001).

  6. Kisgeropoulos, E. C. et al. Key structural motifs balance metal binding and oxidative reactivity in a heterobimetallic Mn/Fe protein. J. Am. Chem. Soc. 142, 5338–5354 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Grāve, K., Griese, J. J., Berggren, G., Bennett, M. D. & Högbom, M. The Bacillus anthracis class Ib ribonucleotide reductase subunit NrdF intrinsically selects manganese over iron. J. Biol. Inorg. Chem. 25, 571–582 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Reyes-Caballero, H., Campanello, G. C. & Giedroc, D. P. Metalloregulatory proteins: metal selectivity and allosteric switching. Biophys. Chem. 156, 103–114 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. O’Halloran, T. V. & Culotta, V. C. Metallochaperones, an intracellular shuttle service for metal ions. J. Biol. Chem. 275, 25057–25060 (2000).

    Article  PubMed  Google Scholar 

  10. Tottey, S. et al. Protein-folding location can regulate manganese-binding versus copper- or zinc-binding. Nature 455, 1138–1142 (2008).

    Article  CAS  PubMed  ADS  Google Scholar 

  11. Lombardi, A., Pirro, F., Maglio, O., Chino, M. & DeGrado, W. F. De novo design of four-helix bundle metalloproteins: one scaffold, diverse reactivities. Acc. Chem. Res. 52, 1148–1159 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lu, Y., Yeung, N., Sieracki, N. & Marshall, N. M. Design of functional metalloproteins. Nature 460, 855–862 (2009).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Schwizer, F. et al. Artificial metalloenzymes: reaction scope and optimization strategies. Chem. Rev. 118, 142–231 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. Churchfield, L. A. & Tezcan, F. A. Design and construction of functional supramolecular metalloprotein assemblies. Acc. Chem. Res. 52, 345–355 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Faiella, M. et al. An artificial di-iron oxo-protein with phenol oxidase activity. Nat. Chem. Biol. 5, 882–884 (2009).

    Article  CAS  PubMed  Google Scholar 

  17. Zastrow, M. L., Peacock, F. A., Stuckey, J. A. & Pecoraro, V. L. Hydrolytic catalysis and structural stabilization in a designed metalloprotein. Nat. Chem. 4, 118–123 (2012).

    Article  CAS  Google Scholar 

  18. Studer, S. et al. Evolution of a highly active and enantiospecific metalloenzyme from short peptides. Science 362, 1285–1288 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  19. Khare, S. D. et al. Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis. Nat. Chem. Biol. 8, 294–300 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Yeung, N. et al. Rational design of a structural and functional nitric oxide reductase. Nature 462, 1079–1082 (2009).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  21. Song, W. J. & Tezcan, F. A. A designed supramolecular protein assembly with in vivo enzymatic activity. Science 346, 1525–1528 (2014).

    Article  CAS  PubMed  ADS  Google Scholar 

  22. Churchfield, L. A., Medina-Morales, A., Brodin, J. D., Perez, A. & Tezcan, F. A. De novo design of an allosteric metalloprotein assembly with strained disulfide bonds. J. Am. Chem. Soc. 138, 13163–13166 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zhou, L. et al. A protein engineered to bind uranyl selectively and with femtomolar affinity. Nat. Chem. 6, 236–241 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Wegner, S. V., Boyaci, H., Chen, H., Jensen, M. P. & He, C. Engineering a uranyl-specific binding protein from NikR. Angew. Chem. Int. Ed. Engl. 48, 2339–2341 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Brodin, J. D. et al. Evolution of metal selectivity in templated protein interfaces. J. Am. Chem. Soc. 132, 8610–8617 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Guffy, S. L., Der, B. S. & Kuhlman, B. Probing the minimal determinants of zinc binding with computational protein design. Protein Eng. Des. Sel. 29, 327–338 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Akcapinar, G. B. & Sezerman, O. U. Computational approaches for de novo design and redesign of metal-binding sites on proteins. Biosci. Rep. 37, BSR20160179 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Byrd, J. & Winge, D. R. Cooperative cluster formation in metallothionein. Arch. Biochem. Biophys. 250, 233–237 (1986).

    Article  CAS  PubMed  Google Scholar 

  29. Halling, D. B., Liebeskind, B. J., Hall, A. W. & Aldrich, R. W. Conserved properties of individual Ca2+-binding sites in calmodulin. Proc. Natl Acad. Sci. USA 113, E1216–E1225 (2016).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  30. Zygiel, E. M. & Nolan, E. M. Transition metal sequestration by the host-defense protein calprotectin. Annu. Rev. Biochem. 87, 621–643 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rittle, J., Field, M. J., Green, M. T. & Tezcan, F. A. An efficient, step-economical strategy for the design of functional metalloproteins. Nat. Chem. 11, 434–441 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Faraone-Mennella, J., Tezcan, F. A., Gray, H. B. & Winkler, J. R. Stability and folding kinetics of structurally characterized cytochrome c-b562. Biochemistry 45, 10504–10511 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Choi, T. S., Lee, H. J., Han, J. Y., Lim, M. H. & Kim, H. I. Molecular insights into human serum albumin as a receptor of amyloid-β in the extracellular region. J. Am. Chem. Soc. 139, 15437–15445 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Burgot, J.-L. Ionic Equilibria in Analytical Chemistry (Springer, 2012).

  35. Osman, D. et al. Bacterial sensors define intracellular free energies for correct enzyme metalation. Nat. Chem. Biol. 15, 241–249 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Young, T. R. et al. Calculating metalation in cells reveals CobW acquires CoII for vitamin B12 biosynthesis while related proteins prefer ZnII. Nat. Commun. 12, 1195 (2021).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  37. Jeschek, M. et al. Directed evolution of artificial metalloenzymes for in vivo metathesis. Nature 537, 661–665 (2016).

    Article  CAS  PubMed  ADS  Google Scholar 

  38. Thompson, A. N. et al. Mechanism of potassium-channel selectivity revealed by Na+ and Li+ binding sites within the KcsA pore. Nat. Struct. Mol. Biol. 16, 1317–1324 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Capdevila, D. A., Braymer, J. J., Edmonds, K. A., Wu, H. & Giedroc, D. P. Entropy redistribution controls allostery in a metalloregulatory protein. Proc. Natl Acad. Sci. USA 114, 4424–4429 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tokuriki, N. & Tawfik, D. S. Protein dynamism and evolvability. Science 324, 203–207 (2009).

    Article  CAS  PubMed  ADS  Google Scholar 

  41. Motlagh, H. N., Wrabl, J. O., Li, J. & Hilser, V. J. The ensemble nature of allostery. Nature 508, 331–339 (2014).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  42. Papaleo, E. et al. The role of protein loops and linkers in conformational dynamics and allostery. Chem. Rev. 116, 6391–6423 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Arslan, E., Schulz, H., Zufferey, R., Künzler, P. & Thöny-Meyer, L. Overproduction of the Bradyrhizobium japonicum c-type cytochrome subunits of the cbb3 oxidase in Escherichia coli. Biochem. Biophys. Res. Commun. 251, 744–747 (1998).

    Article  CAS  PubMed  Google Scholar 

  44. Bailey, J. B., Subramanian, R. H., Churchfield, L. A. & Tezcan, F. A. in Peptide, Protein and Enzyme Design: Methods in Enzymology Vol. 580 (ed. Pecoraro, V. L.) 223–250 (Academic Press, 2016).

  45. Martel, A., Liu, P., Weiss, T. M., Niebuhr, M. & Tsuruta, H. An integrated high-throughput data acquisition system for biological solution X-ray scattering studies. J. Synchrotron Radiat. 19, 431–434 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Manalastas-Cantos, K. et al. ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis. J. Appl. Crystallogr. 54, 343–355 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Svergun, D., Barberato, C. & Koch, M. H. J. CRYSOL—a program to evaluate X-ray solution scattering of biological macromolecules from atomic coordinates. J. Appl. Crystallogr. 28, 768–773 (1995).

    Article  CAS  Google Scholar 

  48. Collaborative Computational Project. The CCP4 suite: programs for protein crystallography. Acta Crystallogr. D 50, 760–763 (1994).

    Article  Google Scholar 

  49. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. The PyMOL Molecular Graphics System v.1.8 (Schrödinger, 2015).

  52. Schuck, P. Size-distribution analysis of macromolecules by sedimentation velocity ultracentrifugation and Lamm equation modeling. Biophys. J. 78, 1606–1619 (2000).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  53. Manoil, C. & Beckwith, J. A genetic approach to analyzing membrane protein topology. Science 233, 1403–1408 (1986).

    Article  CAS  PubMed  ADS  Google Scholar 

  54. Dapprich, S., Komáromi, I., Byun, K. S., Morokuma, K. & Frisch, M. J. A new ONIOM implementation in Gaussian98. Part I. The calculation of energies, gradients, vibrational frequencies and electric field derivatives. J. Mol. Struct. THEOCHEM 461–462, 1–21 (1999).

    Article  Google Scholar 

  55. Vreven, T., Morokuma, K., Farkas, Ö., Schlegel, H. B. & Frisch, M. J. Geometry optimization with QM/MM, ONIOM, and other combined methods. I. Microiterations and constraints. J. Comput. Chem. 24, 760–769 (2003).

    Article  CAS  PubMed  Google Scholar 

  56. Tao, P. et al. Matrix metalloproteinase 2 inhibition: combined quantum mechanics and molecular mechanics studies of the inhibition mechanism of (4-phenoxyphenylsulfonyl)methylthiirane and its oxirane analogue. Biochemistry 48, 9839–9847 (2009).

    Article  CAS  PubMed  Google Scholar 

  57. Becke, A. D. Density‐functional thermochemistry. III. The role of exact exchange. J. Chem. Phys. 98, 5648–5652 (1993).

    Article  CAS  ADS  Google Scholar 

  58. Lee, C., Yang, W. & Parr, R. G. Development of the Colle–Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 37, 785–789 (1988).

    Article  CAS  ADS  Google Scholar 

  59. Hariharan, P. C. & Pople, J. A. The effect of d-functions on molecular orbital energies for hydrocarbons. Chem. Phys. Lett. 16, 217–219 (1972).

    Article  CAS  ADS  Google Scholar 

  60. Rassolov, V. A., Pople, J. A., Ratner, M. A. & Windus, T. L. 6-31G* basis set for atoms K through Zn. J. Chem. Phys. 109, 1223–1229 (1998).

    Article  CAS  ADS  Google Scholar 

  61. Rassolov, V. A., Ratner, M. A., Pople, J. A., Redfern, P. C. & Curtiss, L. A. 6-31G* basis set for third-row atoms. J. Comput. Chem. 22, 976–984 (2001).

    Article  CAS  Google Scholar 

  62. Freindorf, M., Shao, Y., Furlani, T. R. & Kong, J. Lennard–Jones parameters for the combined QM/MM method using the B3LYP/6-31G*/AMBER potential. J. Comput. Chem. 26, 1270–1278 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Case, D. A. et al. The Amber biomolecular simulation programs. J. Comput. Chem. 26, 1668–1688 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bakowies, D. & Thiel, W. Hybrid models for combined quantum mechanical and molecular mechanical approaches. J. Phys. Chem. 100, 10580–10594 (1996).

    Article  CAS  Google Scholar 

  65. Weiner, S. J., Singh, U. C. & Kollman, P. A. Simulation of formamide hydrolysis by hydroxide ion in the gas phase and in aqueous solution. J. Am. Chem. Soc. 107, 2219–2229 (1985).

    Article  CAS  Google Scholar 

  66. Kakkis, A., Gagnon, D., Esselborn, J., Britt, R. D. & Tezcan, F. A. Metal-templated design of chemically switchable protein assemblies with high-affinity coordination sites. Angew. Chem. Int. Ed. Engl. 59, 21940–21944 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kocyła, A., Pomorski, A. & Krężel, A. Molar absorption coefficients and stability constants of metal complexes of 4-(2-pyridylazo)resorcinol (PAR): revisiting common chelating probe for the study of metalloproteins. J. Inorg. Biochem. 152, 82–92 (2015).

    Article  PubMed  Google Scholar 

  68. Kuzmič, P. Program DYNAFIT for the analysis of enzyme kinetic data: application to HIV proteinase. Anal. Biochem. 237, 260–273 (1996).

    Article  PubMed  Google Scholar 

  69. Stoll, S. & Schweiger, A. EasySpin, a comprehensive software package for spectral simulation and analysis in EPR. J. Magn. Reson. 178, 42–55 (2006).

    Article  CAS  PubMed  ADS  Google Scholar 

  70. Smilgies, D.-M. & Folta-Stogniew, E. Molecular weight-gyration radius relation of globular proteins: a comparison of light scattering, small-angle X-ray scattering and structure-based data. J. Appl. Crystallogr. 48, 1604–1606 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the Tezcan group, J. Rittle, H. Gray, S. Cohen and M. Green for discussions. We also thank A. Kakkis and C.-J. Yu for AUC and EPR measurements, respectively. This work was funded primarily by the National Institutes of Health (NIH; R01-GM138884) and by NASA (80NSSC18M0093; ENIGMA: Evolution of Nanomachines in Geospheres and Microbial Ancestors (NASA Astrobiology Institute Cycle 8)). Parts of this research were carried out at the Stanford Synchrotron Radiation Lightsource (supported by the DOE, Office of Basic Energy Sciences contract DE-AC02-76SF00515 and NIH P30-GM133894) and the Advanced Light Source (supported by the DOE, Office of Basic Energy Sciences contract DE-AC02-05CH11231and NIH P30-GM124169-01). EPR data was acquired on an instrument obtained through a Major Research Instrumentation fund supported by the National Science Foundation (NSF; CHE-2019066).

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  1. Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA

    Tae Su Choi & F. Akif Tezcan

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  1. Tae Su Choi
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T.S.C. conceived the project, designed and performed all experiments, calculations and analyses and co-wrote the paper. F.A.T. conceived and directed the project and co-wrote the manuscript.

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Correspondence to F. Akif Tezcan.

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Extended data figures and tables

Extended Data Fig. 1 Analysis of the oligomerization states of (AB)2, (AC)2 and (BC)2 variants.

a, Sedimentation velocity/AUC analysis of the oligomerization state of (AB)2 (black), (AC)2 (red), and (BC)2 (blue) at 25 μM dimer concentration. The inset shows a higher-order oligomer population of (BC)2. b–d,Guinier analysis of (b) AB (black), (c) AC (red), and (d) BC (blue) constructs measured by solution small-angle X-ray scattering (SAXS) at 0.8 mM dimer concentration. Larger I(0) values in (AC)2 and (BC)2 indicate higher oligomeric states compared to (AB)2. Radius of gyration (Rg) value of (AB)2 corresponds to approximately 25 kDa in Mw–Rg relationship of globular proteins70, indicating a dimeric state for (AB)2.

Extended Data Fig. 2 Structural characterization and metal-binding analysis of MII-(AB)2 complexes.

a, X-band EPR spectrum of CuII-(AB)2 (orange) and simulated pattern (black line), along with the fit parameters consistent with a rhombic coordination geometry. Conditions: 2.5 mM CuII, 20 mM MOPS (pH 7.4) and 150 mM NaCl, 298 K. b, Crystal structure of 2CoII-(AB)2. CoII ions are represented as magenta spheres. c, Solution characterization of 2CoII-(AB)2 complex by competitive Fura-2 titration (left), SAXS (middle), and ESI–MS (right). Circles (magenta) in ESI–MS spectrum represent the number of CoII ions bound to (AB)2. Experimental data points and error bars in the Fura-2 titration are presented as mean and standard deviation of three independent measurements. d, Competitive metal-binding titrations (AB)2 for MnII (pink) and ZnII (grey) binding. Mag-Fura-2 and Fura-2 were used for MnII and ZnII titrations, respectively. e, Investigation of MII-(AB)2 complexation using ESI–MS: MnII (top), FeII (middle), and ZnII (bottom). MnII- and FeII-(AB)2 complexes were not observed owing to the low metal-binding affinities. f, ESI–MS spectra of (AB)2 collected under CuII//ZnII and ZnII//CuII competition conditions. CuII outcompetes ZnII or forms heterometallic complexes of (AB)2. g, Theoretical SAXS scattering profiles of MII-(AB)2 generated by CRYSOL. h, i, Experimental SAXS profiles of (h) 2CoII- and 2NiII-(AB)2 complexes compared with theoretical scattering profile of 1CuII-(AB)2 (cyan), and (i) 1CuII-(AB)2 complex compared with theoretical scattering profiles of 2CoII- (magenta) and 2NiII-(AB)2 (green). j, k, Log-scale plots of Fig. 1e–f to compare (j) experimental scattering profiles between 2CoII/2NiII-(AB)2 and 1CuII-(AB)2 and (k) experimental scattering profiles of 2CoII- (left), 2NiII- (middle), and 1CuII-(AB)2 (right) with theoretical scattering profiles. l, m, Expanded low q-ranges of the scattering plots in (l) (panel j) and (m) (k).

Extended Data Fig. 3 Structural characterization and metal-binding analysis of MII-(AB)2 complexes in competitive conditions with CuII.

a, Crystal structures (ribbon models) of (AB)2 formed under CoII//CuII (PDB:7LRB), CuII//CoII (PDB:7LRR), NiII//CuII (PDB:7LR5), and CuII//NiII (PDB:7LRA) conditions. Root mean square deviation (RMSD) values were determined in comparison with the crystal structures of 2CoII-(AB)2 and 2NiII-(AB)2 (grey cartoon models). b, Experimental SAXS profile of (AB)2 (0.8 mM) in CoII//CuII, CuII//CoII, NiII//CuII, and CuII//NiII conditions ([MII] = 1.6 mM) compared with the theoretical SAXS profile of 1CuII-(AB)2. c, ESI–MS spectrum of (AB)2 (5 μM) without metal ions. d, ESI–MS spectrum of (AB)2 (5 μM) with sub-stoichiometric amounts of CuII (5 μM) and CoII or NiII (5 μM). Circles in ESI–MS spectra represent the number of CoII (magenta), NiII (green), and CuII (cyan) ions bound to (AB)2. The number of circles indicates the equivalents of bound metal ions. e, Competitive metal-binding titration of (AB)2 with CoII (magenta), NiII (green), and CuII (cyan) in 20 mM NH4HCO3 (pH 7.8). Mag-Fura-2 (5 μM) was used for competitive CoII and NiII titration, and Newport green DCF (5 μM) was used for competitive CuII titration. Experimental data points and error bars are presented as mean and standard deviation of three independent measurements. f, g, Log-scale plots (left) of Fig. 2c to compare experimental scattering profiles of (f) CoII//CuII and CuII//CoII, and (g) NiII//CuII and CuII//NiII with theoretical scattering profiles. Right panels present expanded low q-ranges of the scattering plots (left).

Extended Data Fig. 4 Peripheral 5His coordination site of 2CoII-(AB)2 and rotamer analysis of the H100 side chain.

a, CoII-coordination in the dimer interface of (AB)2 with the 2mFo-DFc electron density map (grey mesh) contoured at 5.0σ (metal) and 1.5σ (ligand). Coordination distances and angles between CoII and ligands are shown on the right. b, Possible rotamer orientations of H100 residue in the crystal structure of 2NiII-(AB)2. Rotamers were generated with the combination of χ1 (torsion angle of Cα-Cβ = 60–300°, 60° interval) and χ2 (torsion angle of Cβ-Cγ = 0–330°, 30° interval). c, Averaged number of atomic contacts of H100 rotamers as a function of van der waals (VdW) overlap. Error bars reflect the standard deviations of the atomic contacts in rotamers with fixed χ1 and variable χ2. Compared to the original conformation of H100, all possible rotamers show significant clashes with proximal residues. d, Location of proximal residues interacting with H100 rotamers.

Extended Data Fig. 5 QM/MM calculations of models 1–3.

a–c, QM/MM-optimized structures of (a) Model 1: CuII in the peripheral site of (AB)2 (b) Model 2: CuII in the peripheral site of H100A(AB)2, and (c) Model 3: CuII in the central site of (AB)2. Conformations of the opposite peripheral binding site (red) from the modelled CuII coordination site are shown in the panel on the right in a and b. d, RMSDs of Cα positions between QM/MM-optimized models and the crystal structure (His: Cα’s of 5His or 4His residues in the opposite peripheral site, All: all Cα’s in (AB)2 or H100A(AB)2, and Interface: Cα’s of residues in the dimer interface.

Extended Data Fig. 6 Characterization of metal-bound H100A(AB)2 complexes.

a, ESI–MS spectra of H100A(AB)2 (5 μM) complexed with metal ions. Circles in ESI–MS spectra represent the number of CoII (magenta), NiII (green), and CuII (cyan) ions bound to H100A(AB)2. The number of circles indicates the equivalents of bound metal ions. Metal concentrations were 10 μM. b, Fura-2 competitive metal titration assay of H100A(AB)2 with CuII. Changes in Fura-2 absorbance at 335 nm (cyan) are plotted with theoretical metal-binding isotherms in the absence (grey) and the presence (black) of (AB)2. Experimental data points and error bars in the Fura-2 titration are presented as mean and standard deviation of three independent measurements. c, d, Crystal structure of (c) CoII-H100A(AB)2 (PDB:7N4G) and (d) NiII-H100A(AB)2 (PDB:7N4F). CoII and NiII ions are represented as magenta and green spheres. Atomic details of each metal coordination site are shown in the right panels, with the 2mFo-DFc electron density map (grey mesh) contoured at 5.0σ (metal) and 1.5σ (ligand).

Extended Data Fig. 7 Characterization of periplasmic extracts using SDS–PAGE and ESI–MS.

a, b, SDS–PAGE of periplasmic extracts and medium stained with (a) Coomassie blue for all proteinaceous contents and (b) o-dianisidine for haem-proteins. Regardless of supplemented metal ions, no significant amount of (AB)2 dimer and (AB) monomer was observed in the medium. Uncropped gel images are shown in Supplementary Fig. 2. Reproducibility of SDS–PAGE was tested with two independently extracted sample sets. c, ESI–MS spectra of (AB)2 extracted from cells grown without metal supplement (top) and with CoII (middle) or NiII (bottom) supplement (20 μM) in LB medium. d, e, ESI–MS spectra of (AB)2 extracted from cells grown with (d) 100 µM and (e) 5 µM metal ions in LB medium. Inlet spectra represent expanded m/z ranges for 2MII-(AB)2 complexes with magenta, green, and cyan lines corresponding to theoretical m/z values of 2CoII, 2NiII, and 2CuII-(AB)2 complexes. Supplemented metal ions are represented as magenta (CoII), green (NiII), and cyan (CuII) circles. The number of circles indicates the equivalents of metal ions bound to (AB)2. D and M in gel pictures refer to the protein dimer and monomer, respectively.

Extended Data Table 1 Equilibrium dissociation constants (Kd) of MII-(AB)2 complexes
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Extended Data Table 2 X-ray data collection and refinement statistics for the crystals obtained under non-competitive conditions
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Extended Data Table 3 X-ray data collection and refinement statistics for the crystals obtained under competitive conditions with CuII
Full size table

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This file contains the Supplementary Methods, Supplementary Figures 1–2 and Supplementary Tables 1–5.

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Choi, T.S., Tezcan, F.A. Overcoming universal restrictions on metal selectivity by protein design. Nature 603, 522–527 (2022). https://doi.org/10.1038/s41586-022-04469-8

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