Artificial water channels are synthetic molecules that aim to mimic the structural and functional features of biological water channels (aquaporins). Here we report on a cluster-forming organic nanoarchitecture, peptide-appended hybridarene (PAH), as a new class of artificial water channels. Fluorescence experiments and simulations demonstrated that PAHs can form, through lateral diffusion, clusters in lipid membranes that provide synergistic membrane-spanning paths for a rapid and selective water permeation through water-wire networks. Quantitative transport studies revealed that PAHs can transport >109 water molecules per second per molecule, which is comparable to aquaporin water channels. The performance of these channels exceeds the upper bound limit of current desalination membranes by a factor of ~104, as illustrated by the water/NaCl permeability–selectivity trade-off curve. PAH’s unique properties of a high water/solute permselectivity via cooperative water-wire formation could usher in an alternative design paradigm for permeable membrane materials in separations, energy production and barrier applications.
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The datasets that support the finding of this study are available in ScholarSphere repository with the identifier(s) (https://doi.org/10.26207/ykbm-r806).
Preston, G. M., Carroll, T. P., Guggino, W. B. & Agre, P. Appearance of water channels in xenopus oocytes expressing red cell CHIP28 protein. Science 256, 385–387 (1992).
Noda, Y., Sohara, E., Ohta, E. & Sasaki, S. Aquaporins in kidney pathophysiology. Nat. Rev. Nephrol. 6, 168 (2010).
Park, H. B., Kamcev, J., Robeson, L. M., Elimelech, M. & Freeman, B. D. Maximizing the right stuff: the trade-off between membrane permeability and selectivity. Science 356, eaab0530 (2017).
Werber, J. R., Osuji, C. O. & Elimelech, M. Materials for next-generation desalination and water purification membranes. Nat. Rev. Mater. 1, 16018 (2016).
Shen, Y.-x., Saboe, P. O., Sines, I. T., Erbakan, M. & Kumar, M. Biomimetic membranes: a review. J. Membr. Sci. 454, 359–381 (2014).
Hélix-Nielsen, C. Biomimetic membranes as a technology platform: challenges and opportunities. Membranes 8, 44 (2018).
Song, W., Lang, C., Shen, Y.-.x. & Kumar, M. Design considerations for artificial water channel-based membranes. Ann. Rev. Mater. Res. 48, 57–82 (2018).
Song, W., Tu, Y.-M., Oh, H., Samineni, L. & Kumar, M. Hierarchical optimization of high-performance biomimetic and bioinspired membranes. Langmuir 35, 589–607 (2019).
Horner, A. et al. The mobility of single-file water molecules is governed by the number of H-bonds they may form with channel-lining residues. Sci. Adv. 1, e1400083 (2015).
Shen, Y.-x. et al. Achieving high permeability and enhanced selectivity for angstrom-scale separations using artificial water channel membranes. Nat. Commun. 9, 2294 (2018).
Licsandru, E. et al. Salt-Excluding artificial water channels exhibiting enhanced dipolar water and proton translocation. J. Am. Chem. Soc. 138, 5403–5409 (2016).
Chen, L. et al. Chiral selective transmembrane transport of amino acids through artificial channels. J. Am. Chem. Soc. 135, 2152–2155 (2013).
Shen, Y.-x. et al. Highly permeable artificial water channels that can self-assemble into two-dimensional arrays. Proc. Natl Acad. Sci. USA 112, 9810–9815 (2015).
Tunuguntla, R. H. et al. Enhanced water permeability and tunable ion selectivity in subnanometer carbon nanotube porins. Science 357, 792–796 (2017).
Freger, V. Selectivity and polarization in water channel membranes: lessons learned from polymeric membranes and CNTs. Faraday Discuss. 209, 371–388 (2018).
Song, W. & Kumar, M. Artificial water channels: toward and beyond desalination. Curr. Opin. Chem. Eng. 25, 9–17 (2019).
Sui, H., Han, B.-G., Lee, J. K., Walian, P. & Jap, B. K. Structural basis of water-specific transport through the AQP1 water channel. Nature 414, 872–878 (2001).
Tajkhorshid, E. et al. Control of the selectivity of the aquaporin water channel family by global orientational tuning. Science 296, 525–530 (2002).
Saparov, S. M. et al. Mobility of a one-dimensional confined file of water molecules as a function of file length. Phys. Rev. Lett. 96, 148101 (2006).
Hannesschläger, C., Barta, T., Siligan, C. & Horner, A. Quantification of water flux in vesicular systems. Sci. Rep. 8, 8516 (2018).
Borgnia, M. J., Kozono, D., Calamita, G., Maloney, P. C. & Agre, P. Functional reconstitution and characterization of AqpZ, the E. coli water channel protein11 edited by W. Baumeister. J. Mol. Biol. 291, 1169–1179 (1999).
Pohl, P., Saparov, S. M., Borgnia, M. J. & Agre, P. Highly selective water channel activity measured by voltage clamp: analysis of planar lipid bilayers reconstituted with purified AqpZ. Proc. Natl Acad. Sci. USA 98, 9624–9629 (2001).
Horner, A. & Pohl, P. Comment on “Enhanced water permeability and tunable ion selectivity in subnanometer carbon nanotube porins”. Science 359, eaap9173 (2018).
Baaden, M. et al. Biomimetic water channels: general discussion. Faraday Discuss. 209, 205–229 (2018).
Geise, G. M., Park, H. B., Sagle, A. C., Freeman, B. D. & McGrath, J. E. Water permeability and water/salt selectivity tradeoff in polymers for desalination. J. Membr. Sci. 369, 130–138 (2011).
Toyoshima, Y. & Thompson, T. E. Chloride flux in bilayer membranes. Chloride permeability in aqueous dispersions of single-walled, bilayer vesicles. Biochemistry 14, 1525–1531 (1975).
Lang, C. et al. Biomimetic transmembrane channels with high stability and transporting efficiency from helically folded macromolecules. Angew. Chem. Int. Ed. 55, 9723–9727 (2016).
Taylor, G. J., Venkatesan, G. A., Collier, C. P. & Sarles, S. A. Direct in situ measurement of specific capacitance, monolayer tension, and bilayer tension in a droplet interface bilayer. Soft Matter 11, 7592–7605 (2015).
Najem, J. S. et al. Memristive ion channel-doped biomembranes as synaptic mimics. ACS Nano 12, 4702–4711 (2018).
Venkatesan, G. A. et al. Adsorption kinetics dictate monolayer self-assembly for both lipid-in and lipid-out approaches to droplet interface bilayer formation. Langmuir 31, 12883–12893 (2015).
Taylor, G. J. & Sarles, S. A. Heating-enabled formation of droplet interface bilayers using Escherichia coli total lipid extract. Langmuir 31, 325–337 (2015).
Najem, J. S. et al. Dynamical nonlinear memory capacitance in biomimetic membranes. Nat. Commun. 10, 3239 (2019).
Werber, J. R. & Elimelech, M. Permselectivity limits of biomimetic desalination membranes. Sci. Adv. 4, eaar8266 (2018).
Erbakan, M. et al. Molecular cloning, overexpression and characterization of a novel water channel protein from Rhodobacter sphaeroides. PLoS ONE 9, e86830 (2014).
Chowdhury, R. et al. PoreDesigner for tuning solute selectivity in a robust and highly permeable outer membrane pore. Nat. Commun. 9, 3661 (2018).
Chowdhury, R., Allan, M. F. & Maranas, C. D. OptMAVEn-2.0: De novo design of variable antibody regions against targeted antigen epitopes. Antibodies 7, 23.
Horner, A. & Pohl, P. Single-file transport of water through membrane channels. Faraday Discuss. 209, 9–33 (2018).
Woodle, M. C. & Papahadjopoulos, D.  Liposome preparation and size characterization. Methods Enzymol. 171, 193–217 (1989).
Latimer, P. & Pyle, B. E. Light scattering at various angles: theoretical predictions of the effects of particle volume changes. Biophys. J. 12, 764–773 (1972).
Tong, J., Canty, J. T., Briggs, M. M. & McIntosh, T. J. The water permeability of lens aquaporin-0 depends on its lipid bilayer environment. Exp. Eye Res. 113, 32–40 (2013).
Biwersi, J., Tulk, B. & Verkman, A. S. Long-Wavelength chloride-sensitive fluorescent indicators. Anal. Biochem. 219, 139–143 (1994).
Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005).
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).
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential function for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).
Decker, K. et al. Selective permeability of truncated aquaporin 1 in silico. ACS Biomater. Sci. Eng. 3, 342–348 (2017).
Feller, S. E., Zhang, Y., Pastor, R. W. & Brooks, B. R. Constant pressure molecular dynamics simulation: the Langevin piston method. J. Chem. Phys. 103, 4613–4621 (1995).
Martyna, G. J., Tobias, D. J. & Klein, M. L. Constant pressure molecular dynamics algorithms. J. Chem. Phys. 101, 4177–4189 (1994).
Sindhikara, D. J., Kim, S., Voter, A. F. & Roitberg, A. E. Bad seeds sprout perilous dynamics: stochastic thermostat induced trajectory synchronization in biomolecules. J. Chem. Theory Comput. 5, 1624–1631 (2009).
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).
Klauda, J. B. et al. Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114, 7830–7843 (2010).
Yoo, J. & Aksimentiev, A. New tricks for old dogs: improving the accuracy of biomolecular force fields by pair-specific corrections to non-bonded interactions. Phys. Chem. Chem. Phys. 20, 8432–8449 (2018).
Yoo, J. & Aksimentiev, A. Improved parametrization of Li+ Na+, K+, and Mg2+ ions for all-atom molecular dynamics simulations of nucleic acid systems. J Phys Chem Lett 3, 45–50 (2011).
Miyamoto, S. & Kollman, P. A. SETTLE: an analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 13, 952–962 (1992).
Andersen, H. C. RATTLE: a ‘velocity’ version of the SHAKE algorithm for molecular dynamics calculations. J. Comput. Phys. 52, 24–34 (1983).
Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. Model 14, 33–38 (1996).
The authors acknowledge financial support from the National Science Foundation (NSF) CAREER grant (CBET-1552571) to M.K. for this work. A.A. and H.J. acknowledge support from the National Science Foundation under grant DMR-1827346 and the National Institutes of Health under grant P41-GM104601. Additional support was provided by NSF grant CBET-1804836 to M.K. Supercomputer time was provided through XSEDE Allocation Grant no. MCA05S028 and the Blue Waters petascale supercomputer system at the University of Illinois at Urbana−Champaign. H.J. acknowledges the Government of India for the DST-Overseas Visiting Fellowship in Nano Science and Technology.
The authors declare no competing interests.
Peer review information Nature Nanotechnology thanks Andreas Horner, Meni Wanunu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Supplementary methods and discussions, Figs. 1–30 and Video captions 1–5.
Molecular Model of PAH channels. The central constriction of the hybridarene macromolecules and the nearest phenylalanine moieties are coloured in green. The remaining phenylalanine moieties are coloured in purple.
Top and cross-sectional views of a MD system featuring a 22-mer cluster (green and purple) of PAH channels embedded in a POPC lipid bilayer membrane (turquoise). For visual clarity, water and ions are not shown.
A 100 ns long MD simulation trajectory of 22-mer PAH cluster with the ±1 V externally applied bias across the simulation box. In these applied electric field MD simulations, the cluster was harmonically restrained to its equilibrium configuration obtained at the end of a 400 ns-long MD simulation. Lipid bilayer membrane is shown in turquoise, whereas the PAH units are shown in purple and green. Na+ and Cl− ions are shown in yellow and blue colour with vdW representation. Water is not shown for clarity.
MD system featuring a 22-mer cluster (green and purple) of PAH channels embedded in a POPC lipid bilayer membrane (turquoise), showing cooperative water wire network formation spanning the membrane. Water, Na+, and Cl− atoms are shown in red and white, magenta, and yellow, respectively.
Cut-away view of a simulation system illustrating the cooperative water permeation through a water wire network. The 22-mer PAH cluster, POPC lipid membrane, and water molecules are shown in green and purple, turquoise, and red and white, respectively.
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Song, W., Joshi, H., Chowdhury, R. et al. Artificial water channels enable fast and selective water permeation through water-wire networks. Nat. Nanotechnol. 15, 73–79 (2020). https://doi.org/10.1038/s41565-019-0586-8
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