In silico study of synthetic Bromophenol Compounds against Staphylococcus aeurus's target protein (DHFR) Enzyme

Abstract

Natural products (NPs) serve as prototypes for a majority of antimicrobial agents currently in clinical use. The evolutionary process that gives rise to these molecules is inevitably accompanied by resistance mechanisms that curtail the clinical lifespan of any given class of antibiotics. Staphylococcus aeurus is among the class of microorganisms that exhibit resistance to multiple drugs. Dihydrofolate reductase (DHFR) enzyme is a promising target in the pursuit of mitigating S. aureus infections. This enzyme catalyzes the formation of tetrahydrofolate (THF) through the reduction of Dihydrofolate (DHF) in the presence of nicotinamide adenine dinucleotide phosphate (NADPH). Diaminopyrimidines (DAPs), such as trimethoprim (TMP), a bacterial infection treatment, which is target DHFR. DAP DHFR inhibitors have been used therapeutically for more over 30 years, and resistance to these drugs has grown prevalent. Their wide range of cellular functions make them ideal targets for antimicrobial agents. Due to the broad scope of their cellular functions, the task of developing analogues that can selectively overcome the resistance conferred by DHFR enzymes from Halopitys Incurvus Algaen's Bromophenol Compound has posed a significant challenge. To identify the compound's drug-like properties, a pre-filtering process was conducted utilizing the Lipinski rule of five in computational analysis. Subsequently, a molecular docking study was carried out using the Chimera#AutoDock Vina 1.17.3 program between the synthetic compound and DHFR (PDB ID: 2W9S) of S. aeurus and TMP was used as control. The results demonstrated that the synthetic compound displayed a favorable binding affinity with the active site of the target protein DHFR enzyme (PDB ID: 2W9S) similar to the control (TMP).

Keywords

• Staphylococcus aeurus
• Bromophenol
• Staphylococcus aeurus
• DHFR
• Molecular Docking
• Molecular Dynamic

References

1. Alqahtani, S. (2017). In silico ADME-Tox modeling: progress and prospects. Expert opinion on drug metabolism & toxicology, 13(11), 1147-1158.
2. Bakchi, B., Krishna, A. D., Sreecharan, E., Ganesh, V. B. J., Niharika, M., Maharshi, S., ... & Shaik, A. B. (2022). An overview on applications of SwissADME web tool in the design and development of anticancer, antitubercular and antimicrobial agents: A medicinal chemist's perspective. Journal of Molecular Structure, 1259, 132712.
3. Bietz, S., Urbaczek, S., Schulz, B., & Rarey, M. (2014). Protoss: a holistic approach to predict tautomers and protonation states in protein-ligand complexes. Journal of cheminformatics, 6, 1-12.
4. Bitew, M., Desalegn, T., Demissie, T. B., Belayneh, A., Endale, M., & Eswaramoorthy, R. (2021). Pharmacokinetics and drug-likeness of antidiabetic flavonoids: Molecular docking and DFT study. Plos one, 16(12), e0260853.
5. Bourne, C. R. (2014). Utility of the biosynthetic folate pathway for targets in antimicrobial discovery. Antibiotics, 3(1), 1-28.
6. Chan, Y. G., Frankel, M. B., Dengler, V., Schneewind, O., & Missiakas, D. (2013). Staphylococcus aureus mutants lacking the LytR-CpsA-Psr family of enzymes release cell wall teichoic acids into the extracellular medium. Journal of bacteriology, 195(20), 4650-4659.
7. Charest, M. G., Lerner, C. D., Brubaker, J. D., Siegel, D. R., & Myers, A. G. (2005). A convergent enantioselective route to structurally diverse 6-deoxytetracycline antibiotics. Science, 308(5720), 395-398.
8. Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific reports, 7(1), 42717.

9. Dong, H., Dong, S., Erik Hansen, P., Stagos, D., Lin, X., & Liu, M. (2020). Progress of bromophenols in marine algae from 2011 to 2020: Structure, bioactivities, and applications. Marine drugs, 18(8), 411.
10. Ertl, P., & Schuffenhauer, A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminformatics, 1, 1-11.
11. Fisher, J. F., & Mobashery, S. (2020). β-Lactams against the Fortress of the Gram-Positive Staphylococcus aureus Bacterium. Chemical reviews, 121(6), 3412-3463.
12. Gfeller, D., Grosdidier, A., Wirth, M., Daina, A., Michielin, O., & Zoete, V. (2014). SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic acids research, 42(W1), W32-W38.
13. Ghosh, S., Das, S., Ahmad, I., & Patel, H. (2021). In silico validation of anti-viral drugs obtained from marine sources as a potential target against SARS-CoV-2 Mpro. Journal of the Indian Chemical Society, 98(12), 100272.
14. Hao, D. C., Ge, G. B., Xiao, P. G., Wang, P., & Yang, L. (2015). Drug metabolism and pharmacokinetic diversity of ranunculaceae medicinal compounds. Current drug metabolism, 16(4), 294-321. Hollingsworth, S. A., & Dror, R. O. (2018). Molecular dynamics simulation for all. Neuron, 99(6), 1129-1143.
15. Heaslet, H., Harris, M., Fahnoe, K., Sarver, R., Putz, H., Chang, J., ... & Miller, J. R. (2009). Structural comparison of chromosomal and exogenous dihydrofolate reductase from Staphylococcus aureus in complex with the potent inhibitor trimethoprim. Proteins: Structure, Function, and Bioinformatics, 76(3), 706-717.
16. Kakkassery, J. T., Raphael, V. P., & Johnson, R. (2021). In vitro antibacterial and in silico docking studies of two Schiff bases on Staphylococcus aureus and its target proteins. Future Journal of Pharmaceutical Sciences, 7(1), 1-9.
17. Karplus, M., & McCammon, J. A. (2002). Molecular dynamics simulations of biomolecules. Nature structural biology, 9(9), 646-652.
18. Kerwin, S. M. (2010). ChemBioOffice ultra 2010 suite.
19. Kirar, M., Singh, H., & Sehrawat, N. (2022). Virtual screening and molecular dynamics simulation study of plant protease inhibitors against SARS-CoV-2 envelope protein. Informatics in Medicine Unlocked, 30, 100909.
20. Kretschmer, T. (2012). Information and communication technologies and productivity growth: A survey of the literature.
21. Kumar, B. K., Sekhar, K. V. G., Kunjiappan, S., Jamalis, J., Balaña-Fouce, R., & Sankaranarayanan, M. (2021). Recent Update on the Anti-infective Potential of β-carboline Analogs. Mini reviews in medicinal chemistry, 21(4), 398-425.
22. Lee, J. K., Banerjee, S., Nam, H. G., & Zare, R. N. (2015). Acceleration of reaction in charged microdroplets. Quarterly reviews of biophysics, 48(4), 437-444.
23. Li, C., Feng, J., Liu, S., & Yao, J. (2022). A novel molecular representation learning for molecular property prediction with a multiple SMILES-based augmentation. Computational Intelligence and Neuroscience, 2022.
24. Lipinski, C. A. L. F. (2002). Poor aqueous solubility—an industry wide problem in drug discovery. Am. Pharm. Rev, 5(3), 82-85.
25. Muegge, I., Heald, S. L., & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of medicinal chemistry, 44(12), 1841-1846.
26. Nguyen, N. T., Nguyen, T. H., Pham, T. N. H., Huy, N. T., Bay, M. V., Pham, M. Q., ... & Ngo, S. T. (2019). Autodock vina adopts more accurate binding poses but autodock4 forms better binding affinity. Journal of Chemical Information and Modeling, 60(1), 204-211.
27. Nittinger, E., Inhester, T., Bietz, S., Meyder, A., Schomburg, K. T., Lange, G., ... & Rarey, M. (2017). Large-scale analysis of hydrogen bond interaction patterns in protein–ligand interfaces. Journal of Medicinal Chemistry, 60(10), 4245-4257.
28. Paulsen, P. A., Jurkowski, W., Apostolov, R., Lindahl, E., Nissen, P., & Poulsen, H. (2013). The C-terminal cavity of the Na, K-ATPase analyzed by docking and electrophysiology. Molecular membrane biology, 30(2), 195-205.
29. Prakash, P., Vijayasarathi, D., Selvam, K., Karthi, S., & Manivasagaperumal, R. (2021). Pharmacore maping based on docking, ADME/toxicity, virtual screening on 3, 5-dimethyl-1, 3, 4-hexanetriol and dodecanoic acid derivates for anticancer inhibitors. Journal of Biomolecular Structure and Dynamics, 39(12), 4490-4500.
30. Protti, Í. F., Rodrigues, D. R., Fonseca, S. K., Alves, R. J., de Oliveira, R. B., & Maltarollo, V. G. (2021). Do Drug‐likeness Rules Apply to Oral Prodrugs?. ChemMedChem, 16(9), 1446-1456. Qing, X., Yin Lee, X., De Raeymaeker, J., RH Tame, J., YJ Zhang, K., De Maeyer, M., & RD Voet, A. (2014). Pharmacophore modeling: advances, limitations, and current utility in drug discovery. Journal of Receptor, Ligand and Channel Research, 81-92.
31. Rezai, M., Bayrak, C., Taslimi, P., GÜLÇİN, İ., & Menzek, A. (2018). The first synthesis and antioxidant and anticholinergic activities of 1-(4, 5-dihydroxybenzyl) pyrrolidin-2-one derivative bromophenols including natural products. Turkish journal of chemistry, 42(3), 808-825.
32. Sanapalli, B. K. R., Yele, V., Jupudi, S., & Karri, V. V. S. R. (2021). Ligand-based pharmacophore modeling and molecular dynamic simulation approaches to identify putative MMP-9 inhibitors. RSC advances, 11(43), 26820-26831.
33. Seiple, I. B., Zhang, Z., Jakubec, P., Langlois-Mercier, A., Wright, P. M., Hog, D. T., ... & Myers, A. G. (2016). A platform for the discovery of new macrolide antibiotics. Nature, 533(7603), 338-345.
34. Thakkar, A., Chadimová, V., Bjerrum, E. J., Engkvist, O., & Reymond, J. L. (2021). Retrosynthetic accessibility score (RAscore)–rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chemical Science, 12(9), 3339-3349.
35. Velez, L. A., Delgado, Y., Ferrer-Acosta, Y., Suárez-Arroyo, I. J., Rodríguez, P., & Pérez, D. (2022). Theoretical Calculations and Analysis Method of the Physicochemical Properties of Phytochemicals to Predict Gastrointestinal Absorption.
36. Waglechner, Nicholas, and Gerard D. Wright. "Antibiotic resistance: it’s bad, but why isn’t it worse?." BMC biology 15.1 (2017): 1-8.
37. Wang, N. N., Dong, J., Deng, Y. H., Zhu, M. F., Wen, M., Yao, Z. J., ... & Cao, D. S. (2016). ADME properties evaluation in drug discovery: prediction of Caco-2 cell permeability using a combination of NSGA-II and boosting. Journal of chemical information and modeling, 56(4), 763-773.
38. Zadorozhnii, P. V., Kiselev, V. V., & Kharchenko, A. V. (2022). In silico ADME profiling of salubrinal and its analogues. Future Pharmacology, 2(2), 160-197.