Generic placeholder image

Current Topics in Medicinal Chemistry


ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Research Article

In Silico Studies for Bacterystic Evaluation against Staphylococcus aureus of 2-Naphthoic Acid Analogues

Author(s): Alex France Messias Monteiro, Marcus Tullius Scotti *, Alejandro Speck-Planche, Renata Priscila Costa Barros and Luciana Scotti

Volume 20, Issue 4, 2020

Page: [293 - 304] Pages: 12

DOI: 10.2174/1568026619666191206111742

Price: $65


Background: Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas.

Aim: This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools.

Methods: A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG).

Results: Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus.

Conclusion: It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.

Keywords: Staphylococcus aureus, 2-Naphthoic Acid Analogues, KNIME Analytics Platform, Lipinski rule, Cytotoxicity risks, Phenolic compounds.

Graphical Abstract
World Health Organizairon. Antimicrobial Resistance: Global Report on Surveillance; WHO , 2014.
Stryjewski, M.E.; Chambers, H.F. Skin and soft-tissue infections caused by community-acquired methicillin-resistant staphylococcus aureus. Clin. Infect. Dis., 2008, 46(5), S368-S377.
Wolcott, R.D.; Rhoads, D.D.; Bennett, M.E.; Wolcott, B.M.; Gogokhia, L.; Costerton, J.W.; Dowd, S.E. Chronic wounds and the medical biofilm paradigm. J. Wound Care, 2010, 19(2), 45-46, 48-50, 52-53.
[] [PMID: 20216488]
van Hal, S.J.; Jensen, S.O.; Vaska, V.L.; Espedido, B.A.; Paterson, D.L.; Gosbell, I.B. Predictors of mortality in Staphylococcus aureus Bacteremia. Clin. Microbiol. Rev., 2012, 25(2), 362-386.
[] [PMID: 22491776]
Peacock, S.J.; Paterson, G.K. Mechanisms of Methicillin Resistance in Staphylococcus aureus. Annu. Rev. Biochem., 2015, 84, 577-601.
[] [PMID: 26034890]
Mimica, M.J.; Berezin, E.N. Vancomycin-resistant Staphylococcus aureus: an emerging problem. Arq. Med. Hosp. Fac. Cienc. Med. Santa Casa São Paulo, 2018, 51(2), 52-56.
WHO. Global Antimicrobial Resistance Surveillance System (GLASS) Report: Early Implementation 2016-2017. In: Global antimicrobial resistance surveillance system (GLASS) report: early implementation 2016-2017; Geneva: World Health Organization, 2017.
Morgan, D.J.; Murthy, R.; Munoz-Price, L.S.; Barnden, M.; Camins, B.C.; Johnston, B.L.; Rubin, Z.; Sullivan, K.V.; Shane, A.L.; Dellinger, E.P.; Rupp, M.E.; Bearman, G. Reconsidering contact precautions for endemic methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus. Infect. Control Hosp. Epidemiol., 2015, 36(10), 1163-1172.
[] [PMID: 26138329]
Otto, M. Basis of virulence in community-associated methicillin-resistant Staphylococcus aureus. Annu. Rev. Microbiol., 2010, 64, 143-162.
[] [PMID: 20825344]
Chambers, H.F.; Deleo, F.R. Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat. Rev. Microbiol., 2009, 7(9), 629-641.
[] [PMID: 19680247]
DeLeo, F.R.; Chambers, H.F. Reemergence of antibiotic-resistant Staphylococcus aureus in the genomics era. J. Clin. Invest., 2009, 119(9), 2464-2474.
[] [PMID: 19729844]
Hsu, L-Y.; Harris, S.R.; Chlebowicz, M.A.; Lindsay, J.A.; Koh, T-H.; Krishnan, P.; Tan, T-Y.; Hon, P-Y.; Grubb, W.B.; Bentley, S.D.; Parkhill, J.; Peacock, S.J.; Holden, M.T. Evolutionary dynamics of methicillin-resistant Staphylococcus aureus within a healthcare system. Genome Biol., 2015, 16(1), 81.
[] [PMID: 25903077]
Centers of Disease Control and Prevention (CDC). Biggest Threats and Data, Available at:.
Assis, L.M.; Nedeljković, M.; Dessen, A. New strategies for targeting and treatment of multi-drug resistant Staphylococcus aureus. Drug Resist. Updat., 2017, 31, 1-14.
[] [PMID: 28867240]
Baig, M.H.; Ahmad, K.; Roy, S.; Ashraf, J.M.; Adil, M.; Siddiqui, M.H.; Khan, S.; Kamal, M.A.; Provazník, I.; Choi, I. computer aided drug design: success and limitations. Curr. Pharm. Des., 2016, 22(5), 572-581.
[] [PMID: 26601966]
Lu, P.; Bevan, D.R.; Leber, A.; Hontecillas, R.; Tubau-Juni, N.; Bassaganya-Riera, J. Computer-aided drug discovery. In: Accelerated Path to Cures; Elsevier: Amsterdam, 2018; pp. 7-24.
Harak, S.S.; Mali, D.R.; Amrutkar, S.V. Computer aided drug design. IJSRST, 2017, 3(10), 118-120.
Dearden, J.C. The history and development of quantitative structure-activity relationships (QSARs). Int. J. Quant. Struct. Relationships, 2016, 1(1), 1-44.
Marković, Z.; Filipović, M.; Manojlović, N.; Amić, A.; Jeremić, S.; Milenković, D. QSAR of the free radical scavenging potency of selected hydroxyanthraquinones. Chem. Pap., 2018, 72(42), 2785-2793.
dos Santos, R.N.; Ferreira, L.G.; Andricopulo, A.D. Practices in Molecular Docking and Structure-Based Virtual Screening BT - Computational Drug Discovery and Design; Gore, M; Jagtap, U.B., Ed.; Springer: New York, 2018, pp. 31-50.
Azad, C.S.; Bhunia, S.S.; Krishna, A.; Shukla, P.K.; Saxena, A.K. Novel glycoconjugate of 8-fluoro norfloxacin derivatives as gentamicin-resistant staphylococcus aureus inhibitors: synthesis and molecular modelling studies. Chem. Biol. Drug Des., 2015, 86(4), 440-446.
[] [PMID: 25546316]
P, Csizmadia. MarvinSketch and MarvinView: molecule applets for the World Wide Web; Proceedings of ECSOC-3, The third international electronic conference on synthetic organic chemistry Basel. Switzerland, September 1-30, 1999.
HyperChem. http//www. hyper.com2002.
Campos, L. V. B.; de, ; Correia, J. C. G.; Carauta, A. N. M. Study of the interaction of triethoxysilane with linoleic acid as a water repellent in ornamental rocks via molecular modeling. 2017.
Moreira, M. P. New cross-linked cross-linked glycerophosphoric acid / beta-cyclodextrin polymers: preparation and incorporation of ciprofloxacin. 2017.
Barros, R.P.C. Virtual screening of secondary metabolites of the genus Solanum with potential antimicrobial activity. Rev. Bras. Farmacogn., 2017, 28(6), 686-691.
Altê, M. A. Structural study and design of new inhibitors for Mycobacterium tuberculosis enzyme prefenate dehydratase., 2017.
Santana, C.B. Chemical composition, antimicrobial activity, insecticide and antioxidant of essential oil and extracts of Myrcia oblongata DC, 2017.
Pereira, J.C. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model., 2017, 56(12), 2495-2506.
Silva, H. Virtual screening of compounds from Brazilian biodiversity plants, with potential inhibitory activity of human alpha-amylase enzymes, 2017.
Raj, B.V.; Rao, M.V.R.; Acharya, Y. Structure based virtual screening, docking and molecular dynamic simulation studies to identify potent mdm2-p53 inhibitors: future implications for cancer therapy. Acta Medica Int., 2017, 4(1), 11.
Berenger, F.; Vu, O.; Meiler, J. Consensus queries in ligand-based virtual screening experiments. J. Cheminform., 2017, 9(1), 60.
[] [PMID: 29185065]
Fereidoonnezhad, M.; Mostoufi, A.; Eskandari, M.; Zali, S.; Aliyan, F. Multitarget drug design, molecular docking and PLIF studies of novel tacrine-coumarin hybrids for the treatment of alzheimer’s disease. Iran. J. Pharm. Res., 2018, 17(4), 1217-1228.
[PMID: 30568682]
Piccirillo, E.; do Amaral, A.T. Virtual screening of bioactive compounds: concepts and aplications. Quim. Nova, 2018, 41(6), 662-677.
Tuncbilek, M.; Kucukdumlu, A.; Guven, E.B.; Altiparmak, D.; Cetin-Atalay, R. Synthesis of novel 6-substituted amino-9-(β-d-ribofuranosyl)purine analogs and their bioactivities on human epithelial cancer cells. Bioorg. Med. Chem. Lett., 2018, 28(3), 235-239.
[] [PMID: 29326016]
Harika, M.S.; Kumar, T.R.; Reddy, L.S.S. Docking studies of benzimidazole derivatives using hex 8.0. Int. J. Pharm. Sci. Res., 2017, 8(4), 1677.
Marnolia, A.; Toepak, E.P.; Siregar, S.; Kerami, D.; Tambunan, U.S.F. Computational screening of flavonoid based inhibitor targeting denv NS5 methyltransferase.AIP Conference Proceedings; AIP Publishing: Maryland, 2018, Vol. 2023, p. 20070.
Nowotka, M.M.; Gaulton, A.; Mendez, D.; Bento, A.P.; Hersey, A.; Leach, A. Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery. Expert Opin. Drug Discov., 2017, 12(8), 757-767.
[PMID: 28602100]
de Sousa, J.M.A. Descriptors generation using the CDK toolkit and web Services; Tutorials in Chemoinformatics, 2017, pp. 127-134.
Pereira, G.; Szwarc, B.; Mondragao, M.A.; Lima, P.A.; Pereira, F.A. Ligand-based approach to the discovery of lead-like po tassium channel KV 1.3 inhibitors. ChemistrySelect, 2018, 3(5), 1352-1364.
Marques, L.C.; Ulson, J.A.C. A Application of deep neural networks for detection and classification of weed plants and their art. Electron. J. Grad., 2018, 11(01), 391-403.
Lorenzo, V.P.; Alves, M.F.; Scotti, L.; Dos Santos, S.G.; de Fatima Formiga Melo Diniz, M.; Scotti, M.T. Computational chemistry study of natural alkaloids and homemade databank to predict inhibitory potential against key enzymes in neurodegenerative diseases. Curr. Top. Med. Chem., 2017, 17(26), 2926-2934.
[] [PMID: 28828994]
Kumari, M.; Tiwari, N.; Subbarao, N.; Chandra, S. Evaluation of predictive models based on random forest, decision tree and support vector machine classifiers and virtual screening of anti-mycobacterial compounds. Int. J. Comput. Biol. Drug Des., 2017, 10(3), 248-263.
Amin, S.A.; Adhikari, N.; Gayen, S.; Jha, T. First report on the structural exploration and prediction of new BPTES analogs as glutaminase inhibitors. J. Mol. Struct., 2017, 1143, 49-64.
Varma, P.B.S.; Adimulam, Y.B.; Subrahmanyam, K. In silico virtual screening of pubchem compounds against phosphotransacetylase, a putative drug target for staphylococcus Aureus. Int. J. Comput. Biol. Drug Des., 2017, 10(1), 39-48.
Wu, Y-Y.; Zhang, T-Y.; Zhang, M-Y.; Cheng, J.; Zhang, Y-X. An endophytic Fungi of Ginkgo biloba L. produces antimicrobial metabolites as potential inhibitors of FtsZ of Staphylococcus aureus. Fitoterapia, 2018, 128, 265-271.
[] [PMID: 29864480]
Ali, S.E.; Chehri, K.; Karimi, N.; Karimi, I. Computational approaches to the in vitro antibacterial activity of allium hirtifolium boiss against gentamicin-resistant escherichia coli: focus on ribosome recycling factor. Silico. Pharmacol., 2017, 5(1), 7.
Fathima, M.Z.; Shanmugarajan, T.S.; Kumar, S.S.; Yadav, B.V.V.N. Comparative in silico docking studies of hinokitiol with sorafenib and nilotinib against proto-oncogene tyrosine-protein kinase (abl1) and mitogen-activated protein kinase (mapk) to target hepatocellular carcinoma. Res. J. Pharm. Technol., 2017, 10(1), 257.
de Ávila, M.B.; Xavier, M.M.; Pintro, V.O.; de Azevedo, W.F., Jr Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem. Biophys. Res. Commun., 2017, 494(1-2), 305-310.
[] [PMID: 29017921]
Monteiro, A.; Luna, I.; Scotti, M.; Scotti, L. In silico analysis of cytotoxicity, rate of absorption and molecular docking of natural products against protease, integrase and HIV-1 reverse transcriptase; Proceedings of MOL2NET 2018, international conference on multidisciplinary sciences.. MDPI: Basel, Switzerland, 2018, p. 5539.
Tan, C. M.; Therien, A. G.; Lu, J.; Lee, S. H.; Caron, A.; Gill, C. J.; Lebeau-Jacob, C.; Benton-Perdomo, L.; Monteiro, J. M.; Pereira, P. M. Restoring methicillin-resistant staphylococcus aureus susceptibility to beta-lactam antibiotics., Sci. Transl. Med., 2012, 4, 126ra35-126ra35.
Ting, Y.T.; Harris, P.W.; Batot, G.; Brimble, M.A.; Baker, E.N.; Young, P.G. Peptide binding to a bacterial signal peptidase visualized by peptide tethering and carrier-driven crystallization. IUCrJ, 2016, 3(Pt 1), 10-19.
[] [PMID: 26870377]
Loo, J.S.E.; Emtage, A.L.; Ng, K.W.; Yong, A.S.J.; Doughty, S.W. Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: Binding mode prediction and docking enrichment. J. Mol. Graph. Model., 2018, 80, 38-47.
[] [PMID: 29306746]
Ounthaisong, U.; Tangyuenyongwatana, P. Cross docking study of flavanoids against tyrosinase enzymes using PyRx 0.8 virtual screening tool. TJPS, 2017, 2017, 41.
Yanuar, A.; Pratiwi, I.; Syahdi, R.R. In silico activity analysis of saponins and 2, 5-piperazinedione from marine organism against murine double minute-2 inhibitor and procaspase-3 activator. J. Young Pharm., 2018, 10(2), S16.
Wang, T.; Yang, Z.; Zhang, Y.; Yan, W.; Wang, F.; He, L.; Zhou, Y.; Chen, L. Discovery of novel CDK8 inhibitors using multiple crystal structures in docking-based virtual screening. Eur. J. Med. Chem., 2017, 129, 275-286.
[] [PMID: 28231524]
Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015, 1-2, 19-25.
Berendsen, H.J.C.; van der Spoel, D.; van Drunen, R. GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun., 1995, 91(1), 43-56.
Schüttelkopf, A.W.; van Aalten, D.M.F. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. D Biol. Crystallogr., 2004, 60(Pt 8), 1355-1363.
[] [PMID: 15272157]
Piggot, T.J.; Piñeiro, Á.; Khalid, S. Correction to molecular dynamics simulations of phosphatidylcholine membranes: a comparative force field study. J. Chem. Theory Comput., 2017, 13(4), 1862-1865.
[] [PMID: 28301153]
Carballo-Pacheco, M.; Ismail, A.E.; Strodel, B. On the applicability of force fields to study the aggregation of amyloidogenic peptides using molecular dynamics Simulations. J. Chem. Theory Comput., 2018, 14(11), 6063-6075.
[] [PMID: 30336669]
Bondi, A. Van der waals volumes and radii. J. Phys. Chem., 1964, 68(3), 441-451.
Huang, B.; Lou, Y.; Li, T.; Lin, Z.; Sun, S.; Yuan, Y.; Liu, C.; Gu, Y. Molecular dynamics simulations of adsorption and desorption of bone morphogenetic protein-2 on textured hydroxyapatite surfaces. Acta Biomater., 2018, 80, 121-130.
[] [PMID: 30223095]
Dong, Y.W.; Liao, M.L.; Meng, X.L.; Somero, G.N. Structural flexibility and protein adaptation to temperature: Molecular dynamics analysis of malate dehydrogenases of marine molluscs. Proc. Natl. Acad. Sci. USA, 2018, 115(6), 1274-1279.
[] [PMID: 29358381]
Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera--a visualization system for exploratory research and analysis. J. Comput. Chem., 2004, 25(13), 1605-1612.
[] [PMID: 15264254]
Goddard, T.D.; Huang, C.C.; Meng, E.C.; Pettersen, E.F.; Couch, G.S.; Morris, J.H.; Ferrin, T.E. UCSF ChimeraX: Meeting modern challenges in visualization and analysis. Protein Sci., 2018, 27(1), 14-25.
[] [PMID: 28710774]
Boughorbel, S.; Jarray, F.; El-Anbari, M. Optimal classifier for imbalanced data using matthews correlation coefficient metric. PLoS One, 2017, 12(6)e0177678
[] [PMID: 28574989]
Danielson, M.L.; Sawada, G.A.; Raub, T.J.; Desai, P.V. In silico and in vitro assessment of OATP1B1 inhibition in drug discovery. Mol. Pharm., 2018, 15(8), 3060-3068.
[] [PMID: 29927611]
Razzaghi-Asl, N.; Mirzayi, S.; Mahnam, K.; Sepehri, S. Identification of COX-2 inhibitors via structure-based virtual screening and molecular dynamics simulation. J. Mol. Graph. Model., 2018, 83, 138-152.
[] [PMID: 29936228]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy