Molecular Dynamics (MD) Simulation of GPR87-LPA Binding: Therapeutic Implications for Targeted Cancer Treatment

  • Авторы: Rani M.1, Sharma A.2, Nischal A.3, Khattri S.4, Sahoo G.5, Singh R.6
  • Учреждения:
    1. Department of Bioinformatics, National Institute for Plant Biotechnology, Indian Council of Agricultural Research
    2. Department of Physics, Faculty of Engineering, Teerthanker Mahaveer University
    3. Department of Pharmacology and Therapeutics, King George's Medical University
    4. Rajendra Memorial Research Institute of Medical Sciences, Indian Council of Medical Research
    5. Biomedical Informatics Centre, Institute of Medical Sciences, Indian Council of Medical Research
    6. Department of Pharmaceutical Chemistry, Shivalik College of Pharmacy
  • Выпуск: Том 25, № 17 (2025)
  • Страницы: 1342-1358
  • Раздел: Chemistry
  • URL: https://filvestnik.nvsu.ru/1871-5206/article/view/694457
  • DOI: https://doi.org/10.2174/0118715206374428250403103159
  • ID: 694457

Цитировать

Полный текст

Аннотация

Background: GPR87 is an orphan G-protein-coupled receptor (GPCR) that represents a potential molecular target for developing novel drugs aimed at treating squamous cell carcinomas (SCCs) or adenocarcinomas of the lungs and bladder.

Objective: The present study aims to identify potential LPA analogues as inhibitors of the GPR87 protein through computational screening. To achieve this, the human GPR87 structure was modeled using template-based tools (Phyre2 and SWISS-MODEL), iterative threading (I-TASSER), and neural network-based de novo prediction (AlphaFold2). The modeled structures were then validated by assessing their quality against template structures using Verify-3D, ProSA, and ERRAT servers.

Methods: We conducted a comprehensive structural and functional analysis of the target protein using various computational tools. Several computational techniques were employed to explore the structural and functional characteristics of the target, with LPA selected as the initial pharmacological candidate. A library of 2,605 LPA analogues was screened against orphan GPR87 through in-silico docking analysis to identify higher-affinity and more selective potential drugs.

Results: Molecular dynamics (MD) simulations were performed to track structural changes and convergence during the simulations. Key metrics, including the root mean square fluctuation (RMSF) of Cα-atoms, radius of gyration, and RMSD of backbone atoms, were calculated for both the apo-form and the LPA-GPR87 complex structures. These studies on structure-based drug targeting could pave the way for the development of specific inhibitors for the treatment of squamous cell carcinomas.

Conclusion: These findings may contribute to the design and development of new therapeutic compounds targeting GPR87 for the treatment of SCC.

Об авторах

Mukta Rani

Department of Bioinformatics, National Institute for Plant Biotechnology, Indian Council of Agricultural Research

Автор, ответственный за переписку.
Email: info@benthamscience.net

Amit Sharma

Department of Physics, Faculty of Engineering, Teerthanker Mahaveer University

Автор, ответственный за переписку.
Email: info@benthamscience.net

Anuradha Nischal

Department of Pharmacology and Therapeutics, King George's Medical University

Email: info@benthamscience.net

Sanjay Khattri

Rajendra Memorial Research Institute of Medical Sciences, Indian Council of Medical Research

Email: info@benthamscience.net

Ganesh Sahoo

Biomedical Informatics Centre, Institute of Medical Sciences, Indian Council of Medical Research

Email: info@benthamscience.net

Rajesh Singh

Department of Pharmaceutical Chemistry, Shivalik College of Pharmacy

Автор, ответственный за переписку.
Email: info@benthamscience.net

Список литературы

  1. Civelli, O. GPCR deorphanizations: The novel, the known and the unexpected transmitters. Trends Pharmacol. Sci., 2005, 26(1), 15-19. doi: 10.1016/j.tips.2004.11.005 PMID: 15629200
  2. Costanzi, S. Homology modeling of class a G protein-coupled receptors. Methods Mol. Biol., 2011, 857, 259-279. doi: 10.1007/978-1-61779-588-6_11 PMID: 22323225
  3. Bräuner-Osborne, H.; Wellendorph, P.; Jensen, A. Structure, pharmacology and therapeutic prospects of family C G-protein coupled receptors. Curr. Drug Targets, 2007, 8(1), 169-184. doi: 10.2174/138945007779315614 PMID: 17266540
  4. Dorsam, R.T.; Gutkind, J.S. G-protein-coupled receptors and cancer. Nat. Rev. Cancer, 2007, 7(2), 79-94. doi: 10.1038/nrc2069 PMID: 17251915
  5. Shore, D.M.; Reggio, P.H. The therapeutic potential of orphan GPCRs, GPR35 and GPR55. Front. Pharmacol., 2015, 6, 69. doi: 10.3389/fphar.2015.00069 PMID: 25926795
  6. Tabata, K.; Baba, K.; Shiraishi, A.; Ito, M.; Fujita, N. The orphan GPCR GPR87 was deorphanized and shown to be a lysophosphatidic acid receptor. Biochem. Biophys. Res. Commun., 2007, 363(3), 861-866. doi: 10.1016/j.bbrc.2007.09.063 PMID: 17905198
  7. Chung, S.; Funakoshi, T.; Civelli, O. Orphan GPCR research. Br. J. Pharmacol., 2008, 153, S339-S346. doi: 10.1038/sj.bjp.0707606
  8. Audet, M.; Bouvier, M. Restructuring G-protein- coupled receptor activation. Cell, 2012, 151(1), 14-23. doi: 10.1016/j.cell.2012.09.003 PMID: 23021212
  9. Sadiq, S.K.; Guixa-Gonzalez, R.; Dainese, E.; Pastor, M.; De Fabritiis, G.; Selent, J. Molecular modeling and simulation of membrane lipid-mediated effects on GPCRs. Curr. Med. Chem., 2012, 20(1), 22-38. doi: 10.2174/0929867311320010004 PMID: 23151000
  10. Nonaka, Y.; Hiramoto, T.; Fujita, N. Identification of endogenous surrogate ligands for human P2Y12 receptors by in silico and in vitro methods. Biochem. Biophys. Res. Commun., 2005, 337(1), 281-288. doi: 10.1016/j.bbrc.2005.09.052 PMID: 16185654
  11. Wittenberger, T.; Schaller, H.C.; Hellebrand, S. An expressed sequence tag (EST) data mining strategy succeeding in the discovery of new G-protein coupled receptors. J. Mol. Biol., 2001, 307(3), 799-813. doi: 10.1006/jmbi.2001.4520 PMID: 11273702
  12. Singh, R.K. Key Heterocyclic Cores for Smart Anticancer Drug–Design Part I; Bentham Science Publishers, 2022. doi: 10.2174/97898150400741220101
  13. Gugger, M.; White, R.; Song, S.; Waser, B.; Cescato, R.; Rivière, P.; Reubi, J.C. GPR87 is an overexpressed G-protein coupled receptor in squamous cell carcinoma of the lung. Dis. Markers, 2008, 24(1), 41-50. doi: 10.1155/2008/857474 PMID: 18057535
  14. Zhang, Y.; Qian, Y.; Lu, W.; Chen, X. The G protein-coupled receptor 87 is necessary for p53-dependent cell survival in response to genotoxic stress. Cancer Res., 2009, 69(15), 6049. doi: 10.1158/0008-5472.CAN-09-0621
  15. Yan, M.; Li, H.; Zhu, M.; Zhao, F.; Zhang, L.; Chen, T. G protein-coupled receptor 87 (GPR87) promotes the growth and metastasis of CD133+ cancer stem-like cells in hepatocellular carcinoma. PLoS ONE, 2013, 8(4), e61056.
  16. Kerley-Hamilton, J.S.; Pike, A.M.; Li, N.; DiRenzo, J.; Spinella, M.J.A. p53-dominant transcriptional response to cisplatin in testicular germ cell tumor-derived human embyronal carcinoma. Oncogene, 2005, 24(40), 6090-6100. doi: 10.1038/sj.onc.1208755 PMID: 15940259
  17. Andradas, C.; Caffarel, M.M.; Pérez-Gómez, E.; Salazar, M.; Lorente, M.; Velasco, G.; Guzmán, M.; Sánchez, C. The orphan G protein-coupled receptor GPR55 promotes cancer cell proliferation via ERK. Oncogene, 2011, 30(2), 245-252. doi: 10.1038/onc.2010.402 PMID: 20818416
  18. Sud, N.; Sharma, R.; Ray, R.; Chattopadhyay, T.K.; Ralhan, R. Differential expression of G-protein coupled receptor 56 in human esophageal squamous cell carcinoma. Cancer Lett., 2006, 233(2), 265-270. doi: 10.1016/j.canlet.2005.03.018 PMID: 15916848
  19. Cui, X.; Shi, E.; Li, J.; Li, Y.; Qiao, Z.; Wang, Z.; Liu, M.; Tang, W.; Sun, Y.; Zhang, Y.; Xie, Y.; Zhen, J.; Wang, X.; Yi, F. GPR87 promotes renal tubulointerstitial fibrosis by accelerating glycolysis and mitochondrial injury. Free Radic. Biol. Med., 2022, 189, 58-70. doi: 10.1016/j.freeradbiomed.2022.07.004 PMID: 35843477
  20. Ochiai, S.; Furuta, D.; Sugita, K.; Taniura, H.; Fujita, N. GPR87 mediates lysophosphatidic acid-induced colony dispersal in A431 cells. Eur. J. Pharmacol., 2013, 715(1-3), 15-20. doi: 10.1016/j.ejphar.2013.06.029 PMID: 23831392
  21. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol., 1990, 215(3), 403-410. doi: 10.1016/S0022-2836(05)80360-2 PMID: 2231712
  22. Li, B.; Han, S.; Wang, M.; Yu, Y.; Ma, L.; Chu, X.; Tan, Q.; Zhao, Q.; Wu, B. Structural insights into signal transduction of the purinergic receptors P2Y1R and P2Y12R. Protein Cell, 2022, 14(5), pwac025. doi: 10.1093/procel/pwac025 PMID: 37155313
  23. Larkin, M.A.; Blackshields, G.; Brown, N.P.; Chenna, R.; McGettigan, P.A.; McWilliam, H.; Valentin, F.; Wallace, I.M.; Wilm, A.; Lopez, R.; Thompson, J.D.; Gibson, T.J.; Higgins, D.G. Clustal W and Clustal X version 2.0. Bioinformatics, 2007, 23(21), 2947-2948. doi: 10.1093/bioinformatics/btm404 PMID: 17846036
  24. Martí-Renom, M.A.; Stuart, A.C.; Fiser, A.; Sánchez, R.; Melo, F.; Šali, A. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct., 2000, 29(1), 291-325. doi: 10.1146/annurev.biophys.29.1.291 PMID: 10940251
  25. Cerius2 Modeling Environment, Release 4.7; Accelrys Software Inc.: San Diego, 2003.
  26. Yang, J.; Zhang, Y. I-TASSER server: New development for protein structure and function predictions. Nucleic Acids Res., 2015, 43(W1), W174-W181. doi: 10.1093/nar/gkv342 PMID: 25883148
  27. Kelley, L.A.; Mezulis, S.; Yates, C.M.; Wass, M.N.; Sternberg, M.J.E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc., 2015, 10(6), 845-858. doi: 10.1038/nprot.2015.053 PMID: 25950237
  28. Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F.T.; de Beer, T.A.P.; Rempfer, C.; Bordoli, L.; Lepore, R.; Schwede, T. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res., 2018, 46(W1), W296-W303. doi: 10.1093/nar/gky427 PMID: 29788355
  29. Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589. doi: 10.1038/s41586-021-03819-2 PMID: 34265844
  30. De Lano, W.L. The PyMOL molecular graphics system, 2002, 40(1), 82-92. Available from: http://www.pymol.org
  31. Laskowski, R.A.; MacArthur, M.W.; Moss, D.S.; Thornton, J.M. PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Cryst., 1993, 26(2), 283-291. doi: 10.1107/S0021889892009944
  32. Colovos, C.; Yeates, T.O. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci., 1993, 2(9), 1511-1519. doi: 10.1002/pro.5560020916 PMID: 8401235
  33. Bowie, J.U.; Lüthy, R.; Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional structure. Science, 1991, 253(5016), 164-170. doi: 10.1126/science.1853201 PMID: 1853201
  34. Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res., 2007, 35, W407. doi: 10.1093/nar/gkm290
  35. Shen, M.; Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci., 2006, 15(11), 2507-2524. doi: 10.1110/ps.062416606 PMID: 17075131
  36. Bryson, K.; McGuffin, L.J.; Marsden, R.L.; Ward, J.J.; Sodhi, J.S.; Jones, D.T. Protein structure prediction servers at University College London Nucleic Acid. Research, 2005, 33(2), W36. doi: 10.1093/nar/gki410
  37. Cserzö, M.; Wallin, E.; Simon, I.; von Heijne, G.; Elofsson, A. Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: The dense alignment surface method. Protein Eng. Des. Sel., 1997, 10(6), 673-676. doi: 10.1093/protein/10.6.673 PMID: 9278280
  38. Krogh, A.; Larsson, B.; von Heijne, G.; Sonnhammer, E.L.L. Predicting transmembrane protein topology with a hidden markov model: Application to complete genomes11Edited by F. Cohen. J. Mol. Biol., 2001, 305(3), 567-580. doi: 10.1006/jmbi.2000.4315 PMID: 11152613
  39. Tusnády, G.E.; Simon, I. Principles governing amino acid composition of integral membrane proteins: Application to topology prediction. J. Mol. Biol., 1998, 283(2), 489-506. doi: 10.1006/jmbi.1998.2107 PMID: 9769220
  40. Hofmann, K.; Stoffel, W. TMbase: A database of membrane spanning proteins segments. Biol. Chem. Hoppe Seyler, 1993, 374, 166-170.
  41. Claros, M.G.; Heijne, G. TopPred II: An improved software for membrane protein structure predictions. Bioinformatics, 1994, 10(6), 685-686. doi: 10.1093/bioinformatics/10.6.685 PMID: 7704669
  42. Hirokawa, T.; Boon-Chieng, S.; Mitaku, S. SOSUI: Classification and secondary structure prediction system for membrane proteins. Bioinformatics, 1998, 14(4), 378-379. doi: 10.1093/bioinformatics/14.4.378 PMID: 9632836
  43. Juretić, D.; Zoranić, L.; Zucić, D. Basic charge clusters and predictions of membrane protein topology. J. Chem. Inf. Comput. Sci., 2002, 42(3), 620-632. doi: 10.1021/ci010263s PMID: 12086524
  44. Rost, B.; Yachdav, G.; Liu, J. The predict protein server. Nucleic Acids Res., 2004, 32(2), W321. doi: 10.1093/nar/gkh377
  45. Jones, D.T.; Taylor, W.R.; Thornton, J.M. A model recognition approach to the prediction of all-helical membrane protein structure and topology. Biochemistry, 1994, 33(10), 3038-3049. doi: 10.1021/bi00176a037 PMID: 8130217
  46. Bagos, P.G.; Liakopoulos, T.D.; Hamodrakas, S.J. Algorithms for incorporating prior topological information in HMMs: Application to transmembrane proteins. BMC Bioinformatics, 2006, 7(1), 189. doi: 10.1186/1471-2105-7-189 PMID: 16597327
  47. Schultz, J.; Milpetz, F.; Bork, P.; Ponting, C.P. SMART, a simple modular architecture research tool: Identification of signaling domains. Proc. Natl. Acad. Sci. USA, 1998, 95(11), 5857-5864. doi: 10.1073/pnas.95.11.5857 PMID: 9600884
  48. Reynolds, S.M.; Käll, L.; Riffle, M.E.; Bilmes, J.A.; Noble, W.S. Transmembrane topology and signal peptide prediction using dynamic bayesian networks. PLOS Comput. Biol., 2008, 4(11), e1000213. doi: 10.1371/journal.pcbi.1000213 PMID: 18989393
  49. Gallwitz, B.; Witt, M.; Paetzold, G.; Morys-Wortmann, C.; Zimmermann, B.; Eckart, K.; Fölsch, U.R.; Schmidt, W.E. Structure/activity characterization of glucagon-like peptide-1. Eur. J. Biochem., 1994, 225(3), 1151-1156. doi: 10.1111/j.1432-1033.1994.1151b.x PMID: 7957206
  50. Zhang, Z.; Li, Y.; Lin, B.; Schroeder, M.; Huang, B. Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics, 2011, 27(15), 2083-2088. doi: 10.1093/bioinformatics/btr331 PMID: 21636590
  51. Huang, B.; Schroeder, M. LIGSITEcsc: Predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct. Biol., 2006, 6(1), 19. doi: 10.1186/1472-6807-6-19 PMID: 16995956
  52. Brady, G.P., Jr; Stouten, P.F.W. Fast prediction and visualization of protein binding pockets with PASS. J. Comput. Aided Mol. Des., 2000, 14(4), 383-401. doi: 10.1023/A:1008124202956 PMID: 10815774
  53. Laurie, A.T.R.; Jackson, R.M. Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites. Bioinformatics, 2005, 21(9), 1908-1916. doi: 10.1093/bioinformatics/bti315 PMID: 15701681
  54. Laskowski, R.A. SURFNET: A program for visualizing molecular surfaces, cavities, and intermolecular interactions. J. Mol. Graph., 1995, 13(5), 323. doi: 10.1016/0263-7855(95)00073-9
  55. Dundas, J.; Ouyang, Z.; Tseng, J.; Binkowski, A.; Turpaz, Y.; Liang, J. CASTp: Computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res., 2006, 34(2), W116. doi: 10.1093/nar/gkl282
  56. Rani, M.; Nischal, A.; Sahoo, G.C.; Khattri, S. Computational analysis of the 3-D structure of human GPR87 protein: Implications for structure-based drug design. Asian Pac. J. Cancer Prev., 2013, 14(12), 7473-7482. doi: 10.7314/APJCP.2013.14.12.7473 PMID: 24460321
  57. Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem., 2005, 26(16), 1701-1718. doi: 10.1002/jcc.20291 PMID: 16211538
  58. Berendsen, H.J.C.; Grigera, J.R.; Straatsma, T.P. The missing term in effective pair potentials. J. Phys. Chem., 1987, 91(24), 6269-6271. doi: 10.1021/j100308a038
  59. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph., 1996, 14(1), 33-38. doi: 10.1016/0263-7855(96)00018-5
  60. Xu, Y.; Shen, Z.; Wiper, D.W.; Wu, M.; Morton, R.E.; Elson, P.; Kennedy, A.W.; Belinson, J.; Markman, M.; Casey, G. Lysophosphatidic acid as a potential biomarker for ovarian and other gynecologic cancers. JAMA, 1998, 280(8), 719-723. doi: 10.1001/jama.280.8.719 PMID: 9728644
  61. Abu-Dief, A.M.; El-Khatib, R.M.; El-Dabea, T.; Abdou, A.; Aljohani, F.S.; Al-Farraj, E.S.; Barnawi, I.O.; El-Remaily, M.A.E.A.A.A. Fabrication, structural elucidation of some new metal chelates based on N-(1H-Benzoimidazol-2-yl)-guanidine ligand: DNA interaction, pharmaceutical studies and molecular docking approach. J. Mol. Liq., 2023, 386, 122353. doi: 10.1016/j.molliq.2023.122353
  62. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 1996, 261(3), 470-489. doi: 10.1006/jmbi.1996.0477 PMID: 8780787
  63. Rarey, M.; Kramer, B.; Lengauer, T. The particle concept: Placing discrete water molecules during protein-ligand docking predictions. Proteins, 1999, 34(1), 17-28. doi: 10.1002/(SICI)1097-0134(19990101)34:13.0.CO;2-1 PMID: 10336380
  64. Böhm, H.J. The computer program LUDI: A new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des., 1992, 6(1), 61-78. doi: 10.1007/BF00124387 PMID: 1583540
  65. Rani, M.; Dikhit, M.R.; Sahoo, G.C.; Das, P. Comparative domain modeling of human EGF-like module EMR2 and study of interaction of the fourth domain of EGF with chondroitin 4-sulphate. J. Biomed. Res., 2011, 25(2), 100-110. doi: 10.1016/S1674-8301(11)60013-4 PMID: 23554678
  66. Sahoo, G.C.; Dikhit, M.R.; Rani, M.; Ansari, M.Y.; Jha, C.; Rana, S.; Das, P. Analysis of sequence, structure of GAPDH of Leishmania donovani and its interactions. J. Biomol. Struct. Dyn., 2013, 31(3), 258-275. doi: 10.1080/07391102.2012.698189 PMID: 22830998
  67. Rani, M.; Sharma, A.K.; Chouhan, R.S.; Sur, S.; Mansuri, R.; Singh, R.K. Natural flavonoid pectolinarin computationally targeted as a promising drug candidate against SARS-CoV-2. Curr. Res. Struct. Biol., 2024, 7, 100120. doi: 10.1016/j.crstbi.2023.100120 PMID: 38205118
  68. Téletchéa, S.; Esque, J.; Urbain, A.; Etchebest, C.; de Brevern, A.G. Evaluation of transmembrane protein structural models using HPMScore. BioMedInformatics, 2023, 3(2), 306-326. doi: 10.3390/biomedinformatics3020021
  69. Wall, M.A.; Coleman, D.E.; Lee, E.; Iñiguez-Lluhi, J.A.; Posner, B.A.; Gilman, A.G.; Sprang, S.R. The structure of the G protein heterotrimer Giα1β1γ2. Cell, 1995, 83(6), 1047-1058. doi: 10.1016/0092-8674(95)90220-1 PMID: 8521505
  70. Ota, T.; Suzuki, Y.; Nishikawa, T.; Otsuki, T.; Sugiyama, T.; Irie, R.; Wakamatsu, A. Complete sequencing and characterization of 21,243 full-length human cDNAs. Nat. Genet., 2004, 36(1), 40-45. doi: 10.1038/ng1285 PMID: 14702039
  71. Kim, K.M.; Caron, M.G. Complementary roles of the DRY motif and C-terminus tail of GPCRS for G protein coupling and β-arrestin interaction. Biochem. Biophys. Res. Commun., 2008, 366(1), 42-47. doi: 10.1016/j.bbrc.2007.11.055 PMID: 18036556
  72. Heifetz, A.; Barker, O.; Morris, G.B.; Law, R.J.; Slack, M.; Biggin, P.C. Toward an understanding of agonist binding to human Orexin-1 and Orexin-2 receptors with G-protein-coupled receptor modeling and site-directed mutagenesis. Biochemistry, 2013, 52(46) doi: 10.1021/bi401119m

Дополнительные файлы

Доп. файлы
Действие
1. JATS XML
Скачать

© Bentham Science Publishers, 2025