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Understanding the interactions between repurposed drugs sertindole and temoporfin with receptor for advanced glycation endproducts: Therapeutic implications in cancer and metabolic diseases

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Abstract

Context

In the pursuit of novel therapeutic possibilities, repurposing existing drugs has gained prominence as an efficient strategy. The findings from our study highlight the potential of repurposed drugs as promising candidates against receptor for advanced glycation endproducts (RAGE) that offer therapeutic implications in cancer, neurodegenerative conditions and metabolic syndromes. Through careful analyses of binding affinities and interaction patterns, we identified a few promising candidates, ultimately focusing on sertindole and temoporfin. These candidates exhibited exceptional binding affinities, efficacy, and specificity within the RAGE binding pocket. Notably, they displayed a pronounced propensity to interact with the active site of RAGE. Our investigation further revealed that sertindole and temoporfin possess desirable pharmacological properties that highlighted them as attractive candidates for targeted drug development. Overall, our integrated computational approach provides a comprehensive understanding of the interactions between repurposed drugs, sertindole and temoporfin and RAGE that pave the way for future experimental validation and drug development endeavors.

Methods

We present an integrated approach utilizing molecular docking and extensive molecular dynamics (MD) simulations to evaluate the potential of FDA-approved drugs, sourced from DrugBank, against RAGE. To gain deeper insights into the binding mechanisms of the elucidated candidate repurposed drugs, sertindole and temoporfin with RAGE, we conducted extensive all-atom MD simulations, spanning 500 nanoseconds (ns). These simulations elucidated the conformational dynamics and stability of the RAGE-sertindole and RAGE-temoporfin complexes.

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Data availability

All the data is presented in the manuscript.

References

  1. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Rudrapal M, Khairnar SJ, Jadhav AG (2020) Drug repurposing (DR): an emerging approach in drug discovery. Drug Repurposing-Hypothesis Molec Asp Ther Appl 10. 1(1). https://doi.org/10.5772/intechopen.93193

  3. Cha Y, Erez T, Reynolds I, Kumar D, Ross J, Koytiger G, Kusko R, Zeskind B, Risso S, Kagan E (2018) Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol 175:168–180

    Article  CAS  PubMed  Google Scholar 

  4. Sleire L, Førde HE, Netland IA, Leiss L, Skeie BS, Enger PØ (2017) Drug repurposing in cancer. Pharmacol Res 124:74–91

    Article  CAS  PubMed  Google Scholar 

  5. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, Doig A, Guilliams T, Latimer J, McNamee C (2019) Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discovery 18:41–58

    Article  CAS  PubMed  Google Scholar 

  6. Bierhaus A, Humpert PM, Morcos M, Wendt T, Chavakis T, Arnold B, Stern DM, Nawroth PP (2005) Understanding RAGE, the receptor for advanced glycation end products. J Mol Med 83:876–886

    Article  CAS  PubMed  Google Scholar 

  7. Schmidt AM, Du Yan S, Yan SF, Stern DM (2000) The biology of the receptor for advanced glycation end products and its ligands. Biochimica et Biophysica Acta (BBA)-Molec Cell Res 1498:99–111

  8. Chuah YK, Basir R, Talib H, Tie TH, Nordin N (2013) Receptor for advanced glycation end products and its involvement in inflammatory diseases. Int J Inflamm 2013:1–15. https://doi.org/10.1155/2013/403460

  9. Jangde N, Ray R, Rai V (2020) RAGE and its ligands: from pathogenesis to therapeutics. Crit Rev Biochem Mol Biol 55:555–575

    Article  CAS  PubMed  Google Scholar 

  10. Rouhiainen A, Kuja-Panula J, Tumova S, Rauvala H (2013) RAGE-mediated cell signaling. Calcium-Bind Proteins RAGE: Struct Basics Clin Appl 963:239–263

  11. Bongarzone S, Savickas V, Luzi F, Gee AD (2017) Targeting the receptor for advanced glycation endproducts (RAGE): a medicinal chemistry perspective. J Med Chem 60:7213–7232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Sparvero LJ, Asafu-Adjei D, Kang R, Tang D, Amin N, Im J, Rutledge R, Lin B, Amoscato AA, Zeh HJ (2009) RAGE (receptor for advanced glycation endproducts), RAGE ligands, and their role in cancer and inflammation. J Transl Med 7:1–21

    Article  Google Scholar 

  13. Burstein A, Sabbagh M, Andrews R, Valcarce C, Dunn I, Altstiel L (2018) Development of Azeliragon, an oral small molecule antagonist of the receptor for advanced glycation endproducts, for the potential slowing of loss of cognition in mild Alzheimer’s disease. J Prev Alzheim Dis 5:149–154

    CAS  Google Scholar 

  14. Xue H, Li J, Xie H, Wang Y (2018) Review of drug repositioning approaches and resources. Int J Biol Sci 14:1232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Jourdan J-P, Bureau R, Rochais C, Dallemagne P (2020) Drug repositioning: a brief overview. J Pharm Pharmacol 72:1145–1151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082

    Article  CAS  PubMed  Google Scholar 

  17. Huey R, Morris GM, Forli S (2012) Using autodock 4 and autodock vina with autodocktools: a tutorial. Scripps Res Inst Molec Graph Lab 10550:92037

    Google Scholar 

  18. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Biovia DS (2017) Discovery studio visualizer. San Diego, CA, USA, p 936

  20. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718

    Article  PubMed  Google Scholar 

  21. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38

    Article  CAS  PubMed  Google Scholar 

  23. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-Pdb Viewer: an environment for comparative protein modeling. Electrophoresis 18:2714–2723

    Article  CAS  PubMed  Google Scholar 

  24. Turner P (2005) XMGRACE, Version 5.1. 19. Center for Coastal and Land-Margin Research, Oregon Graduate Institute of Science and Technology, Beaverton, OR 2 5:19

  25. Kozlyuk N, Gilston BA, Salay LE, Gogliotti RD, Christov PP, Kim K, Ovee M, Waterson AG, Chazin WJ (2021) A fragment-based approach to discovery of receptor for advanced glycation end products inhibitors. Proteins: Structure. Funct Bioinforma 89:1399–1412

    Article  CAS  Google Scholar 

  26. Lagunin A, Stepanchikova A, Filimonov D, Poroikov V (2000) PASS: prediction of activity spectra for biologically active substances. Bioinformatics 16:747–748

    Article  CAS  PubMed  Google Scholar 

  27. Schmid N, Eichenberger AP, Choutko A, Riniker S, Winger M, Mark AE, Van Gunsteren WF (2011) Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur Biophys J 40:843–856

    Article  CAS  PubMed  Google Scholar 

  28. Wu Y, Tepper HL, Voth GA (2006) Flexible simple point-charge water model with improved liquid-state properties. J Chem Phys 124:024503

    Article  PubMed  Google Scholar 

  29. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods are used to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kumari R, Kumar R, Consortium OSDD, Lynn A (2014) g_mmpbsa a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inform Model 54(7):1951–1962

  31. Magna M, Hwang GH, McIntosh A, Drews-Elger K, Takabatake M, Ikeda A, Mera BJ, Kwak T, Miller P, Lippman ME (2023) RAGE inhibitor TTP488 (Azeliragon) suppresses metastasis in triple-negative breast cancer. NPJ Breast Cancer 9:59

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Faruqui T, Khan MS, Akhter Y, Khan S, Rafi Z, Saeed M, Han I, Choi E-H, Yadav DK (2022) RAGE inhibitors for targeted therapy of cancer: a comprehensive review. Int J Mol Sci 24:266

    Article  PubMed  PubMed Central  Google Scholar 

  33. Park H, Boyington JC (2010) The 1.5 Å crystal structure of human receptor for advanced glycation endproducts (RAGE) ectodomains reveals unique features determining ligand binding. J Biol Chem 285:40762–40770

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. El-Far AH, Sroga G, Al Jaouni SK, Mousa SA (2020) Role and mechanisms of RAGE-ligand complexes and RAGE-inhibitors in cancer progression. Int J Mol Sci 21:3613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kawasumi M, Nghiem P (2007) Chemical genetics: elucidating biological systems with small-molecule compounds. J Investig Dermatol 127:1577–1584

    Article  CAS  PubMed  Google Scholar 

  36. Galloway WR, Isidro-Llobet A, Spring DR (2010) Diversity-oriented synthesis as a tool for the discovery of novel biologically active small molecules. Nat Commun 1:80

    Article  PubMed  Google Scholar 

  37. Singh S, Baker QB, Singh DB (2022) Molecular docking and molecular dynamics simulation. Elsevier, Bioinformatics, pp 291–304

    Google Scholar 

  38. Cavasotto CN, Abagyan RA (2004) Protein flexibility in ligand docking and virtual screening to protein kinases. J Mol Biol 337:209–225

    Article  CAS  PubMed  Google Scholar 

  39. Pitera JW (2014) Expected distributions of root-mean-square positional deviations in proteins. J Phys Chem B 118:6526–6530

    Article  CAS  PubMed  Google Scholar 

  40. Singh R, Purohit R (2023) Computational analysis of protein-ligand interaction by targeting a cell cycle restrainer. Comput Methods Programs Biomed 231:107367

    Article  PubMed  Google Scholar 

  41. Lobanov MY, Bogatyreva N, Galzitskaya O (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42:623–628

    Article  CAS  Google Scholar 

  42. Marsh JA, Teichmann SA (2011) Relative solvent accessible surface area predicts protein conformational changes upon binding. Structure 19:859–867

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Pace CN, Fu H, Lee Fryar K, Landua J, Trevino SR, Schell D, Thurlkill RL, Imura S, Scholtz JM, Gajiwala K (2014) Contribution of hydrogen bonds to protein stability. Protein Sci 23:652–661

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Böhm HJ, Klebe G (1996) What can we learn from molecular recognition in protein–ligand complexes for the design of new drugs? Angew Chem, Int Ed Engl 35:2588–2614

    Article  Google Scholar 

  45. Yunta MJ (2017) It is important to compute intramolecular hydrogen bonding in drug design. Am J Model Optim 5:24–57

    Google Scholar 

  46. Menéndez CA, Accordino SR, Gerbino DC, Appignanesi GA (2016) Hydrogen bond dynamic propensity studies for protein binding and drug design. PLoS ONE 11:e0165767

    Article  PubMed  PubMed Central  Google Scholar 

  47. Caliskan M, Mandaci SY, Uversky VN, Coskuner-Weber O (2021) Secondary structure dependence of amyloid-β (1–40) on simulation techniques and force field parameters. Chem Biol Drug Des 97:1100–1108

    Article  CAS  PubMed  Google Scholar 

  48. Orengo CA, Todd AE, Thornton JM (1999) From protein structure to function. Curr Opin Struct Biol 9:374–382

    Article  CAS  PubMed  Google Scholar 

  49. Cournia Z, Chipot C, Roux B, York DM, Sherman W (2021) Free energy methods in drug discovery—introduction. Free energy methods in drug discovery: current state and future directions, pp 1–38. American Chemical Society

  50. Schug A, Onuchic JN (2010) From protein folding to protein function and biomolecular binding by energy landscape theory. Curr Opin Pharmacol 10:709–714

    Article  CAS  PubMed  Google Scholar 

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Funding

M.S.K. acknowledges the generous support from the Research Supporting project (RSP2024R352) by the King Saud University, Riyadh, Kingdom of Saudi Arabia. A.S. is grateful to Ajman University, UAE, for supporting this publication.

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Authors and Affiliations

  1. Center of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates

    Anas Shamsi, Moyad Shahwan & Akram Ashames

  2. Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, 110025, India

    Saleha Anwar

  3. Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, P.O. Box 346, Ajman, United Arab Emirates

    Moyad Shahwan & Akram Ashames

  4. Department of Biochemistry, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Mohd Shahnawaz Khan

  5. Gachon Institute of Pharmaceutical Science and Department of Pharmacy, College of Pharmacy, Gachon University, Incheon, Republic of Korea

    Dharmendra Kumar Yadav

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  1. Anas Shamsi
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Contributions

Moyad Shahwan: conceptualization, writing—review and editing, investigation, funding acquisition; Akram Ashames: writing—review and editing, funding acquisition; Mohd Shahnawaz Khan: data curation, methodology, funding acquisition, data validation, methodology; Saleha Anwar: visualization, software, writing—review and editing, data curation; Dharmendra Kumar Yadav: methodology, resources, formal analysis, project administration; Anas Shamsi: conceptualization, data curation, data validation, resources, visualization, software, writing—review and editing, funding acquisition.

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Correspondence to Anas Shamsi.

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Shamsi, A., Shahwan, M., Anwar, S. et al. Understanding the interactions between repurposed drugs sertindole and temoporfin with receptor for advanced glycation endproducts: Therapeutic implications in cancer and metabolic diseases. J Mol Model 30, 170 (2024). https://doi.org/10.1007/s00894-024-05967-4

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