In silico screening, molecular dynamic simulation, and pharmacokinetic studies of new Schiff base derivatives from 2-(3-benzoylphenyl) propionic acid as tyrosyl-tRNA synthetase inhibitor

Abstract

Bacterial resistance is a major problem in hospitals and the community. Thus, much antibacterial research has focused on discovering new chemical agents and bacterial targets. Computational and structure-based design methods are used for the improvement of drug discovery. This work developed new Schiff base compounds from 2-(3-benzoylphenyl) propionic acid. The unique compounds were categorized as S and S(1-6). They were examined in silico for antibacterial activity on the tyrosyl-tRNA synthetase enzyme. Dynamic simulation and pharmacokinetic studies were also studied theoretically. In silico, experiments, including SwissADME studies, are utilized to predict the pharmacokinetics of newly designed compounds. While the docking studies done using GOLD Suite (v. 2021.3.0) software showed the binding of compounds with the enzyme tyrosyl-tRNA synthetase, finally, dynamic simulation studies of compound [S2] using the Desmond modules of the Schrodinger 2023 software. Since all compounds meet Lipinski's rule requirements, the new agents are expected to be given orally. Docking experiments showed that compound [S2] bound to tyrosyl-tRNA synthetase had the greatest PLP fitness value (89.02) compared to the reference ligand (79.71). Simulations of the compound [S2] with the enzyme pocket revealed stable variations with RMSD values below 3Å during the simulation period. Based on docking, compound [S2] is deemed a promising agent as a tyrosyl-tRNA synthetase inhibitor, with stable variations during dynamic simulation and RMSD and RMSF values within the normal range.

Keywords

• Tyrosyl-tRNA synthetase
• Molecular dynamic simulation
• SWISS ADME

References

1. [1] Lin X, Li X, Lin X. A review on applications of computational methods in drug screening and design. Molecules. 25(6) (2020) 1–17.
2. [2] Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol Rev. 66(1) (2014) 334–395.
3. [3] Choudhuri S, Yendluri M, Poddar S, Li A, Mallick K, Mallik S, et al. Recent Advancements in Computational Drug Design Algorithms through Machine Learning and Optimization. Kinases and Phosphatases. 1(2) (2023) 117–140.
4. [4] Sadybekov A V., Katritch V. Computational approaches streamlining drug discovery. Nature. 616(7958) (2023) 673–685.
5. [5] Ngaini Z, Rasin F, Wan Zullkiplee WSH, Abd Halim AN. Synthesis and molecular design of mono aspirinate thiourea-azo hybrid molecules as potential antibacterial agents. Phosphorus, Sulfur Silicon Relat Elem. 196(3) )(2020) 275–282.
6. [6] Xiao ZP, Wei W, Liu Q, Wang PF, Luo X, Chen FY, et al. C-7 modified flavonoids as novel tyrosyl-tRNA synthetase inhibitors. RSC Adv. 7(11) (2017) 6193–6201.
7. [7] Hooda T, Sharma S, Goyal N. Synthesis, In Silico Designing, Microbiological Evaluation and Structure Activity Relationship of Novel Amide Derivatives of 1-(2,4-Dinitrophenyl)-2-(3-Methylbenzo[b]Thiophen-6-yl)-1H-Benzo[d]Imidazole-5-Carboxylic Acid. Polycycl Aromat Compd. 42(6) (2022) 3361–3376.
8. [8] Guo ZH, Yin Y, Wang C, Wang PF, Zhang XT, Wang ZC, et al. Design, synthesis and molecular docking of salicylic acid derivatives containing metronidazole as a new class of antimicrobial agents. Bioorganic Med Chem. 23(18) (2015) 6148–6156.

9. [9] Hooda T, Sharma S, Goyal N. In-silico designing, synthesis, SAR and microbiological evaluation of novel amide derivatives of 2-(3-methylbenzo[b]thiophen-6-yl)-1-(3-nitrophenyl)-1H-benzo[d]imidazole-5-carboxylic Acid. Indian J Pharm Educ Res. 53(3) (2019) 437–450.
10. [10] Hughes CA, Gorabi V, Escamilla Y, Dean FB, Bullard JM. Two Forms of Tyrosyl-tRNA Synthetase from Pseudomonas aeruginosa: Characterization and Discovery of Inhibitory Compounds. SLAS Discov. 25(9) (2020) 1072–1086.
11. [11] Bouz G, Zitko J. Inhibitors of aminoacyl-tRNA synthetases as antimycobacterial compounds: An up-to-date review. Bioorg Chem. 110 (2020) 104806.
12. [12] Wei W, Liu Q, Li ZZ, Shi WK, Fu X, Liu J, et al. Synthesis and evaluation of adenosine containing 3-arylfuran-2(5H)-ones as tyrosyl-tRNA synthetase inhibitors. Eur J Med Chem. 133 (2017) 62–68.
13. [13] Qiu X, Janson CA, Smith WW, Green SM, McDevitt P, Johanson K, et al. Crystal structure of Staphylococcus aureus tyrosyl‐tRNA synthetase in complex with a class of potent and specific inhibitors. Protein Sci. 10(10) (2001) 2008–2016.
14. [14] Vondenhoff GHM, Van Aerschot A. Aminoacyl-tRNA synthetase inhibitors as potential antibiotics. Eur J Med Chem. 46(11) (2011) 5227–5236.
15. [15] Ren W, Zhao Q, Yu M, Guo L, Chang H, Jiang X, et al. Design and synthesis of novel spirooxindole–indenoquinoxaline derivatives as novel tryptophanyl-tRNA synthetase inhibitors. Mol Divers. 24(4) (2020) 1043–1063.
16. [16] Qiu XY, Janson C, Smith W, Green S, Mcdevitt P, Johanson K, et al. Crystallographic Studies of Staphylococcus aureus Tyrosyl-tRNA Synthetase in Complex with Inhibitors. 2862 (1999) 2862.
17. [17] Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. In: Progress in Medicinal Chemistry. 1st ed. Elsevier B.V., (2021) 273–343.
18. [18] Astalakshmi D., T G, K B GS, M N, M R HHS, S G, et al. Over View on Molecular Docking: A Powerful Approach for Structure Based Drug Discovery. Int J Pharm Sci Rev Res. 77(2) (2022) 146–57.
19. [19] Grinter SZ, Zou X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design. Molecules. 19(7) (2014) 10150–10176.
20. [20] Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 7 (2017) 42717.
21. [21] Roman AL, Mark BS. LigPlot+: Multiple Ligand-Protein Interaction Diagrams for Drug Discovery. J Chem Inf Model. 51 (2011) 2778–2786.
22. [22] Cabrera N, Cuesta SA, Mora JR, Calle L, Márquez EA, Kaunas R, et al. In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study. Pharmaceutics. 14(2) (2022).
23. [23] Kumar BS, Anuragh S, Kammala AK, Ilango K. Computer Aided Drug Design Approach to Screen Phytoconstituents of Adhatoda vasica as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme. Life. 12(2) (2022).
24. [24] Abdulhamza HM, Farhan MS. Synthesis, characterization and preliminary anti-inflammatory evaluation of new fenoprofen hydrazone derivatives. Iraqi J Pharm Sci. 9(2) (2021) 239–244.
25. [25] Hou T, Wang J, Zhang W, Xu X. ADME evaluation in drug discovery. J Chem Inf Model. 2007;47(1) (2007) 208–218.
26. [26] john M.beale J john HB. Wilson and Gisvold’s textbook of organic medicinal and pharmaceutical chemistry. 12th ed. (2004) 1–1022.
27. [27] Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein–ligand docking using GOLD. Proteins. 52 (2003) 609–623.
28. [28] Hanna JS, Khan AK, Essa HJ. Synthesis, Molecular Docking, and Cytotoxic Evaluation of Some Novel 1H-Pyrazole Derivatives from Pentoxifylline. Int J Pharm Res. 12(02) (2020) 3158–3168.
29. [29] Alvarez J, Shoichet B. Virtual screening in drug discovery. Virtual Screening in Drug Discovery. (2005) 1–470
30. [30] Saurabh S, Sivakumar PM, Perumal V, Khosravi A, Sugumaran A, Prabhawathi V. Molecular Dynamics Simulations in Drug Discovery and Drug Delivery. Eng Mater. (2020) 275–301
31. [31] Radwan A, Mahrous GM. Docking studies and molecular dynamics simulations of the binding characteristics of waldiomycin and its methyl ester analog to Staphylococcus aureus histidine kinase. PLoS One. 15(6) (2020) 1–16.