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Submitted
Feb 24, 2021
Accepted
Mar 18, 2021
Published
Mar 27, 2021
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Abstract

Cardiovascular disease cases increase due to consumption cholesterol dietary habit. It is well-known that squalence synthase (SQS) is the first committed enzyme for cholesterol synthesis. Therefore, SQS become target of anti-cholesterol. This paper aims to determine the potential binding pocket of SQS (PDB ID: 1EZF). Dogsitescorer, siteFinder, and DEPTH were used for binding pocket prediction and MOE 2009.10 was performed for molecular docking. We found that there are five out of 37 pockets which have druggability score above 0.8. Pocket_5 is the highest drugability and favorable for hydrophobic interaction, yet lower number of hydrogen bond with the ligand. However, Pocket_2, and Pocket_3 are suitable for hydrogen bond formation of ligand-protein. Molecular docking study showed that TAK-475, D99, and Cynarin inhibitors were embedded on the P_2 and P_3 of SQS, showing that P2_and P3 are promising binding pocket for ligand interactions. These results show a promising alternative to design anti-cholesterol using these potential pocket in silico.

Keywords

Squalene Synthase Cholesterol Potential Binding Pocket Molecular Interaction

Article Details

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How to Cite
1.
Amelia F, Hidayat B, Iryani I, Iswendi I. Potential Hydrophobic Pocket of Squalene Synthase: An In Silico Analysis. EKSAKTA [Internet]. 2021Mar.27 [cited 2024Apr.26];22(1):18-26. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/263

References

  1. Dietschy J M and Siperstein M D 1967 Effect of cholesterol feeding and fasting on sterol synthesis in seventeen tissues of the rat. J. Lipid Res. 8 97–104
  2. WHO Raised cholesterol: Situation and trends
  3. Sando K R and Knight M 2015 Nonstatin therapies for management of dyslipidemia: a review. Clin. Ther. 37 2153–79
  4. Souich P du, Roederer G and Dufour R 2017 Myotoxicity of statins: Mechanism of action Pharmacol. Ther. 175 1–16
  5. Mil A H M van, Westendorp R G J, Bollen E L E M, Lagaay M A and Blauw G J 2000 HMG-CoA Reductase Inhibitors in the Prevention of Stroke Drugs 59 1–6
  6. Huang H, Chu C L, Chen L and Shui D 2019 Evaluation of potential inhibitors of squalene synthase based on virtual screening and in vitro studies Comput. Biol. Chem. 80 390–7
  7. Chen Y, Chen X, Luo G, Zhang X, Lu F, Qiao L, He W, Li G and Zhang Y 2018 Discovery of Potential Inhibitors of Squalene Synthase from Traditional Chinese Medicine Based on Virtual Screening and In Vitro Evaluation of Lipid-Lowering Effect Molecules 23 1–18
  8. Do R, Kiss R S, Gaudet D and Engert J C 2009 Squalene synthase: A critical enzyme in the cholesterol biosynthesis pathway Clin. Genet. 75 19–29
  9. Charlton-Menys V and Durrington P N 2007 Squalene synthase inhibitors: Clinical pharmacology and cholesterol-lowering potential Drugs 67 11–6
  10. Hassan N M, Alhossary A A, Mu Y and Kwoh C-K 2017 Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration Sci. Rep. 7 15451
  11. Hetényi C and van der Spoel D 2009 Efficient docking of peptides to proteins without prior knowledge of the binding site Protein Sci. 11 1729–37
  12. Kumar P and Ghosh I 2019 Application of In Silico Drug Repurposing in Infectious Diseases pp 427–62
  13. Bianchi V, Gherardini P F, Helmer-Citterich M and Ausiello G 2012 Identification of binding pockets in protein structures using a knowledge-based potential derived from local structural similarities BMC Bioinformatics 13 1–13
  14. Ghersi D and Sanchez R 2009 Improving Accuracy and Efficiency of Blind Protein-Ligand Docking NIH Public Access 74 417–24
  15. Fahrrolfes R, Bietz S, Flachsenberg F, Meyder A, Nittinger E, Otto T, Volkamer A and Rarey M 2017 ProteinsPlus: a web portal for structure analysis of macromolecules Nucleic Acids Res. 45
  16. C.C.G inc 2009 MOLECULAR OPERATING ENVIRONMENT
  17. Tan K P, Nguyen T B, Patel S, Varadarajan R and Madhusudhan M S 2013 Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins Nucleic Acids Res. 41 14–21
  18. Amelia F, Iryani, Sari P Y, Parikesit A A, Bakri R, Toepak E P and Tambunan U S F 2018 Assessment of Drug Binding Potential of Pockets in the NS2B/NS3 Dengue Virus Protein IOP Conf. Series: Materials Science and Engineering (IOP Conf. Series: Materials Science and Engineering) p 349
  19. Iryani, Amelia F and Iswendi 2018 Active sites prediction and binding analysis E1-E2 protein human papillomavirus with biphenylsulfonacetic acid IOP Conf. Series: Materials Science and Engineering (IOP Publishing)
  20. Schmidtke P and Barril X 2010 Understanding and Predicting Druggability. A High-Throughput Method for Detection of Drug Binding Sites J. Med. Chem. 53 5858–5867
  21. Volkamer A, Kuhn D, Grombacher T, Rippmann F and Rarey M 2012 Combining global and local measures for structure-based druggability predictions. J. Chem. Inf. Model. 52 360–72
  22. Amelia F, Hidayat B and Iryani 2020 Hydrophobic Pocket of SARS-Cov-2 Spike Glycoprotein are Potential as Binding Pocket 1st ICChSE IOP Conf. Series: Journal of Physic (inpress)
  23. Murshudov G N, Dodson E J, Neil T K, Dodson G G, Higgins C F and Wilkinson A J 1994 The structural basis of sequence-independent peptide binding by OppA protein Science (80-. ). 264 1578–81
  24. Salentin S, Haupt V J, Daminelli S and Schroeder M 2014 Polypharmacology rescored: Proteineligand interaction profiles for remote binding site similarity assessment Prog. Biophys. Mol. Biol. 116 1–13