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Transient water wires mediate selective proton transport in designed channel proteins

Nature Chemistry volume 15, pages 1012–1021 (2023)Cite this article

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Abstract

Selective proton transport through proteins is essential for forming and using proton gradients in cells. Protons are conducted along hydrogen-bonded ‘wires’ of water molecules and polar side chains, which, somewhat surprisingly, are often interrupted by dry apolar stretches in the conduction pathways, inferred from static protein structures. Here we hypothesize that protons are conducted through such dry spots by forming transient water wires, often highly correlated with the presence of the excess protons in the water wire. To test this hypothesis, we performed molecular dynamics simulations to design transmembrane channels with stable water pockets interspersed by apolar segments capable of forming flickering water wires. The minimalist designed channels conduct protons at rates similar to viral proton channels, and they are at least 106-fold more selective for H+ over Na+. These studies inform the mechanisms of biological proton conduction and the principles for engineering proton-conductive materials.

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Fig. 1: Hypothesis of proton-selective transport along transient water wires.
Fig. 2: De novo channels incorporate PLSs at key positions to modulate hydrophobic lengths.
Fig. 3: Permeation of water into the designed channels correlates with the position of the luminal Gln residues.
Fig. 4: Crystal structures of the designed channels demonstrate that the introduction of polar Gln residues mediates stable water pockets.
Fig. 5: MS-RMD predicts that introduction of the PLS enables the formation of proton-conductive transient water wires.
Fig. 6: Designed channels selectively move protons across the membrane.

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Data availability

Coordinates and data files have been deposited with the PDB with accession codes 7UDY (QLLL), 7UDZ (LQLL), 7UDV (LLQL), 7UDW (QQLL) and 7UDX (QLQL). Source data are provided with this paper. All other data and materials are available from the corresponding authors on request.

References

  1. Moriyama, Y. & Futai, M. H+-ATPase, a primary pump for accumulation of neurotransmitters, is a major constituent of brain synaptic vesicles. Biochem. Biophys. Res. Commun. 173, 443–448 (1990).

    Article  CAS  PubMed  Google Scholar 

  2. Nishi, T. & Forgac, M. The vacuolar (H+)-ATPases—nature’s most versatile proton pumps. Nat. Rev. Mol. Cell Biol. 3, 94–103 (2002).

    Article  CAS  PubMed  Google Scholar 

  3. Mitchell, P. Coupling of phosphorylation to electron and hydrogen transfer by a chemi-osmotic type of mechanism. Nature 191, 144–148 (1961).

    Article  CAS  PubMed  Google Scholar 

  4. Nicholls, D. G. Mitochondrial ion circuits. Essays Biochem. 47, 25–35 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. Diering, G. H. & Numata, M. Endosomal pH in neuronal signaling and synaptic transmission: role of Na+/H+ exchanger NHE5. Front. Physiol. 4, 412 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Agmon, N. The Grotthuss mechanism. Chem. Phys. Lett. 244, 456–462 (1995).

    Article  CAS  Google Scholar 

  7. Calio, P. B., Li, C. & Voth, G. A. Resolving the structural debate for the hydrated excess proton in water. J. Am. Chem. Soc. 143, 18672–18683 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. Li, C. & Voth, G. A. A quantitative paradigm for water-assisted proton transport through proteins and other confined spaces. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2113141118 (2021).

  9. Wraight, C. A. Chance and design—proton transfer in water, channels and bioenergetic proteins. Biochim. Biophys. Acta 1757, 886–912 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Decoursey, T. E. Voltage-gated proton channels and other proton transfer pathways. Physiol. Rev. 83, 475–579 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Peng, Y., Swanson, J. M., Kang, S. G., Zhou, R. & Voth, G. A. Hydrated excess protons can create their own water wires. J. Phys. Chem. B 119, 9212–9218 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Banh, R. et al. Hydrophobic gasket mutation produces gating pore currents in closed human voltage-gated proton channels. Proc. Natl Acad. Sci. USA 116, 18951–18961 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Garczarek, F. & Gerwert, K. Functional waters in intraprotein proton transfer monitored by FTIR difference spectroscopy. Nature 439, 109–112 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Kaur, D., Khaniya, U., Zhang, Y. & Gunner, M. R. Protein motifs for proton transfers that build the transmembrane proton gradient. Front. Chem. 9, 660954 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kalra, A., Garde, S. & Hummer, G. Osmotic water transport through carbon nanotube membranes. Proc. Natl Acad. Sci. USA 100, 10175–10180 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ben-Abu, Y., Zhou, Y., Zilberberg, N. & Yifrach, O. Inverse coupling in leak and voltage-activated K+ channel gates underlies distinct roles in electrical signaling. Nat. Struct. Mol. Biol. 16, 71–79 (2009).

    Article  CAS  PubMed  Google Scholar 

  17. Jensen, M. O. et al. Principles of conduction and hydrophobic gating in K+ channels. Proc. Natl Acad. Sci. USA 107, 5833–5838 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Aryal, P., Sansom, M. S. & Tucker, S. J. Hydrophobic gating in ion channels. J. Mol. Biol. 427, 121–130 (2015).

    Article  CAS  PubMed  Google Scholar 

  19. Zhu, F. & Hummer, G. Drying transition in the hydrophobic gate of the GLIC channel blocks ion conduction. Biophys. J. 103, 219–227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Rasaiah, J. C., Garde, S. & Hummer, G. Water in nonpolar confinement: from nanotubes to proteins and beyond. Annu. Rev. Phys. Chem. 59, 713–740 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, T. et al. Deprotonation of D96 in bacteriorhodopsin opens the proton uptake pathway. Structure 21, 290–297 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Weinert, T. et al. Proton uptake mechanism in bacteriorhodopsin captured by serial synchrotron crystallography. Science 365, 61–65 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Freier, E., Wolf, S. & Gerwert, K. Proton transfer via a transient linear water-molecule chain in a membrane protein. Proc. Natl Acad. Sci. USA 108, 11435–11439 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Regan, L. & DeGrado, W. F. Characterization of a helical protein designed from first principles. Science 241, 976–978 (1988).

    Article  CAS  PubMed  Google Scholar 

  25. Walsh, S. T., Cheng, H., Bryson, J. W., Roder, H. & DeGrado, W. F. Solution structure and dynamics of a de novo designed three-helix bundle protein. Proc. Natl Acad. Sci. USA 96, 5486–5491 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kuhlman, B. et al. Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364–1368 (2003).

    Article  CAS  PubMed  Google Scholar 

  27. Vorobieva, A. A. et al. De novo design of transmembrane β barrels. Science https://doi.org/10.1126/science.abc8182 (2021).

  28. Yang, C. et al. Bottom-up de novo design of functional proteins with complex structural features. Nat. Chem. Biol. 17, 492–500 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule-binding proteins. Science 369, 1227–1233 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cao, L. et al. De novo design of picomolar SARS-CoV-2 miniprotein inhibitors. Science 370, 426–431 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Fleishman, S. J. et al. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332, 816–821 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jiang, L. et al. De novo computational design of retro-aldol enzymes. Science 319, 1387–1391 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lassila, J. K., Privett, H. K., Allen, B. D. & Mayo, S. L. Combinatorial methods for small-molecule placement in computational enzyme design. Proc. Natl Acad. Sci. USA 103, 16710–16715 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Polizzi, N. F. et al. De novo design of a hyperstable non-natural protein–ligand complex with sub-A accuracy. Nat. Chem. 9, 1157–1164 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Koga, N. et al. Principles for designing ideal protein structures. Nature 491, 222–227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Scott, A. J. et al. Constructing ion channels from water-soluble α-helical barrels. Nat. Chem. 13, 643–650 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Xu, C. et al. Computational design of transmembrane pores. Nature 585, 129–134 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Joh, N. H. et al. De novo design of a transmembrane Zn2+-transporting four-helix bundle. Science 346, 1520–1524 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lu, P. et al. Accurate computational design of multipass transmembrane proteins. Science 359, 1042–1046 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Thomaston, J. L. et al. X-ray crystal structure of the influenza A M2 proton channel S31N mutant in two conformational states: an open and shut case. J. Am. Chem. Soc. 141, 11481–11488 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Saotome, K. et al. Structures of the otopetrin proton channels Otop1 and Otop3. Nat. Struct. Mol. Biol. 26, 518–525 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mravic, M. et al. Packing of apolar side chains enables accurate design of highly stable membrane proteins. Science 363, 1418–1423 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Klesse, G., Rao, S., Sansom, M. S. P. & Tucker, S. J. CHAP: a versatile tool for the structural and functional annotation of ion channel pores. J. Mol. Biol. 431, 3353–3365 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lee, S., Liang, R., Voth, G. A. & Swanson, J. M. Computationally efficient multiscale reactive molecular dynamics to describe amino acid deprotonation in proteins. J. Chem. Theory Comput. 12, 879–891 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Knight, C., Lindberg, G. E. & Voth, G. A. Multiscale reactive molecular dynamics. J. Chem. Phys. 137, 22A525 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Yamashita, T., Peng, Y., Knight, C. & Voth, G. A. Computationally efficient multiconfigurational reactive molecular dynamics. J. Chem. Theory Comput. 8, 4863–4875 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Moffat, J. C. et al. Proton transport through influenza A virus M2 protein reconstituted in vesicles. Biophys. J. 94, 434–445 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Ma, C. et al. Identification of the functional core of the influenza A virus A/M2 proton-selective ion channel. Proc. Natl Acad. Sci. USA 106, 12283–12288 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Leiding, T., Wang, J., Martinsson, J., DeGrado, W. F. & Arskold, S. P. Proton and cation transport activity of the M2 proton channel from influenza A virus. Proc. Natl Acad. Sci. USA 107, 15409–15414 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Slope, L. N. & Peacock, A. F. De novo design of xeno-metallo coiled coils. Chem. Asian J. 11, 660–666 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Pinter, T. B. J., Koebke, K. J. & Pecoraro, V. L. Catalysis and electron transfer in de novo designed helical scaffolds. Angew. Chem. Int. Ed. 59, 7678–7699 (2020).

    Article  CAS  Google Scholar 

  53. Khurana, E. et al. Molecular dynamics calculations suggest a conduction mechanism for the M2 proton channel from influenza A virus. Proc. Natl Acad. Sci. USA 106, 1069–1074 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Yi, M., Cross, T. A. & Zhou, H. X. A secondary gate as a mechanism for inhibition of the M2 proton channel by amantadine. J. Phys. Chem. B 112, 7977–7979 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Ramsey, I. S. et al. An aqueous H+ permeation pathway in the voltage-gated proton channel Hv1. Nat. Struct. Mol. Biol. 17, 869–875 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Chamberlin, A. et al. Hydrophobic plug functions as a gate in voltage-gated proton channels. Proc. Natl Acad. Sci. USA 111, E273–E282 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. Takeshita, K. et al. X-ray crystal structure of voltage-gated proton channel. Nat. Struct. Mol. Biol. 21, 352–357 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Wikstrom, M., Krab, K. & Sharma, V. Oxygen activation and energy conservation by cytochrome c oxidase. Chem. Rev. 118, 2469–2490 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hofacker, I. & Schulten, K. Oxygen and proton pathways in cytochrome c oxidase. Proteins 30, 100–107 (1998).

    Article  CAS  PubMed  Google Scholar 

  60. Wikström, M., Verkhovsky, M. I. & Hummer, G. Water-gated mechanism of proton translocation by cytochrome c oxidase. Biochim. Biophys. Acta Bioenerg. 1604, 61–65 (2003).

    Article  Google Scholar 

  61. Tashiro, M. & Stuchebrukhov, A. A. Thermodynamic properties of internal water molecules in the hydrophobic cavity around the catalytic center of cytochrome c oxidase. J. Phys. Chem. B 109, 1015–1022 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Goyal, P., Lu, J., Yang, S., Gunner, M. R. & Cui, Q. Changing hydration level in an internal cavity modulates the proton affinity of a key glutamate in cytochrome c oxidase. Proc. Natl Acad. Sci. USA 110, 18886–18891 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Liang, R., Swanson, J. M. J., Wikstrom, M. & Voth, G. A. Understanding the essential proton-pumping kinetic gates and decoupling mutations in cytochrome c oxidase. Proc. Natl Acad. Sci. USA 114, 5924–5929 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Liang, R., Swanson, J. M., Peng, Y., Wikstrom, M. & Voth, G. A. Multiscale simulations reveal key features of the proton-pumping mechanism in cytochrome c oxidase. Proc. Natl Acad. Sci. USA 113, 7420–7425 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lynch, C. I., Rao, S. & Sansom, M. S. P. Water in nanopores and biological channels: a molecular simulation perspective. Chem. Rev. https://doi.org/10.1021/acs.chemrev.9b00830 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Chen, H. et al. Charge delocalization in proton channels, I: the aquaporin channels and proton blockage. Biophys. J. 92, 46–60 (2007).

    Article  CAS  PubMed  Google Scholar 

  67. Murata, K. et al. Structural determinants of water permeation through aquaporin-1. Nature 407, 599–605 (2000).

    Article  CAS  PubMed  Google Scholar 

  68. Mondal, D., Kolev, V. & Warshel, A. Combinatorial approach for exploring conformational space and activation barriers in computer-aided enzyme design. ACS Catal. 10, 6002–6012 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Tunuguntla, R. H., Allen, F. I., Kim, K., Belliveau, A. & Noy, A. Ultrafast proton transport in sub-1-nm diameter carbon nanotube porins. Nat. Nanotechnol. 11, 639–644 (2016).

    Article  CAS  PubMed  Google Scholar 

  70. Geng, J. et al. Stochastic transport through carbon nanotubes in lipid bilayers and live cell membranes. Nature 514, 612–615 (2014).

    Article  CAS  PubMed  Google Scholar 

  71. Jiang, T. et al. Single-chain heteropolymers transport protons selectively and rapidly. Nature 577, 216–220 (2020).

    Article  CAS  PubMed  Google Scholar 

  72. Caffrey, M. & Cherezov, V. Crystallizing membrane proteins using lipidic mesophases. Nat. Protoc. 4, 706–731 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Caffrey, M. Crystallizing membrane proteins for structure determination: use of lipidic mesophases. Annu. Rev. Biophys. 38, 29–51 (2009).

    Article  CAS  PubMed  Google Scholar 

  74. Kabsch, W. XDS. Acta Crystallogr. D 66, 125–132 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Winn, M. D. et al. Overview of the CCP4 suite and current developments. Acta Crystallogr. D 67, 235–242 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. 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 

  78. Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D 68, 352–367 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Böckmann, A. et al. Characterization of different water pools in solid-state NMR protein samples. J. Biomol. NMR 45, 319–327 (2009).

    Article  PubMed  Google Scholar 

  80. Luo, W. & Hong, M. Conformational changes of an ion channel detected through water–protein interactions using solid-state NMR spectroscopy. J. Am. Chem. Soc. 132, 2378–2384 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Williams, J. K. & Hong, M. Probing membrane protein structure using water polarization transfer solid-state NMR. J. Magn. Reson. 247, 118–127 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Mandala, V. S. et al. Structure and drug binding of the SARS-CoV-2 envelope protein transmembrane domain in lipid bilayers. Nat. Struct. Mol. Biol. 27, 1202–1208 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Gelenter, M. D. et al. Water orientation and dynamics in the closed and open influenza B virus M2 proton channels. Commun. Biol. 4, 338 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Hong, M. et al. Coupling amplification in 2D MAS NMR and its application to torsion angle determination in peptides. J. Magn. Reson. 129, 85–92 (1997).

    Article  CAS  PubMed  Google Scholar 

  85. Krivov, G. G., Shapovalov, M. V. & Dunbrack, R. L. Jr. Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77, 778–795 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40, D370–D376 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  88. Van Der Spoel, D. et al. GROMACS: fast, flexible, and free. J. Comput. Chem. 26, 1701–1718 (2005).

    Article  PubMed  Google Scholar 

  89. Huang, J. & MacKerell, A. D. Jr. CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 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).

    Article  CAS  PubMed  Google Scholar 

  91. Jo, S., Kim, T. & Im, W. Automated builder and database of protein/membrane complexes for molecular dynamics simulations. PLoS ONE 2, e880 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. 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).

    Article  CAS  PubMed  Google Scholar 

  94. Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).

    Article  Google Scholar 

  96. Nelson, J. G., Peng, Y., Silverstein, D. W. & Swanson, J. M. Multiscale reactive molecular dynamics for absolute pKa predictions and amino acid deprotonation. J. Chem. Theory Comput. 10, 2729–2737 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Biswas, R., Tse, Y. L., Tokmakoff, A. & Voth, G. A. Role of presolvation and anharmonicity in aqueous phase hydrated proton solvation and transport. J. Phys. Chem. B 120, 1793–1804 (2016).

    Article  CAS  PubMed  Google Scholar 

  98. Day, T. J. F., Soudackov, A. V., Čuma, M., Schmitt, U. W. & Voth, G. A. A second generation multistate empirical valence bond model for proton transport in aqueous systems. J. Chem. Phys. 117, 5839–5849 (2002).

    Article  CAS  Google Scholar 

  99. Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).

    Article  CAS  Google Scholar 

  100. Bonomi, M. B. et al. Promoting transparency and reproducibility in enhanced molecular simulations. Nat. Methods 16, 670–673 (2019).

    Article  Google Scholar 

  101. Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C. & Bussi, G. PLUMED 2: new feathers for an old bird. Comput. Phys. Commun. 185, 604–613 (2014).

    Article  CAS  Google Scholar 

  102. Grossfield, A. WHAM: the weighted histogram analysis method, v 2.0.9 (University of Rochester, 2002); http://membrane.urmc.rochester.edu/wordpress/?page_id=126

  103. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  104. Hodel, A., Kim, S.-H. & Brünger, A. T. Acta Crystallogr. A 48, 851–858 (1992).

    Article  Google Scholar 

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Acknowledgements

Diffraction data were collected at the GM/CA@APS and ALS BL 8.3.1. GM/CA@APS is supported by the National Cancer Institute (ACB-12002) and the National Institute of General Medical Sciences (AGM-12006 and P30GM138396), and the Eiger 16M detector by the National Institutes of Health (NIH; S10 OD012289). We also acknowledge the Advanced Photon Source, supported by the US Department of Energy (DE; contract DE-AC02-06CH11357), and beamline 8.3.1 at the Advanced Light Source operated by the University of California at San Francisco with support from the NIH (R01 GM124149 and P30 GM124169), Plexxikon and the Integrated Diffraction Analysis Technologies programme (US Department of Energy Office of Biological and Environmental Research). The Advanced Light Source at Lawrence Berkeley National Laboratory is supported by DE (DE-AC02-05CH11231). H.T.K. was supported by the NIH (K99GM138753). W.F.D. was supported by the NIH (R35 GM122603), NSF (CHE 1709506) and the Air Force Office of Scientific Research (FA9550-19-1-0331). L.C.W. and G.A.V. were supported by the NIH (R01 GM053148). J.M.N. was supported by the NIH (5T32HL007731 and F32GM133085). J.L.T. was supported by the NIH (R35GM122603).

Author information

Author notes

  1. Huong T. Kratochvil

    Present address: Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

  2. Laura C. Watkins

    Present address: Kemper Insurance, Chicago, IL, USA

  3. Marco Mravic

    Present address: Department of Integrative Structural and Computational Biology Scripps Research Institute, La Jolla, CA, USA

  4. John M. Nicoludis

    Present address: Genentech, San Francisco, CA, USA

Authors and Affiliations

  1. Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA

    Huong T. Kratochvil, Marco Mravic, Jessica L. Thomaston, John M. Nicoludis & William F. DeGrado

  2. Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics and James Franck Institute, The University of Chicago, Chicago, IL, USA

    Laura C. Watkins & Gregory A. Voth

  3. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Noah H. Somberg & Mei Hong

  4. DLX Scientific, Lawrence, KS, USA

    Lijun Liu

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H.T.K. designed the channels, ran flux measurements, crystallized and collected the X-ray diffraction data, and ran and analysed the classical MD simulations. L.C.W. ran the MS-RMD simulations. L.C.W. and G.A.V. analysed the data. M.M. ran classical MD simulations. J.L.T, J.M.N. and L.L. processed and refined the crystal structures. H.T.K. and W.F.D. analysed the experimental data. All authors contributed to the data analysis and writing of the paper.

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Correspondence to Huong T. Kratochvil, Gregory A. Voth or William F. DeGrado.

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The authors declare no competing interests.

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Nature Chemistry thanks Mihail Barboiu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Composite omit maps (2mFo-DFc) of designed proton channels.

Composite omit maps of the asymmetric unit for a, LQLL, b, LLQL, and one pentamer from the asymmetric unit for c, QQLL, and d, QLQL (shown only for the waters for clarity). All contours at σ = 1.0. Omit maps with simulated cartesian annealing were generated using Phenix, using methodology described in Hodel, et al.104.

Extended Data Fig. 2 Pulse diagram and water-edited 13 C spectra, water buildup curves of membrane-bound LQLL and LLLL peptides.

a, Pulse diagram of water-edited 13 C CP experiment. b, Representative water-edited 13 C spectra of I13 in LQLL and LLLL, measured with 225 ms and 49 ms 1H mixing. The relative intensities of the 49 ms spectrum to the 225 ms spectrum are higher for LQLL Ile13 than LLLL Ile13, especially for the sidechain Cγ2 and Cδ carbons. c, Site-resolved water buildup curves for Ile13 in LQLL and LLLL. For all 13C sites, LQLL shows a faster water buildup than LLLL, consistent with water molecules in the pore lumen due to the Gln10 PLS.

Source data

Extended Data Fig. 3 Overview of proton flux measurements.

a, Full schematic for proton flux measurement including CCCP step, which is included to check vesicle leakiness and confirm proton selectivity. b, Chemical structures of key components of vesicle assay. c, Calibration curves for HPTS at ~5 µM in 12 solutions of 50 mM K2SO4, 30 mM K2HPO4 at different pH values for two plate readers used in data collection process. Unless stated, all data collected with instrument that generated the blue calibration curve. Fits used for downstream data processing shown for each of the two instruments with adjusted R-squared values of 0.9866 and 0.9998 for the left and right curves, respectively. Data for n = 3 independent samples shown as mean values +/− SD.

Extended Data Fig. 4 All proton flux assay data for long kinetics runs.

Long kinetics runs of about 5 hours total for a, empty, b, LLLL, and c, LQLL vesicles. Dotted lines denote times in experiment when valinomycin and CCCP were added to the samples. Three samples of the different conditions were measured in triplicate. These long-time measurements reveal that the vesicles are not leaky to Na+, K+, or H+ and maintain their cargo and assembly over the entire course of the measurement. d, From the linear regression fits of the first 220 s following addition of valinomycin, all slopes (which give the initial rates (in M/s)) were used to calculate the mean and standard error. The one-way ANOVA analysis (with Dunn’s test) reveals that LLLL rates are not significantly different (p > 0.05, adjusted p = 0.4021) when compared to the control empty vesicles. LQLL rates, however, are statistically significant (p < 0.0001, p = 3.23E-5) when compared to the control empty vesicles using one-way ANOVA analysis with Dunn’s test. All data from n = 3 independent samples are shown as mean values +/− SD.

Source data

Extended Data Fig. 5 All proton flux assay data for QLLL vesicle samples.

Nine samples (each run in triplicate with shaded error bars shown) containing 1:500 peptide:lipid ratio; samples were run independently in the assay. a, pHin as a function of time throughout the measurement for each independent sample. b, Mean and standard deviation for data collection. c, Data prior to CCCP addition shows little change in pHin after addition of valinomycin. d, Fits for the initial 50 seconds following addition of valinomycin. From the linear regression fits, all slopes (which give the initial rates (in M/s)) were used to calculate mean and standard error presented in Fig. 6g and Supplementary Table 3.

Source data

Extended Data Fig. 6 All proton flux assay data for LLQL vesicle samples.

Eight samples (each run in triplicate with shaded error bars shown) containing 1:500 peptide:lipid ratio; samples were run independently in the assay. a, pHin as a function of time throughout the measurement for each independent sample. b, Mean and standard deviation for data collection. c, Data prior to CCCP addition shows notable change in pHin after addition of valinomycin. d, Fits for the initial 50 seconds following addition of valinomycin. From the linear regression fits, all slopes (which give the initial rates (in M/s)) were used to calculate mean and standard error presented in Fig. 6g and Supplementary Table 3.

Source data

Extended Data Fig. 7 All proton flux assay data for QLQL vesicle samples.

Seven samples (each run in triplicate with shaded error bars shown) containing 1:500 peptide:lipid ratio; samples were run independently in the assay. a, pHin as a function of time throughout the measurement for each independent sample. b, Mean and standard deviation for data collection. c, Data prior to CCCP addition shows notable change in pHin after addition of valinomycin. d, Fits for the initial 50 seconds following addition of valinomycin. From the linear regression fits, all slopes (which give the initial rates (in M/s)) were used to calculate mean and standard error presented in Fig. 6g and Supplementary Table 3.

Source data

Extended Data Fig. 8 Determining orientation of pentamers in vesicles.

a, HPLC trace of unreacted and reacted peptides following reaction with the highly polar, amine-reactive methyltetrazine 3-sulfo-N-hydroxysuccinimide ester (methyltetrazine sulfo-NHS, see Methods). The only amine-reactive groups are the N-terminus or the N-terminal lysine sidechain. Thus, only peptides in which N-terminus is exposed on the outside of the vesicle should react to the dye. b, HPLC traces of mixtures of different ratios of non-reacted and reacted peptides. c, Calibration curves for area under the curve for nonreacted and reacted peaks in the HPLC traces corresponding to the different mixtures in b. Data shown are for n = 2 independent experiments and shown as mean values +/− SD. d, Traces of three independent samples of LQLL pentamers from vesicles after reaction with methyltetrazine sulfo-NHS. e, Using the calibration curves in c, the area under the curve was determined for each sample. The data indicate that half the amines react, as expected from a random orientation of pentamers in the lipid vesicle.

Source data

Extended Data Table 1 Length (lobs) of longer and shorter (lobs,sh) hydrophobic stretches in designed channels
Full size table
Extended Data Table 2 Data collection and refinement statistics for all structures
Full size table

Supplementary information

Supplementary Information

Materials and Methods, Supplementary Figs. 1–25, Tables 1–6, a description of Video 1, and complete references and notes.

Reporting Summary

Supplementary Video 1

Snapshots from MS-RMD simulations along the MFEP (Fig. 5) depict the proton (represented as a yellow hydronium in the video) inducing the formation of water wires in hydrophobic regions of the channel lumen. The proton moves along these transiently forming water wires to make its way down to a region just below the PLS, formed by the green Gln residues. Once near this site, the presence of the proton enables the formation of a secondary water wire through the longer hydrophobic region. This allows the proton to traverse the rest of the way across the channel.

Source data

Source Data Fig. 2

Unclipped gel.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 6

All data for every panel in Fig. 6, with each panel in unique tabs.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 4

H+ concentration (M) for first 220 s after the addition of valinomycin for long kinetics runs.

Source Data Extended Data Fig. 5

All data for QLLL.

Source Data Extended Data Fig. 6

All data for LLQL.

Source Data Extended Data Fig. 7

All data for QLQL.

Source Data Extended Data Fig. 8

Data for calibration curves.

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Kratochvil, H.T., Watkins, L.C., Mravic, M. et al. Transient water wires mediate selective proton transport in designed channel proteins. Nat. Chem. 15, 1012–1021 (2023). https://doi.org/10.1038/s41557-023-01210-4

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