Introducing a two‐dimensional graph of docking score difference vs. similarity of ligand‐receptor interactions

Mohammad Rizki Fadhil Pratama; Hadi Poerwono; Siswandono Siswodihardjo

doi:10.22146/ijbiotech.62194

Introducing a two‐dimensional graph of docking score difference vs. similarity of ligand‐receptor interactions

https://doi.org/10.22146/ijbiotech.62194

Mohammad Rizki Fadhil Pratama⁽¹⁾, Hadi Poerwono⁽²⁾, Siswandono Siswodihardjo^(3*)

(1) Doctoral Program of Pharmaceutical Science, Faculty of Pharmacy, Universitas Airlangga, Surabaya, East Java
(2) Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Airlangga, Surabaya, East Java
(3) Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Airlangga, Surabaya, East Java
(*) Corresponding Author

Abstract

Observation of molecular docking results was generally performed by analyzing the docking score and the interacting amino acid residues separately either in tables or graphs. Sometimes it was not easy to rank the tested ligands’ docking results, especially if there were many ligands. This study aims to introduce a new way to analyze docking results with a two‐dimensional graph between the difference in docking score and the similarity of ligand‐receptor interactions. Molecular docking was performed with one reference ligand and several test ligands. The docking score difference was obtained between the test and the reference ligands as the graph’s x‐axis. Meanwhile, the y‐axis contains the similarity of ligand‐receptor interactions, obtained from the ratio of amino acid residues and the types of interactions between the test and reference ligands. Docking result analysis was more straightforward because two critical parameters were presented in one graph. This graph could be used to support the analysis of the docking results.

Keywords

Analysis; docking; docking score; interaction; two‐dimensional graph

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References

Abuelizz HA, AlSalahi R, AlAsri J, Mortier J, Marzouk M, Ezzeldin E, Ali AA, Khalil MG, Wolber G, Ghabbour HA, Almehizia AA, Abdel Jaleel GA. 2017. Synthesis, crystallographic characterization, molecular docking and biological activity of isoquinoline derivatives. Chem Cent J. 11(1):103. doi:10.1186/s1306501703211.

Coybarrera E. 2020. Discrimination of naturally occurring 2arylbenzofurans as cyclooxygenase2 inhibitors: Insights into the binding mode and enzymatic inhibitory activity. Biomolecules. 10(2):176. doi:10.3390/biom10020176.

Deshpande RR, Tiwari AP, Nyayanit N, Modak M. 2020. In silico molecular docking analysis for repurposing therapeutics against multiple proteins from SARSCoV2. Eur J Pharmacol. 886:173430. doi:10.1016/j.ejphar.2020.173430.

Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. 2015. Molecular docking and structurebased drug design strategies. Molecules. 20(7):13384–13421. doi:10.3390/molecules200713384.

Forli S, Huey R, Pique ME, Sanner MF, Goodsell DS, Olson AJ. 2016. Computational protein ligand docking and virtual drug screening with the AutoDock suite. Nat Protoc. 11(5):905–919. doi:10.1038/nprot.2016.051.

Gimeno A, OjedaMontes MJ, TomásHernández S, CeretoMassagué A, BeltránDebón R, Mulero M, Pujadas G, GarciaVallvé S. 2019. The light and dark sides of virtual screening: What is there to know? Int J Mol Sci. 20(6):1375. doi:10.3390/ijms20061375.

Kolb P, Irwin J. 2009. Docking Screens: Right for the Right Reasons? Curr Top Med Chem. 9(9):755–770. doi:10.2174/156802609789207091.

Lam PC, Abagyan R, Totrov M. 2018. Ligandbiased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structurebased approach. J Comput Aided Mol Des. 32(1):187–198. doi:10.1007/s10822 0170058x.

Li L, Koh CC, Reker D, Brown JB, Wang H, Lee NK, haw Liow H, Dai H, Fan HM, Chen L, Wei DQ. 2019. Predicting proteinligand interactions based on bow pharmacological space and Bayesian additive regression trees. Sci Rep. 9(1):7703. doi:10.1038/s41598 019431256.

Lin X, Li X, Lin X. 2020. A review on applications of computational methods in drug screening and design. Molecules 25(6):1375. doi:10.3390/molecules25061375.

Mandour Y, Handoussa H, Swilam N, Hanafi R, Mahran L. 2016. Structural Docking Studies of COXII Inhibitory Activity for Metabolites Derived from Corchorus olitorius and Vitis vinifera. Int J Food Prop. 19(10):2377–2384. doi:10.1080/10942912.2015.1114492.

Meng XY, Zhang HX, Mezei M, Cui M. 2012. Molecular Docking: A Powerful Approach for StructureBased Drug Discovery. Curr Comput Aided Drug Des. 7(2):146–157. doi:10.2174/157340911795677602.

Molinari A, Oliva A, ArismendiMacuer M, Guzmán L, Acevedo W, Aguayo D, Vinet R, Feliciano AS. 2019. Antiproliferative benzoindazolequinones as potential cyclooxygenase2 inhibitors. Molecules 24(12):2261. doi:10.3390/molecules24122261.

Morris GM, Ruth H, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. 2009. Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 30(16):2785–2791. doi:10.1002/jcc.21256.

Oniga SD, Pacureanu L, Stoica CI, Palage MD, Crăciun A, Rusu LR, Crisan EL, Araniciu C. 2017. COX inhibition profile and molecular docking studies of some 2(Trimethoxyphenyl)Thiazoles. Molecules 21(9):4538. doi:10.3390/molecules22091507.

Pagadala NS, Syed K, Tuszynski J. 2017. Software for molecular docking: a review. Biophys Rev. 9(2):91– 102. doi:10.1007/s1255101602471.

Pantsar T, Poso A. 2018. Binding affinity via docking: Fact and fiction. Molecules. 23(8):1899. doi:10.3390/molecules23081899.

Pinzi L, Rastelli G. 2019. Molecular docking: Shifting paradigms in drug discovery. Int J Mol Sci. 20(18):4331. doi:10.3390/ijms20184331.

Pratama MRF, Nasibova TA, Pratiwi D, Kumar P, Garaev EA. 2021. Peganum harmala and its alkaloids as dopamine receptor antagonists: In silico study. Biointerface Res Appl Chem. 11(3):10301–10316. doi:10.33263/BRIAC113.1030110316.

Pratama MRF, Poerwono H, Siswodihardjo S. 2020. Molecular docking of novel 5Obenzoylpinostrobin derivatives as SARSCoV2 main protease inhibitors. Pharm Sci. 26(Suppl1):S63–S77. doi:10.34172/PS.2020.57.

Ramírez D, Caballero J. 2016. Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target? Int J Mol Sci. 17(4):525. doi:10.3390/ijms17040525.

Sadasivam K, Salgado Moran G, MendozaHuizar LH, Cardona Villada W, Gerli Candia L, MenesesOlmedo LM, Cuesta Hoyos S. 2020. Theoretical investigation of the molecular structure and molecular docking of etoricoxib. J Chil Chem Soc. 65(2):4804– 4806. doi:10.4067/S071797072020000204804.

Salmaso V, Moro S. 2018. Bridging molecular docking to molecular dynamics in exploring ligandprotein recognition process: An overview. Front Pharmacol. 9:923. doi:10.3389/fphar.2018.00923.

Shrivastava N, Joshi J, Sehgal N, Kumar IP. 2017. Cyclooxygenase2 identified as a potential target for novel radio modulator scopolamine methyl bromide: An in silico study. Inform Med Unlocked. 9:18–25. doi:10.1016/j.imu.2017.05.007.

Vieira TF, Sousa SF. 2019. Comparing AutoDock and Vina in ligand/decoy discrimination for virtual screening. Appl Sci. 9(21). doi:10.3390/app9214538.

Wang JL, Limburg D, Graneto MJ, Springer J, Hamper JRB, Liao S, Pawlitz JL, Kurumbail RG, Maziasz T, Talley JJ, Kiefer JR, Carter J. 2010. The novel benzopyran class of selective cyclooxygenase 2 inhibitors. Part 2: The second clinical candidate having a shorter and favorable human halflife. Bioorg Med Chem Lett. 20(23):7159–7163. doi:10.1016/j.bmcl.2010.07.054.

DOI: https://doi.org/10.22146/ijbiotech.62194