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).