Abstract
Chronic neuropathic pain, caused by nerve damage or disease, is increasing in prevalence, but current treatments are ineffective and over-reliant on opioids. The neuronal glycine transporter, GlyT2, regulates inhibitory glycinergic neurotransmission and represents a promising target for new analgesics. However, most GlyT2 inhibitors cause significant side effects, in part due to irreversible inhibition at analgesic doses. Here we develop a reversible inhibitor of GlyT2, RPI-GLYT2-82, and identify its binding site by determining cryo-EM structures of human GlyT2. We capture three fundamental conformational states of GlyT2 in the substrate-free state, and bound to either glycine, RPI-GLYT2-82 or the pseudo-irreversible inhibitor ORG25543. We demonstrate that RPI-GLYT2-82 dissociates from GlyT2 faster than ORG25543, providing analgesia in mouse neuropathic pain models without on-target side-effects or addiction liability. Our data provide a mechanistic understanding of allosteric inhibition of glycine transport, enabling structure-based design of non-opioid analgesics.
Data availability
Atomic coordinates of hGlyT2Δ185 bound to ORG25543, RPI-GLYT2-82, glycine, or in substrate-free state have been deposited in the Protein Data Bank (PDB) under accession codes 9HUE, 9HUF, 9R1H, and 9HUG, respectively. The corresponding cryo-EM maps have been deposited in the Electron Microscopy Data Bank (EMDB) under accession numbers EMD-52409, EMD-52410, EMDB-53509, and EMD-52411, respectively. Molecular dynamics starting conformations, mdp files and topologies are available at (https://github.com/OMaraLab/GlyT2_2024) and on Zenodo as entry 18179190 [doi.org/10.5281/zenodo.18179190]96. Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request. Source data are provided with this paper.
References
Finnerup, N. B. et al. Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis. Lancet Neurol. 14, 162–173 (2015).
Colloca, L. et al. Neuropathic pain. Nat. Rev. Dis. Prim. 3, 17002 (2017).
Lu, Y. et al. A feed-forward spinal cord glycinergic neural circuit gates mechanical allodynia. J. Clin. Invest 123, 4050–4062 (2013).
Aroeira, R. I., Sebastiao, A. M. & Valente, C. A. GlyT1 and GlyT2 in brain astrocytes: expression, distribution and function. Brain Struct. Funct. 219, 817–830 (2014).
Vandenberg, R. J., Ryan, R. M., Carland, J. E., Imlach, W. L. & Christie, M. J. Glycine transport inhibitors for the treatment of pain. Trends Pharm. Sci. 35, 423–430 (2014).
Cioffi, C. L. Modulation of glycine-mediated spinal neurotransmission for the treatment of chronic pain. J. Med Chem. 61, 2652–2679 (2018).
Harvey, R. J. & Yee, B. K. Glycine transporters as novel therapeutic targets in schizophrenia, alcohol dependence and pain. Nat. Rev. Drug Discov. 12, 866–885 (2013).
Rousseau, F., Aubrey, K. R. & Supplisson, S. The glycine transporter GlyT2 controls the dynamics of synaptic vesicle refilling in inhibitory spinal cord neurons. J. Neurosci. 28, 9755–9768 (2008).
Arribas-Gonzalez, E., de Juan-Sanz, J., Aragon, C. & Lopez-Corcuera, B. Molecular basis of the dominant negative effect of a glycine transporter 2 mutation associated with hyperekplexia. J. Biol. Chem. 290, 2150–2165 (2015).
Morrow, J. A. et al. Molecular cloning and functional expression of the human glycine transporter GlyT2 and chromosomal localisation of the gene in the human genome. FEBS Lett. 439, 334–340 (1998).
Jardetzky, O. Simple allosteric model for membrane pumps. Nature 211, 969–970 (1966).
Subramanian, N. et al. Identification of a 3rd Na+ Binding Site of the Glycine Transporter, GlyT2. PLoS One 11, e0157583 (2016).
Benito-Munoz, C. et al. Modification of a putative third sodium site in the glycine transporter GlyT2 influences the chloride dependence of substrate transport. Front Mol. Neurosci. 11, 347 (2018).
Shahsavar, A. et al. Structural insights into the inhibition of glycine reuptake. Nature 591, 677–681 (2021).
Motiwala, Z. et al. Structural basis of GABA reuptake inhibition. Nature 606, 820–826 (2022).
Li, Y. et al. Dopamine reuptake and inhibitory mechanisms in human dopamine transporter. Nature 632, 686–694 (2024).
Nielsen, J. C. et al. Structure of the human dopamine transporter in complex with cocaine. Nature 632, 678–685 (2024).
Coleman, J. A., Green, E. M. & Gouaux, E. X-ray structures and mechanism of the human serotonin transporter. Nature 532, 334–339 (2016).
Penmatsa, A., Wang, K. H. & Gouaux, E. X-ray structure of dopamine transporter elucidates antidepressant mechanism. Nature 503, 85–90 (2013).
Srivastava, D. K. et al. Structure of the human dopamine transporter and mechanisms of inhibition. Nature 632, 672–677 (2024).
Wang, K. H., Penmatsa, A. & Gouaux, E. Neurotransmitter and psychostimulant recognition by the dopamine transporter. Nature 521, 322–327 (2015).
Plenge, P. et al. The antidepressant drug vilazodone is an allosteric inhibitor of the serotonin transporter. Nat. Commun. 12, 5063 (2021).
Pedersen, C. N. et al. Cryo-EM structure of the dopamine transporter with a novel atypical non-competitive inhibitor bound to the orthosteric site. J. Neurochem 168, 2043–2055 (2024).
Hu, T. et al. Transport and inhibition mechanisms of the human noradrenaline transporter. Nature 632, 930–937 (2024).
Wei, Y. et al. Transport mechanism and pharmacology of the human GlyT1. Cell 187, 1719–1732 e1714 (2024).
Vandenberg, R. J., Shaddick, K. & Ju, P. Molecular basis for substrate discrimination by glycine transporters. J. Biol. Chem. 282, 14447–14453 (2007).
Benito-Munoz, C. et al. Structural determinants of the neuronal glycine transporter 2 for the selective inhibitors ALX1393 and ORG25543. ACS Chem. Neurosci. 12, 1860–1872 (2021).
Wilson, B. S. et al. Peripheral administration of selective glycine transporter-2 inhibitor, Oleoyl-D-Lysine, reverses chronic neuropathic pain but not acute or inflammatory pain in male mice. J. Pharm. Exp. Ther. 382, 246–255 (2022).
Mingorance-Le Meur, A. et al. Reversible inhibition of the glycine transporter GlyT2 circumvents acute toxicity while preserving efficacy in the treatment of pain. Br. J. Pharm. 170, 1053–1063 (2013).
Caulfield, W. L. et al. The first potent and selective inhibitors of the glycine transporter type 2. J. Med Chem. 44, 2679–2682 (2001).
Chater, R. C. et al. The efficacy of the analgesic GlyT2 inhibitor, ORG25543, is determined by two connected allosteric sites. J. Neurochem. https://doi.org/10.1111/jnc.16028 (2023).
Bradaia, A., Schlichter, R. & Trouslard, J. Role of glial and neuronal glycine transporters in the control of glycinergic and glutamatergic synaptic transmission in lamina X of the rat spinal cord. J. Physiol. 559, 169–186 (2004).
Morita, K. et al. Spinal antiallodynia action of glycine transporter inhibitors in neuropathic pain models in mice. J. Pharm. Exp. Ther. 326, 633–645 (2008).
Mostyn, S. N. et al. Development of an N-acyl amino acid that selectively inhibits the glycine transporter 2 to produce analgesia in a rat model of chronic pain. J. Med Chem. 62, 2466–2484 (2019).
Jeong, H. J., Vandenberg, R. J. & Vaughan, C. W. N-arachidonyl-glycine modulates synaptic transmission in superficial dorsal horn. Br. J. Pharm. 161, 925–935 (2010).
Lee, H. J. et al. Reduction of postoperative pain and opioid consumption by VVZ-149, first-in-class analgesic molecule: a confirmatory phase 3 trial of laparoscopic colectomy. J. Clin. Anesth. 101, 111729 (2025).
Nedeljkovic, S. S. et al. Exploratory study of VVZ-149, a novel analgesic molecule, in the affective component of acute postoperative pain after laparoscopic colorectal surgery. J. Clin. Anesth. 76, 110576 (2022).
Pang, M. H., Kim, Y., Jung, K. W., Cho, S. & Lee, D. H. A series of case studies: practical methodology for identifying antinociceptive multi-target drugs. Drug Discov. Today 17, 425–434 (2012).
Tonge, P. J. Drug-target kinetics in drug discovery. ACS Chem. Neurosci. 9, 29–39 (2018).
Cusack, K. P. et al. Design strategies to address kinetics of drug binding and residence time. Bioorg. Med Chem. Lett. 25, 2019–2027 (2015).
Girotto, G. L. et al. The N-terminal tail of the glycine transporter: role in transporter phosphorylation. Med. Res. Arch. https://doi.org/10.18103/mra.v8i5.2085 (2020).
Yamashita, A., Singh, S. K., Kawate, T., Jin, Y. & Gouaux, E. Crystal structure of a bacterial homologue of Na+/Cl−dependent neurotransmitter transporters. Nature 437, 215–223 (2005).
Frangos, Z. J. et al. Membrane cholesterol regulates inhibition and substrate transport by the glycine transporter, GlyT2. Life Sci. Alliance https://doi.org/10.26508/lsa.202201708 (2023).
Hong, W. C. & Amara, S. G. Membrane cholesterol modulates the outward facing conformation of the dopamine transporter and alters cocaine binding. J. Biol. Chem. 285, 32616–32626 (2010).
Nunez, E., Alonso-Torres, P., Fornes, A., Aragon, C. & Lopez-Corcuera, B. The neuronal glycine transporter GLYT2 associates with membrane rafts: functional modulation by lipid environment. J. Neurochem 105, 2080–2090 (2008).
Liu, X., Mitrovic, A. D. & Vandenberg, R. J. Glycine transporter 1 associates with cholesterol-rich membrane raft microdomains. Biochem Biophys. Res Commun. 384, 530–534 (2009).
Zeppelin, T., Ladefoged, L. K., Sinning, S., Periole, X. & Schiott, B. A direct interaction of cholesterol with the dopamine transporter prevents its out-to-inward transition. PLoS Comput Biol. 14, e1005907 (2018).
Malinauskaite, L. et al. A mechanism for intracellular release of Na+ by neurotransmitter/sodium symporters. Nat. Struct. Mol. Biol. 21, 1006–1012 (2014).
Coleman, J. A. et al. Serotonin transporter-ibogaine complexes illuminate mechanisms of inhibition and transport. Nature 569, 141–145 (2019).
Tan, J. et al. Molecular basis of human noradrenaline transporter reuptake and inhibition. Nature 632, 921–929 (2024).
Focht, D. et al. A non-helical region in transmembrane helix 6 of hydrophobic amino acid transporter MhsT mediates substrate recognition. EMBO J. 40, e105164 (2021).
Hao, M. H., Haq, O. & Muegge, I. Torsion angle preference and energetics of small-molecule ligands bound to proteins. J. Chem. Inf. Model 47, 2242–2252 (2007).
Zhang, Y. et al. Structural elements required for coupling ion and substrate transport in the neurotransmitter transporter homolog LeuT, Proc. Natl. Acad. Sci. USA. https://doi.org/10.1073/pnas.1716870115 (2018).
Ben-Yona, A., Bendahan, A. & Kanner, B. I. A glutamine residue conserved in the neurotransmitter:sodium:symporters is essential for the interaction of chloride with the GABA transporter GAT-1. J. Biol. Chem. 286, 2826–2833 (2011).
Zhang, Y. W. et al. Chloride-dependent conformational changes in the GlyT1 glycine transporter. Proc. Natl. Acad. Sci. USA. https://doi.org/10.1073/pnas.2017431118 (2021).
Roux, M. J. & Supplisson, S. Neuronal and glial glycine transporters have different stoichiometries. Neuron 25, 373–383 (2000).
Malinauskaite, L. et al. A conserved leucine occupies the empty substrate site of LeuT in the Na(+)-free return state. Nat. Commun. 7, 11673 (2016).
Mostyn, S. N. et al. Identification of an allosteric binding site on the human glycine transporter, GlyT2, for bioactive lipid analgesics. Elife https://doi.org/10.7554/eLife.47150 (2019).
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Pettersen, E. F. et al. UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
Kidmose, R. T. et al. Namdinator - automatic molecular dynamics flexible fitting of structural models into cryo-EM and crystallography experimental maps. IUCrJ 6, 526–531 (2019).
Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr D. Struct. Biol. 75, 861–877 (2019).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr D. Biol. Crystallogr 66, 486–501 (2010).
Croll, T. I. ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps. Acta Crystallogr D. Struct. Biol. 74, 519–530 (2018).
Bennett, G. J. & Xie, Y. K. A peripheral mononeuropathy in rat that produces disorders of pain sensation like those seen in man. Pain 33, 87–107 (1988).
Seltzer, Z., Dubner, R. & Shir, Y. A novel behavioral model of neuropathic pain disorders produced in rats by partial sciatic nerve injury. Pain 43, 205–218 (1990).
Yoon, C., Wook, Y. Y., Sik, N. H., Ho, K. S. & Mo, C. J. Behavioral signs of ongoing pain and cold allodynia in a rat model of neuropathic pain. Pain 59, 369–376 (1994).
McKendrick, G. et al. Ketamine blocks morphine-induced conditioned place preference and anxiety-like behaviors in mice. Front Behav. Neurosci. 14, 75 (2020).
Schmid, N. et al. Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur. Biophys. J. 40, 843–856 (2011).
Sondergaard, C. R., Olsson, M. H., Rostkowski, M. & Jensen, J. H. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J. Chem. Theory Comput 7, 2284–2295 (2011).
Olsson, M. H., Sondergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput 7, 525–537 (2011).
Stroet, M. et al. Automated topology builder version 3.0: prediction of solvation free enthalpies in water and hexane. J. Chem. Theory Comput 14, 5834–5845 (2018).
Pan, X., Wang, H., Li, C., Zhang, J. Z. H. & Ji, C. MolGpka: A Web Server for Small Molecule pK(a) Prediction Using a Graph-Convolutional Neural Network. J. Chem. Inf. Model 61, 3159–3165 (2021).
Knight, C. J. & Hub, J. S. MemGen: a general web server for the setup of lipid membrane simulation systems. Bioinformatics 31, 2897–2899 (2015).
Hermans, J., Berendsen, H. J. C., Van Gunsteren, W. F. & Postma, J. P. M. A consistent empirical potential for water-protein interactions. Biopolymers 23, 1513–1518 (1984).
Feenstra, K. A., Hess, B. & Berendsen, H. J. C. Improving efficiency of large time-scale molecular dynamics simulations of hydrogen-rich systems. J. Comput Chem. 20, 786–798 (1999).
Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007).
Parrinello, M. & Rahman, A. Crystal structure and pair potentials: a molecular-dynamics study. Phys. Rev. Lett. 45, 1196–1199 (1980).
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).
Hess, B. P-LINCS: a parallel linear constraint solver for molecular simulation. J. Chem. Theory Comput 4, 116–122 (2008).
Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph 14, 33 (1996).
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).
PLUMED consortium, Promoting transparency and reproducibility in enhanced molecular simulations. Na.t Methods 16, 670–673 (2019).
Gowers, R. J. et al. in Conference: PROC. OF THE 15th PYTHON IN SCIENCE CONF. (SCIPY 2016); 2016-07-11 - 2016-07-11; Medium: ED; Size: 98 (Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), United States, 2019).
Genheden, S. & Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 10, 449–461 (2015).
Massova, I. & Kollman, P. A. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. Des. 18, 113–135 (2000).
Kumari, R., Kumar, R., Open Source Drug Discovery, C. & Lynn, A. g_mmpbsa–a GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model 54, 1951–1962 (2014).
Baker, N. A., Sept, D., Joseph, S., Holst, M. J. & McCammon, J. A. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. USA 98, 10037–10041 (2001).
Wang, E. et al. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem. Rev. 119, 9478–9508 (2019).
Jurrus, E. et al. Improvements to the APBS biomolecular solvation software suite. Protein Sci. 27, 112–128 (2018).
Krauth, W. et al. Statistical Mechanics: Algorithms and Computations. 13, (OUP Oxford, 2006).
Sharp, K. A. & Honig, B. Electrostatic interactions in macromolecules: theory and applications. Annu Rev. Biophys. Biophys. Chem. 19, 301–332 (1990).
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput Chem. 31, 455–461 (2010).
Cantwell Chater, R. P. et al. A reversible allosteric inhibitor of GlyT2 for neuropathic pain without on-target side effects. Github, https://doi.org/10.5281/zenodo.18179190 (2025).
Acknowledgements
We thank Shannon N. Mostyn for early contribution to producing the first hGlyT2 constructs. We thank Julian P. Storm and other members of the ASH lab for helpful discussions, and Dr. Roger Dawson for comments on the manuscript. The plasmid for expression of His-tagged HRV-3C protease was a kind gift from Dr. Eric R. Geertsma. We thank Lise Kristensen for access to the protein production facility, and Eva-Marie L. M. A. Pedersen for assistance with virus production, at the Department of Drug Design and Pharmacology, University of Copenhagen. The cryo-EM data was collected at the Core Facility for Integrated Microscopy (CFIM), Faculty of Health and Medical Sciences, University of Copenhagen, supported by the Novo Nordisk Foundation (grants NNF17SA0024386 and NNF22OC0075808). We acknowledge the support offered at the CFIM by Tilmann Pape and Nicholas Sofos. We thank Maria M. Garcia Alai for access to sample preparation and crystallization facility at EMBL Hamburg; Angelica Struve Garcia, Lucas Defelipe, and David R. Carrillo for technical assistance. We are grateful to Assoc. Prof. David Chalmers for access to his Silico software package (http://silico.sourceforge.net). This project has received funding from the Lundbeck Foundation (R368-2021-522), the Novo Nordisk Foundation (NNF23OC0087107), Brødrene Hartmanns Foundation (23080143), and EU Interreg Öresund-Kattegat-Skagerrak project ‘Hanseatic Life Science Research Infrastructure Consortium’ (HALRIC, PP08) to A.S., the National Institute on Drug Abuse, USA, (NIH R01DA048879) to C.L.C. and R.J.V., (NIDA IRP ZIA000069) to M.M and Pain Foundation Ltd to K.R.A. K.E.A. was supported by a Postgraduate scholarship from the University of Sydney. Molecular dynamics simulations were supported by the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS), with access to computational resources provided by the National Computational Infrastructure and Pawsey Supercomputing Research Centre through the National Computational Merit Allocation Scheme. The contributions of the NIH authors are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.
Author information
Authors and Affiliations
Contributions
C.L.C., R.J.V., and A.S. conceived and designed the project. C.L.C. and T.K.P. designed and synthesised RPI-GLYT2-82 compound. R.P.C.C. and A.S. established the protein expression and purification conditions. Expression, purification, cryo-EM sample preparations and data collections, processing, analysis and structure determination were carried out by R.P.C.C. with support from A.S. In vitro experiments were carried out by I.L. and R.P.C.C. with support from R.J.V. In vivo experiments were carried out by J.P-O, Z.J.F, A.E.T, and K.E.A with support from K.R.A., S.A.M., R.J.V., and M.M. The molecular dynamics studies were carried out by A.S.Q. and B.J.W-N, with support from M.L.O. R.P.C.C., R.J.V. and A.S. wrote the initial draft of the manuscript with contributions from all authors.
Corresponding authors
Ethics declarations
Competing interests
C.L.C., R.J.V. and T.K.P. have a provisional patent application (application number 104743-201) for compound RPI-GLYT2−82. The remaining authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Elena Bossi who co-reviewed with Tiziana Romanazzi; Ainara Claveras Cabezudo, Ximin Chi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Cantwell Chater, R.P., Peiser-Oliver, J., Pati, T.K. et al. A reversible allosteric inhibitor of GlyT2 for neuropathic pain without on-target side effects. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69616-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-69616-5
