Accurate modeling and design of protein–ligand interactions have broad applications in cell biology, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein-homology-modeling, ligand-docking protein-design approach that reconstructs protein–ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced-fit modes of ligand binding. In structure modeling tests, we blindly predicted, with near-atomic accuracy, ligand conformations bound to G-protein-coupled receptors (GPCRs) that have rarely been identified using traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally uncharacterized GPCRs. We then applied this technique to design functional human dopamine receptors with novel ligand-binding selectivity. Most blindly predicted ligand-binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand-binding profile of membrane receptors that remain difficult to crystallize.
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Kolb, P. et al. Structure-based discovery of β2-adrenergic receptor ligands. Proc. Natl. Acad. Sci. USA 106, 6843–6848 (2009).
Deupi, X. & Kobilka, B.K. Energy landscapes as a tool to integrate GPCR structure, dynamics, and function. Physiology (Bethesda) 25, 293–303 (2010).
Katritch, V., Cherezov, V. & Stevens, R.C. Diversity and modularity of G protein-coupled receptor structures. Trends Pharmacol. Sci. 33, 17–27 (2012).
Manglik, A. et al. Structural insights into the dynamic process of β2-adrenergic receptor signaling. Cell 161, 1101–1111 (2015).
Nygaard, R. et al. The dynamic process of β(2)-adrenergic receptor activation. Cell 152, 532–542 (2013).
Kahsai, A.W. et al. Multiple ligand-specific conformations of the β2-adrenergic receptor. Nat. Chem. Biol. 7, 692–700 (2011).
Katritch, V., Cherezov, V. & Stevens, R.C. Structure-function of the G-protein-coupled receptor superfamily. Annu. Rev. Pharmacol. Toxicol. 53, 531–556 (2013).
Kim, T.H. et al. The role of ligands on the equilibria between functional states of a G-protein-coupled receptor. J. Am. Chem. Soc. 135, 9465–9474 (2013).
Stevens, R.C. et al. The GPCR Network: a large-scale collaboration to determine human GPCR structure and function. Nat. Rev. Drug Discov. 12, 25–34 (2013).
Pieper, U. et al. Coordinating the impact of structural genomics on the human α-helical transmembrane proteome. Nat. Struct. Mol. Biol. 20, 135–138 (2013).
Eswar, N., Eramian, D., Webb, B., Shen, M.Y. & Sali, A. Protein structure modeling with MODELLER. Methods Mol. Biol. 426, 145–159 (2008).
Kelm, S., Shi, J. & Deane, C.M. MEDELLER: homology-based coordinate generation for membrane proteins. Bioinformatics 26, 2833–2840 (2010).
Chen, K.Y., Sun, J., Salvo, J.S., Baker, D. & Barth, P. High-resolution modeling of transmembrane helical protein structures from distant homologues. PLoS Comput. Biol. 10, e1003636 (2014).
Yang, J. et al. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7–8 (2015).
Davis, I.W. & Baker, D. RosettaLigand docking with full ligand and receptor flexibility. J. Mol. Biol. 385, 381–392 (2009).
Repasky, M.P., Shelley, M. & Friesner, R.A. in Current Protocols in Bioinformatics. Ch. 8, Unit 8.12 (John Wiley and Sons, Inc., 2007).
Zhou, Z., Felts, A.K., Friesner, R.A. & Levy, R.M. Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets. J. Chem. Inf. Model. 47, 1599–1608 (2007).
Moustakas, D.T. et al. Development and validation of a modular, extensible docking program: DOCK 5. J. Comput. Aided Mol. Des. 20, 601–619 (2006).
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).
Huang, X.P. et al. Allosteric ligands for the pharmacologically dark receptors GPR68 and GPR65. Nature 527, 477–483 (2015).
Michino, M. et al. Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat. Rev. Drug Discov. 8, 455–463 (2009).
Kufareva, I., Rueda, M., Katritch, V., Stevens, R.C. & Abagyan, R. Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Structure 19, 1108–1126 (2011).
Kufareva, I., Katritch, V., Stevens, R.C. & Abagyan, R. Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure 22, 1120–1139 (2014).
Forrest, L.R., Tang, C.L. & Honig, B. On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins. Biophys. J. 91, 508–517 (2006).
Stamm, M. & Forrest, L.R. Structure alignment of membrane proteins: accuracy of available tools and a consensus strategy. Proteins 83, 1720–1732 (2015).
Qian, B. et al. High-resolution structure prediction and the crystallographic phase problem. Nature 450, 259–264 (2007).
Combs, S.A. et al. Small-molecule ligand docking into comparative models with Rosetta. Nat. Protoc. 8, 1277–1298 (2013).
Fischer, M., Coleman, R.G., Fraser, J.S. & Shoichet, B.K. Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat. Chem. 6, 575–583 (2014).
Spyrakis, F., BidonChanal, A., Barril, X. & Luque, F.J. Protein flexibility and ligand recognition: challenges for molecular modeling. Curr. Top. Med. Chem. 11, 192–210 (2011).
Cavasotto, C.N. & Abagyan, R.A. Protein flexibility in ligand docking and virtual screening to protein kinases. J. Mol. Biol. 337, 209–225 (2004).
Conklin, B.R. et al. Engineering GPCR signaling pathways with RASSLs. Nat. Methods 5, 673–678 (2008).
Roth, B.L. DREADDs for neuroscientists. Neuron 89, 683–694 (2016).
Nguyen, E.D., Norn, C., Frimurer, T.M. & Meiler, J. Assessment and challenges of ligand docking into comparative models of G-protein-coupled receptors. PLoS One 8, e67302 (2013).
Rodriguez, G.J., Yao, R., Lichtarge, O. & Wensel, T.G. Evolution-guided discovery and recoding of allosteric pathway specificity determinants in psychoactive bioamine receptors. Proc. Natl. Acad. Sci. USA 107, 7787–7792 (2010).
Liu, T., Lin, Y., Wen, X., Jorissen, R.N. & Gilson, M.K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35, D198–D201 (2007).
Wang, C. et al. Structural basis for molecular recognition at serotonin receptors. Science 340, 610–614 (2013).
Barth, P., Schonbrun, J. & Baker, D. Toward high-resolution prediction and design of transmembrane helical protein structures. Proc. Natl. Acad. Sci. USA 104, 15682–15687 (2007).
Lemmon, G. & Meiler, J. Towards ligand docking including explicit interface water molecules. PLoS One 8, e67536 (2013).
Söding, J., Biegert, A. & Lupas, A.N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33, W244–W248 (2005).
Isberg, V. et al. GPCRdb: an information system for G-protein-coupled receptors. Nucleic Acids Res. 44, D1, D356–D364 (2016).
Isberg, V. et al. GPCRDB: an information system for G-protein-coupled receptors. Nucleic Acids Res. 42, D422–D425 (2014).
Yarov-Yarovoy, V., Schonbrun, J. & Baker, D. Multipass membrane protein structure prediction using Rosetta. Proteins 62, 1010–1025 (2006).
Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of data clusters via the Gap statistic. J. R. Statist. Soc. B 63, 411–423 (2001).
Hawkins, P.C.D., Skillman, A.G., Warren, G.L., Ellingson, B.A. & Stahl, M.T. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J. Chem. Inf. Model. 50, 572–584 (2010).
Venkatakrishnan, A.J. et al. Molecular signatures of G-protein-coupled receptors. Nature 494, 185–194 (2013).
Tinberg, C.E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212–216 (2013).
Chen, K.Y., Zhou, F., Fryszczyn, B.G. & Barth, P. Naturally evolved G-protein-coupled receptors adopt metastable conformations. Proc. Natl. Acad. Sci. USA 109, 13284–13289 (2012).
Luo, J., Zhu, Y., Zhu, M.X. & Hu, H. Cell-based calcium assay for medium to high throughput screening of TRP channel functions using FlexStation 3. J. Vis. Exp. http://dx.doi.org/10.3791/3149 (2011).
Sung, Y.M., Wilkins, A.D., Rodriguez, G.J., Wensel, T.G. & Lichtarge, O. Intramolecular allosteric communication in dopamine D2 receptor revealed by evolutionary amino acid covariation. Proc. Natl. Acad. Sci. USA 113, 3539–3544 (2016).
We thank the members of the Barth lab for insightful discussions during this study and critical comments on the manuscript. This work was supported by a grant from the National Institute of Health (1R01GM097207) and by a supercomputer allocation from XSEDE (MCB120101) to P.B. and a training fellowship from the NIH NIGMS T32GM008280 to M.Y. and K.-Y.M.C.
The authors declare no competing financial interests.
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Feng, X., Ambia, J., Chen, KY. et al. Computational design of ligand-binding membrane receptors with high selectivity. Nat Chem Biol 13, 715–723 (2017). https://doi.org/10.1038/nchembio.2371
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