Abstract
The recognition of ligands by transmembrane proteins is essential for the exchange of materials, energy and information across biological membranes. Progress has been made in the de novo design of transmembrane proteins1,2,3,4,5,6, as well as in designing water-soluble proteins to bind small molecules7,8,9,10,11,12, but de novo design of transmembrane proteins that tightly and specifically bind to small molecules remains an outstanding challenge13. Here we present the accurate design of ligand-binding transmembrane proteins by integrating deep learning and energy-based methods. We designed pre-organized ligand-binding pockets in high-quality four-helix backbones for a fluorogenic ligand, and generated a transmembrane span using gradient-guided hallucination. The designer transmembrane proteins specifically activated fluorescence of the target fluorophore with mid-nanomolar affinity, exhibiting higher brightness and quantum yield compared to those of enhanced green fluorescent protein. These proteins were highly active in the membrane fraction of live bacterial and eukaryotic cells following expression. The crystal and cryogenic electron microscopy structures of the designer protein–ligand complexes were very close to the structures of the design models. We showed that the interactions between ligands and transmembrane proteins within the membrane can be accurately designed. Our work paves the way for the creation of new functional transmembrane proteins, with a wide range of applications including imaging, ligand sensing and membrane transport.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The crystal structure models have been deposited in the Protein Data Bank (accession codes 8W6F and 8W6E). The cryo-EM maps have been deposited in the Electron Microscopy Data Bank under the accession code EMD-60929, and the associated model has been deposited in the Research Collaboratory for Structural Bioinformatics Protein Data Bank under the accession code 9IVK. All data are available in the paper or in the Supplementary Information. Source data are provided with this paper.
Code availability
Design models and all relevant scripts are available via Zenodo at https://doi.org/10.5281/zenodo.14196857 (ref. 63). Rosetta Modeling Suite 2019.47.61047 (https://www.rosettacommons.org/) is available to academic and non-commercial users for free. The source code for RIF docking is available via GitHub at https://github.com/rifdock/rifdock. ColabFold is available via GitHub at https://github.com/sokrypton/ColabFold. ColabDesign is available via GitHub at https://github.com/sokrypton/ColabDesign. AF2 weights can be downloaded from https://storage.googleapis.com/alphafold/alphafold_params_2022-12-06.tar. Source data are provided with this paper.
References
Joh, N. H. et al. De novo design of a transmembrane Zn2+-transporting four-helix bundle. Science 346, 1520–1524 (2014).
Lu, P. et al. Accurate computational design of multipass transmembrane proteins. Science 359, 1042–1046 (2018).
Xu, C. et al. Computational design of transmembrane pores. Nature 585, 129–134 (2020).
Scott, A. J. et al. Constructing ion channels from water-soluble alpha-helical barrels. Nat. Chem. 13, 643–650 (2021).
Vorobieva, A. A. et al. De novo design of transmembrane β barrels. Science 371, eabc8182 (2021).
Berhanu, S. et al. Sculpting conducting nanopore size and shape through de novo protein design. Science 385, 282–288 (2024).
Tinberg, C. E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).
Polizzi, N. F. et al. De novo design of a hyperstable non-natural protein–ligand complex with sub-Å accuracy. Nat. Chem. 9, 1157–1164 (2017).
Dou, J. et al. De novo design of a fluorescence-activating beta-barrel. Nature 561, 485–491 (2018).
Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule binding proteins. Science 369, 1227–1233 (2020).
Lu, L. et al. De novo design of drug-binding proteins with predictable binding energy and specificity. Science 384, 106–112 (2024).
An, L. et al. Binding and sensing diverse small molecules using shape-complementary pseudocycles. Science 385, 276–282 (2024).
Zhu, J. & Lu, P. Computational design of transmembrane proteins. Curr. Opin. Struct. Biol. 74, 102381 (2022).
Cordova, J. M., Noack, P. L., Hilcove, S. A., Lear, J. D. & Ghirlanda, G. Design of a functional membrane protein by engineering a heme-binding site in glycophorin A. J. Am. Chem. Soc. 129, 512–518 (2007).
Korendovych, I. V. et al. De novo design and molecular assembly of a transmembrane diporphyrin-binding protein complex. J. Am. Chem. Soc. 132, 15516–15518 (2010).
Hardy, B. J. et al. Cellular production of a de novo membrane cytochrome. Proc. Natl Acad. Sci. USA 120, e2300137120 (2023).
Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Wang, J. et al. Scaffolding protein functional sites using deep learning. Science 377, 387–394 (2022).
Anishchenko, I. et al. De novo protein design by deep network hallucination. Nature 600, 547–552 (2021).
Dauparas, J. et al. Robust deep learning–based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Wicky, B. I. M. et al. Hallucinating symmetric protein assemblies. Science 378, 56–61 (2022).
An, L. et al. Hallucination of closed repeat proteins containing central pockets. Nat. Struct. Mol. Biol. 30, 1755–1760 (2023).
Yeh, A. H.-W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).
Fairman, J. W., Noinaj, N. & Buchanan, S. K. The structural biology of β-barrel membrane proteins: a summary of recent reports. Curr. Opin. Struct. Biol. 21, 523–531 (2011).
Chen, X. et al. Visualizing RNA dynamics in live cells with bright and stable fluorescent RNAs. Nat. Biotechnol. 37, 1287–1293 (2019).
Huang, K. et al. Structure-based investigation of fluorogenic Pepper aptamer. Nat. Chem. Biol. 17, 1289–1295 (2021).
Grigoryan, G. & DeGrado, W. F. Probing designability via a generalized model of helical bundle geometry. J. Mol. Biol. 405, 1079–1100 (2011).
Thomson, A. R. et al. Computational design of water-soluble alpha-helical barrels. Science 346, 485–488 (2014).
Huang, P. S. et al. High thermodynamic stability of parametrically designed helical bundles. Science 346, 481–485 (2014).
Yang, J. et al. Improved protein structure prediction using predicted interresidue orientations. Proc. Natl Acad. Sci. USA 117, 1496–1503 (2020).
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J. Chem. Inf. Model. 61, 3891–3898 (2021).
Thomas, F. et al. De novo-designed α-helical barrels as receptors for small molecules. ACS Synth. Biol. 7, 1808–1816 (2018).
Sarkisyan, K. S. et al. Green fluorescent protein with anionic tryptophan-based chromophore and long fluorescence lifetime. Biophys. J. 109, 380–389 (2015).
Frank, C. et al. Scalable protein design using optimization in a relaxed sequence space. Science 386, 439–445 (2024).
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
Yan, N. Structural biology of the major facilitator superfamily transporters. Annu. Rev. Biophys. 44, 257–283 (2015).
Chow, B. Y. et al. High-performance genetically targetable optical neural silencing by light-driven proton pumps. Nature 463, 98–102 (2010).
McIsaac, R. S. et al. Directed evolution of a far-red fluorescent rhodopsin. Proc. Natl Acad. Sci. USA 111, 13034–13039 (2014).
Kralj, J. M., Douglass, A. D., Hochbaum, D. R., Maclaurin, D. & Cohen, A. E. Optical recording of action potentials in mammalian neurons using a microbial rhodopsin. Nat. Methods 9, 90–95 (2011).
Abdelfattah, A. S. et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science 365, 699–704 (2019).
Liu, S. et al. A far-red hybrid voltage indicator enabled by bioorthogonal engineering of rhodopsin on live neurons. Nat. Chem. 13, 472–479 (2021).
Broser, M. et al. NeoR, a near-infrared absorbing rhodopsin. Nat. Commun. 11, 5682 (2020).
Hegedus, T., Geisler, M., Lukacs, G. L. & Farkas, B. Ins and outs of AlphaFold2 transmembrane protein structure predictions. Cell. Mol. Life Sci. 79, 73 (2022).
Kralj, J. M., Hochbaum, D. R., Douglass, A. D. & Cohen, A. E. Electrical spiking in Escherichia coli probed with a fluorescent voltage-indicating protein. Science 333, 345–348 (2011).
Kwon, J. et al. Bright ligand-activatable fluorescent protein for high-quality multicolor live-cell super-resolution microscopy. Nat. Commun. 11, 273 (2020).
The PyMOL Molecular Graphics System v1.8 (Schrodinger, 2015).
Bada Juarez, J. F. et al. Structures of the archaerhodopsin-3 transporter reveal that disordering of internal water networks underpins receptor sensitization. Nat. Commun. 12, 629 (2021).
Dang, B. et al. De novo design of covalently constrained mesosize protein scaffolds with unique tertiary structures. Proc. Natl Acad. Sci. USA 114, 10852–10857 (2017).
Hanwell, M. D. et al. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform. 4, 17 (2012).
O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminform. 3, 33 (2011).
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
Baek, M. et al. Efficient and accurate prediction of protein structure using RoseTTAFold2. Preprint at bioRxiv https://doi.org/10.1101/2023.05.24.542179 (2023).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997).
Storoni, L. C., McCoy, A. J. & Read, R. J. Likelihood-enhanced fast rotation functions. Acta Crystallogr. D 60, 432–438 (2004).
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).
Davis, I. W. et al. MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res. 35, W375–W383 (2007).
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).
Zhu, J. jz3216/tmFAP: initial release. Zenodo https://doi.org/10.5281/zenodo.14196857 (2024).
Goverde, C. A. et al. Computational design of soluble and functional membrane protein analogues. Nature 631, 449–458 (2024).
Acknowledgements
We acknowledge S. Fan and J. Wang for assistance in structure determination; J. Yu and Y. Zhang for providing the SYNJ2BP plasmid; K. D. Piatkevich for providing the modified mTagBFP2 plasmid; the cryo-EM facility, the flow cytometry facility and the microscopy facility at Westlake University for technical support; the Westlake University HPC Center for computation assistance; the Protein Characterization and Crystallography Facility of Westlake University for help in sample analysis; and Z. Chen from Instrumentation and Service Center for Molecular Sciences at Westlake University for the assistance in fluorescence quantum yield measurement. This work was financed by the Ministry of Science and Technology of the People’s Republic of China (projects 2020YFA0909200 and 2022YFA1303700), the Zhejiang Provincial Natural Science Foundation of China (grant number LR23C050001), the Pioneer and Leading Goose R&D programmes of Zhejiang (grant numbers 2024SSYS0031 and 2024SSYS0029), the National Natural Science Foundation of China (projects 32430063 and 22137005) and a research grant from Westlake University.
Author information
Authors and Affiliations
Contributions
P.L. conceived and supervised the project; J.Z., M.L. and K.S. contributed equally to this work; J.Z. developed the computational method and designed the wFAPs and tmFAPs. J.Z. and M.L. performed the biochemical experiments and imaging. J.Z., M.L. and Y.W. performed directed evolution. M.L. prepared protein crystals and solved the crystal structure. L.Z. and R.G. synthesized HBC599 supervised by Q.H. M.L. prepared cryo-EM samples for data acquisition. K.S., J.S., D.M. and G.H. solved the EM structure of tmFAP. P.L., J.Z., M.L. and K.S. wrote the original draft and all authors participated in manuscript revision.
Corresponding author
Ethics declarations
Competing interests
J.Z., M.L. and P.L. are inventors on a provisional patent application (Application No. 202410300103.3) submitted by Westlake University for the functions of the wFAPs and tmFAPs described in this study. The remaining authors declare no competing interests.
Peer review
Peer review information
Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 De novo design of wFAPs.
a, Detailed computational design steps of wFAPs. b, All five AF2 prediction models (white) aligned to wFAP0 design model (blue). Both the overall structure (left) and Cα coordinates of pocket residues (right) are highly consistent between the design and predictions. c, AF2 metrics of wFAPs designs, including the early designs and the revised designs. Overall pLDDT and pocket Cα RMSD to the design model are plotted for each experimentally tested wFAPs. Calculation is performed using all five AF2 models, and the averaged results are plotted for each design. wFAPs designed using the revised protocol (denoted by yellow dots for wFAP0 and wFAP1.3, the rest in blue) are significantly more consistent to AF2 prediction in the pocket region than those designed using the initial protocol (denoted by grey dots). d, HBC599 docked to the protein part of wFAP0 design model (white ribbon) by Autodock Vina. The overall binding mode in the top docking output (HBC599 in green) was highly consistent to that in the design model (white). e, Chemical structures of the HBC fluorophores29 used in this study.
Extended Data Fig. 2 Purification and characterization of wFAPs.
a, Fluorescence emission spectrum of 1 µM HBC599 with or without 10 µM of the best early design, excited at 495 nm. The design weakly activated fluorescence of HBC599 over the buffer (Tris-buffered saline (TBS, 20 mM Tris, pH 7.4, 150 mM NaCl)). b, Three designs demonstrated fluorescence activation for HBC599. Relative fluorescence intensity was measured at 1 µM HBC599 with 1 µM of each designer protein. c, Normalized UV-vis absorbance spectra of wFAP1.1-HBC599 complex (orange) and free HBC599 (gray). d, Representative gel filtration chromatography and SDS-PAGE of wFAP0, wFAP1.1, wFAP1.2 and wFAP1.3. All four proteins eluted as monomers at similar volume on size-exclusion chromatography (SEC). For gel source data, see Supplementary Fig. 1. At least two independent experiments were performed, yielding consistent outcomes. e, Fluorescence-temperature curves of the wFAP1.1-HBC599 complex (red) and free HBC599 (black) in the temperature-induced dissociation experiment. Fluorescence gradually diminished to the background level as the temperature rose to 95 °C, and recovered to the original level when the sample cooled down. The dissociation midpoint of wFAP1.1 was approximately 60 °C. Data from three independent samples are presented as mean ± SD. f, Far-ultraviolet CD spectra of apo wFAP1.1 at 20 °C (grey line), 50 °C (red line), 95 °C (blue line) and cooled back to 20 °C (green line). g, Ligand specificity of wFAPs. 1 µM protein or buffer (TBS, pH 7.4) was mixed with 1 µM HBC for each combination, and fluorescence reading at the excitation and emission maxima for each condition was measured. Readings were normalized against that of the best protein-ligand combination.
Extended Data Fig. 3 Directed evolution of FAPs.
a, Fluorescence-activated cell sorting (FACS) profile for each round of wFAP0 directed evolution. We constructed a combinatorial library with site-saturation mutation for five ligand-surrounding residues (in total 20^5 = 3,200,000 combinations). The selection comprised three rounds, with 5 μM HBC599 used for round 1-2, and 0.5 μM for round 3. mTagBFP2 was fused to the C-terminus of wFAP0 for monitoring of protein expression level. Upon completion of the final round of sorting, the sequences of individual clones were sequenced, leading to identification of two enriched variants, wFAP1.1 and wFAP1.2 (sequence at mutation sites listed at the bottom). For FACS gating strategies, see Supplementary Fig. 2. b, AF2 predictions of wFAP variants (white) aligned to wFAP0 design model (blue). Backbone structure of the ligand binding pocket remained unchanged after directed evolution. Indices of the mutated residues are labeled. c, FACS profile for each round of tmFAP1 directed evolution. We constructed a combinatorial library with mutations for seven ligand-interacting residues (in total ~1 million combinations). The selection comprised three rounds, with 1 μM HBC599 used for round 1-2, and 0.2 μM for round 3. mTagBFP2 was fused to the C-terminal of tmFAP for monitoring of protein expression level, and MBP fused to N-terminal for improving expression level. Upon completion of the final round of sorting, the sequences of individual clones were sequenced. Sequence at mutation sites listed at the bottom. For FACS gating strategies, see Supplementary Fig. 2. d, AF2 predictions of tmFAP variants (white) aligned to wFAP0 design model (blue). Indices of the mutated residues are labeled.
Extended Data Fig. 4 Sequences of the designer FAPs.
(a-b) Sequence alignment of wFAPs (a) and tmFAPs (b). Residues mutated through directed evolution are colored red. Unchanged residues are denoted by black dots. Residues changed during transmembrane span design (from wFAP1.1/1.2 to tmFAP1) are highlighted (yellow). Rational designed mutations are colored blue. (c) Sequence genesis of the designer FAPs. Arrows with solid lines denote sequence generation by design, while dashed lines denote sequence generation by directed evolution. Designs with the same color code (orange or blue) share the same set of residues in the ligand binding pocket.
Extended Data Fig. 5 Design of the transmembrane span using deep networks hallucination.
a, De novo transmembrane proteins designed by TM-span hallucination in addition to tmFAPs. Surface redesign was performed for five water-soluble precursor proteins in various topologies, including an alpha-helical homodimer (tm-C2)2, a beta-barrel (tm-mFAP0)5, a soluble analogue of Claudin (tm-CLF4) and two soluble analogues of GPCR (tm-GLF18, tm-GLF32)64. Left panel, the designs generated by TM-span hallucination. For designed sequences, see Supplementary Table 2. Model generated by RF2 validation (blue) was aligned to the water-soluble input (white). One representative design was shown for each target. Right panel, pLDDT and core residue Cα RMSD to the water-soluble input are plotted. b, Hallucination trajectories of the 6 tmFAP designs selected for experimental characterization, with Cα RMSD < 0.8 Å to the input wFAP template and pLDDT > 85. Loss sum (blue), pocket Cα RMSD (orange), and pLDDT (green) at each step are plotted. The vertical lines indicate the start of optimizing of one-hot encoded sequence at step 300. c, Prediction of all five AF2 models for tmFAP1 (white) aligned to the hallucination template, wFAP0 (blue). All ligand-interacting residues are highly consistent between the design template and the predicted models. d, Hallucination generated transmembrane span (cyan) has a different amino acid composition compared to that designed by the previous Rosetta protocol (orange). e, AF2 metrics of tmFAPs. Overall pLDDT and pocket Cα RMSD to the design model are plotted for each experimentally tested tmFAPs. Calculation is performed using all five AF2 models (for Rosetta designed sequences) or using the validation model (for hallucination generated sequences), and the averaged results are plotted for each design. tmFAPs designed by hallucination (denoted by colored dots—yellow for tmFAP1 and tmFAP2) are significantly more consistent to AF2 prediction in the pocket region than those designed by Rosetta (denoted by grey dots). f, RF2 and ESMFold were used as additional evaluation methods for tmFAPs, of which the results are overall consistent with AF2. Yellow dots represent tmFAP1 and tmFAP2.
Extended Data Fig. 6 Purification and characterization of tmFAPs.
(a-b), Both tmFAP1 and tmFAP2 were active for HBC599 when expressed in E. coli BW27783 cells (a, flow cytometry profiles) or purified in detergent solution (TBS, pH 7.4, 1.1% OG) (b). (c-h), Representative gel filtration chromatography and SDS-PAGE of tmFAP1, tmFAP2, tmFAP3, tmFAP1.1, tmFAP1.2, tmFAP1.3, and tmFAP3. All designs were fused with MBP at the N-terminus to improve expression level, and exhibited a similar elution volume on size-exclusion chromatography (SEC). However, it is worth noting that the expression level of tmFAP2 in E. coli cells was much lower than that of tmFAP1 variants. For gel source data, see Supplementary Fig. 1. At least two independent experiments were performed, yielding consistent outcomes. i, Fluorescence titration of tmFAP1.1-BRIL. Purified protein in detergent solution was titrated against 50 nM HBC599. Data from three independent experiments are presented as mean ± SD. j, Ligand specificity of tmFAPs. 1 µM protein or buffer (TBS, pH 7.4, 1.1% OG) was mixed with 1 µM HBC for each combination, and fluorescence reading at the excitation and emission maxima for each condition was measured. Readings were normalized against that of the best protein-ligand combination. (k-m), Fluorescence imaging of tmFAPs in eukaryotic cells. Line scans (in Fig. 5) across the membranes show substantial increase in fluorescence across the plasma membranes for tmFAP3 expressed in CHO cells (top panel) and Xenopus oocytes (bottom panel) in the presence of 20 nM HBC599. The BFP signal correlates with HBC599 signal very well. l, CHO cells expressing tmFAP1.1 in the presence of 20 nM HBC599. Scale bar is 10 μm. m, Control Xenopus oocytes cells injected with water in the presence of 20 nM HBC599. Scale bar is 100 μm. At least three independent experiments were performed (l-m), yielding consistent outcomes.
Extended Data Fig. 7 Crystal structures of wFAP1.1.
a, Apo wFAP1.1 crystal structure comprises two molecules of wFAP1.1 in one asymmetric unit (green and blue). b, The conformations of the two wFAP1.1 biological units exhibit a high degree of similarity, as demonstrated by a Cα RMSD of 0.5 Å. (c-d), The ligand binding pocket, situated at the core of the helical bundle in the crystal structure (c), closely matches that of the design model (d). e, The residues defining the pocket in the crystal structure (green) align well with those in the design model (grey). f, Crystals of the wFAP1.1 in complex with HBC599 (scale bar: 100 μm). g, each asymmetric unit consists of two highly similar wFAP1.1 molecules. A single HBC599 molecule in its planar conformation is found nestled in the central pocket of each wFAP1.1 molecule, confirmed by unambiguous electron density as depicted in the omit map (contoured at 2.0σ). h, The crystal structures of wFAP1.1 in the ligand-bound form (rainbow) and the apo form (white) are nearly identical. i, Tyr 100 adopts distinct rotamers in the complex structure and the apo structure. The red sphere denotes the water molecule bridging HBC599 and Tyr 100 (omit map contoured at 2.0σ).
Extended Data Fig. 8 Single-particle cryo-EM image processing and analysis.
a, Representative micrograph of tmFAP1.1-BRIL complexed with HBC599. Remaining micrographs produced consistent results. Scale bar is 90 nm. b, Representative 2D class averages. Remaining averages produced consistent results. Scale bars are 90 Å. (c-d), Flowchart for EM data processing (refer to Materials and Methods for details). e, Local resolution of the final cryo-EM map in Angstrom. f, Cryo-EM map of the surrounding detergents (grey). g, The cryo-EM density for the four helices of tmFAP1.1 (map contoured at 3.0σ). h, Angular distribution curve for the final refinement. i, Gold-standard Fourier Shell Correlation (GSFSC) curve for the final refinement.
Extended Data Fig. 9 Ligand binding in wFAP1.1 and tmFAP1.1 structures.
a, HBC599 docked to the crystal structure of wFAP1.1 (white ribbon) by Autodock Vina. The top docking output is shown in green, highly consistent to that in the crystal structure (white). b, Potential binding mode for HBC620 to wFAP1.1. HBC620 was docked to the protein part of the wFAP1.1 crystal structure (white). The binding mode of HBC620 in the top output (green) closely resembled that of HBC599 (white). (c-d), Mutations introduced in the binding pockets of wFAP1.1 (c) and tmFAP1.1 (d) abolished or reduced the fluorescence activation, in agreement with the structures of wFAP1.1 and tmFAP1.1. E. coli cells expressing the mutants were analyzed by cytometry using the same method as directed evolution experiments. All samples were incubated with 200 nM HBC599 for the same amount of time. N = 3000 different cells of each mutant were used for analysis. The center dot denotes the median value. The upper and lower bounds of box denote the 25th and 75th of the data. Whiskers extended the range to the 10th to 90th percentile. (e-f), Correlation between pLDDT in structure prediction models and B-factor in experimental structures. Yellow dots denote pocket residues, while blue dots denote the remaining residues. The Pearson correlation coefficient (r) and p-value (p) are presented for all residues and pocket residues, respectively (Methods). Data plotted for e, wFAP1.1. f, transmembrane region of tmFAP-BRIL.
Supplementary information
Supplementary Information
Supplementary Methods, Figs. 1 and 2 and Tables 1 and 2. The Supplementary Methods describes the synthetic route of HBC599. Supplementary Fig. 1 contains the full scan of the gels run in this paper. Supplementary Fig. 2 describes the gating strategy used for the flow cytometry experiments. Supplementary Table 1 lists the fluorescence characteristics of the designer proteins in this article. Supplementary Table 2 lists the transmembrane protein sequences designed by our method.
Source data
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhu, J., Liang, M., Sun, K. et al. De novo design of transmembrane fluorescence-activating proteins. Nature 640, 249–257 (2025). https://doi.org/10.1038/s41586-025-08598-8
Received:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1038/s41586-025-08598-8
This article is cited by
-
Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies
World Journal of Microbiology and Biotechnology (2025)
