Login Register Cart 0
Generic placeholder image

Current Bioactive Compounds

Editor-in-Chief

ISSN (Print): 1573-4072
ISSN (Online): 1875-6646

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Early Blockage of Mycobacterium Tuberculosis Cell-wall Synthesis via EchA\6 Inhibition to Overcome Resistance Strain: Insights from Umbrella Sampling Simulations

Author(s): Rafee Habib Askandar, Farhad Sharifi, Sepideh Shayan, Helya Mohammadi, Arian Rahimi, Noeman Ardalan* and Heshw Farhad Mohammed

Volume 19, Issue 10, 2023

Published on: 27 June, 2023

Article ID: e140623218016 Pages: 16

DOI: 10.2174/1573407219666230614163801

Price: $65

conference banner
Abstract

Background: Tuberculosis (TB) has long been the major infectious cause of mortality, ranking higher than HIV/AIDS as the most common cause of death from a single infectious agent worldwide. The EchA6 target of mycobacteria plays a vital role in synthesizing an important component of the mycobacterial outer membrane. The failure of TB treatment has prompted the investigation of novel anti-tubercular drugs.

Objective: This study was aimed at blockage of Mycobacterium tuberculosis cell-wall synthesis via EchA6 inhibition to overcome resistance strain.

Methods: Over 3,000,000 compounds and GSK951A (positive control) were investigated as the inhibitors in this study. The GROMACS molecular dynamic package was used to analyze the protein- inhibitor complex's conformational changes under constant temperature and pressure. Also, umbrella sampling (US) was used for free binding energy (ΔG) calculation.

Results: Four compounds were chosen for the docking investigation. According to the MD analysis, the studied inhibitors demonstrated good stability and flexibility. According to ΔG obtained from US, the ΔG of GSK951A, ZINC11815220, ZINC67770050, ZINC55048326, and ZINC89700914 were -6.14 kcal mol-1, -5.25 kcal mol-1, -10.19 kcal mol-1, -8.55 kcal mol-1, and -8.37 kcal mol-1, respectively.

Conclusion: In conclusion, ZINC67770050 is recommended for further study in the laboratory. This investigation is an important starting point for discovering anti-tubercular drugs using EchA6 inhibition.<.p>

Keywords: Tuberculosis, EchA6 inhibitor, molecular docking, molecular dynamics simulation, umbrella sampling, Mycobacterium tuberculosis.

Graphical Abstract

[1]
Brites, D.; Gagneux, S. Co-evolution of Mycobacterium tuberculosis and Homo sapiens. Immunol. Rev., 2015, 264(1), 6-24.
[http://dx.doi.org/10.1111/imr.12264] [PMID: 25703549]
[2]
Debnath, S.; Nath, M.; Sarkar, A.; Roy, G.; Chakraborty, S.K.; Debnath, B. Phytochemical characterization of Styrax benzoin resin extract, molecular docking, ADME, and antibacterial activity study. Nat. Prod. Res., 2022, 1-6.
[http://dx.doi.org/10.1080/14786419.2022.2132244] [PMID: 36214683]
[3]
Remm, S.; Earp, J.C.; Dick, T.; Dartois, V.; Seeger, M.A. Critical discussion on drug efflux in Mycobacterium tuberculosis. FEMS Microbiol. Rev., 2022, 46(1), fuab050.
[http://dx.doi.org/10.1093/femsre/fuab050] [PMID: 34637511]
[4]
Hadifar, S.; Fateh, A.; Pourbarkhordar, V.; Siadat, S.D.; Mostafaei, S.; Vaziri, F. Variation in Mycobacterium tuberculosis population structure in Iran: A systemic review and meta-analysis. BMC Infect. Dis., 2021, 21(1), 2.
[http://dx.doi.org/10.1186/s12879-020-05639-7] [PMID: 33397308]
[5]
Ifijen, I.H.; Atoe, B.; Ekun, R.O.; Ighodaro, A.; Odiachi, I.J. Treatments of Mycobacterium tuberculosis and Toxoplasma gondii with Selenium nanoparticles. Bionanoscience, 2023, 13(1), 249-277.
[http://dx.doi.org/10.1007/s12668-023-01059-4] [PMID: 36687337]
[6]
Amir, A.; Rana, K.; Arya, A.; Kapoor, N.; Kumar, H.; Siddiqui, M.A. Mycobacterium tuberculosis H37Rv: In silico drug targets identification by metabolic pathways analysis. Int. J. Evol. Biol., 2014, 2014, 1-8.
[http://dx.doi.org/10.1155/2014/284170] [PMID: 24719775]
[7]
Gygli, S.M.; Loiseau, C.; Jugheli, L.; Adamia, N.; Trauner, A.; Reinhard, M.; Ross, A.; Borrell, S.; Aspindzelashvili, R.; Maghradze, N.; Reither, K.; Beisel, C.; Tukvadze, N.; Avaliani, Z.; Gagneux, S. Prisons as ecological drivers of fitness-compensated multidrug-resistant Mycobacterium tuberculosis. Nat. Med., 2021, 27(7), 1171-1177.
[http://dx.doi.org/10.1038/s41591-021-01358-x]
[8]
Alame Emane, A.K.; Guo, X.; Takiff, H.E.; Liu, S. Drug resistance, fitness and compensatory mutations in Mycobacterium tuberculosis. Tuberculosis , 2021, 129, 102091.
[http://dx.doi.org/10.1016/j.tube.2021.102091] [PMID: 34090078]
[9]
Bhagwat, A.; Deshpande, A.; Parish, T. How Mycobacterium tuberculosis drug resistance has shaped anti-tubercular drug discovery. Front. Cell. Infect. Microbiol., 2022, 12, 974101.
[http://dx.doi.org/10.3389/fcimb.2022.974101] [PMID: 36159638]
[10]
Rudraraju, R.S.; Daher, S.S.; Gallardo-Macias, R.; Wang, X.; Neiditch, M.B.; Freundlich, J.S. Mycobacterium tuberculosis KasA as a drug target: Structure-based inhibitor design. Front. Cell. Infect. Microbiol., 2022, 12, 1008213.
[http://dx.doi.org/10.3389/fcimb.2022.1008213] [PMID: 36189349]
[11]
Abrahams, K.A.; Besra, G.S. Synthesis and recycling of the mycobacterial cell envelope. Curr. Opin. Microbiol., 2021, 60, 58-65.
[http://dx.doi.org/10.1016/j.mib.2021.01.012] [PMID: 33610125]
[12]
Cox, J.A.G. Drug development: The cell wall as a drug target. Int. J. Mycobacteriol., 2016, 5(5)(Suppl. 1), S156.
[http://dx.doi.org/10.1016/j.ijmyco.2016.09.012] [PMID: 28043523]
[13]
Cox, J.A.G.; Abrahams, K.A.; Alemparte, C.; Ghidelli-Disse, S.; Rullas, J.; Angulo-Barturen, I.; Singh, A.; Gurcha, S.S.; Nataraj, V.; Bethell, S.; Remuiñán, M.J.; Encinas, L.; Jervis, P.J.; Cammack, N.C.; Bhatt, A.; Kruse, U.; Bantscheff, M.; Fütterer, K.; Barros, D.; Ballell, L.; Drewes, G.; Besra, G.S. THPP target assignment reveals EchA6 as an essential fatty acid shuttle in mycobacteria. Nat. Microbiol., 2016, 1(2), 15006.
[http://dx.doi.org/10.1038/nmicrobiol.2015.6] [PMID: 27571973]
[14]
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.
[http://dx.doi.org/10.1002/jcc.20084] [PMID: 15264254]
[15]
Gasteiger, J.; Marsili, M. Iterative partial equalization of orbital electronegativity-a rapid access to atomic charges. Tetrahedron, 1980, 36(22), 3219-3228.
[http://dx.doi.org/10.1016/0040-4020(80)80168-2]
[16]
Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[17]
Honmore, V.S.; Natu, A.D.; Khedkar, V.M.; Arkile, M.A.; Sarkar, D.; Rojatkar, S.R. Two antibacterial spiro compounds from the roots of Artemisia pallens wall: Evidence from molecular docking. Nat. Prod. Res., 2022, 36(10), 2465-2472.
[http://dx.doi.org/10.1080/14786419.2021.1902325] [PMID: 33749414]
[18]
Schöning-Stierand, K.; Diedrich, K.; Fährrolfes, R.; Flachsenberg, F.; Meyder, A.; Nittinger, E.; Steinegger, R.; Rarey, M. ProteinsPlus: Interactive analysis of protein-ligand binding interfaces. Nucleic Acids Res., 2020, 48(W1), W48-W53.
[http://dx.doi.org/10.1093/nar/gkaa235] [PMID: 32297936]
[19]
Sherafatizangeneh, M.; Farshadfar, C.; Mojahed, N.; Noorbakhsh, A.; Ardalan, N. Blockage of the Monoamine oxidase by a natural compound to overcome Parkinson’s disease via computational biology. J. Comput. Biophys. Chem., 2022, 21(3), 373-387.
[http://dx.doi.org/10.1142/S2737416522500156]
[20]
Ragab, A.E.; Badawy, E.T.; Aboukhatwa, S.M.; Abdel-Aziz, M.M.; Kabbash, A.; Abo Elseoud, K.A. Isonicotinic acid N -oxide, from isoniazid biotransformation by Aspergillus niger, as an InhA inhibitor antituberculous agent against multiple and extensively resistant strains supported by in silico docking and ADME prediction. Nat. Prod. Res., 2023, 37(10), 1687-1692.
[http://dx.doi.org/10.1080/14786419.2022.2103695] [PMID: 35876096]
[21]
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., 2017, 7(1), 42717.
[http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]
[22]
Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem., 2005, 26(16), 1701-1718.
[http://dx.doi.org/10.1002/jcc.20291] [PMID: 16211538]
[23]
Sousa da Silva, A.W.; Vranken, W.F. ACPYPE-Antechamber python parser interface. BMC Res. Notes, 2012, 5(1), 367.
[http://dx.doi.org/10.1186/1756-0500-5-367] [PMID: 22824207]
[24]
Enríquez-Mendiola, D.; Téllez-Valencia, A.; Sierra-Campos, E.; Campos-Almazán, M.; Valdez-Solana, M.; Gómez Palacio-Gastélum, M.; Avitia-Domínguez, C. Kinetic and molecular dynamic studies of inhibitors of shikimate dehydrogenase from methicillin-resistant Staphylococcus aureus. Chem. Biol. Drug.Des., 2019, 94(2), cbdd.13532.
[http://dx.doi.org/10.1111/cbdd.13532] [PMID: 31009175]
[25]
Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N ⋅log(N) method for Ewald sums in large systems. J. Chem. Phys., 1993, 98(12), 10089-10092.
[http://dx.doi.org/10.1063/1.464397]
[26]
Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A smooth particle mesh Ewald method. J. Chem. Phys., 1995, 103(19), 8577-8593.
[http://dx.doi.org/10.1063/1.470117]
[27]
Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys., 2007, 126(1), 014101.
[http://dx.doi.org/10.1063/1.2408420] [PMID: 17212484]
[28]
Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys., 1981, 52(12), 7182-7190.
[http://dx.doi.org/10.1063/1.328693]
[29]
Enayatkhani, M.; Hasaniazad, M.; Faezi, S.; Guklani, H.; Davoodian, P.; Ahmadi, N. Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: An in silico study. J. Biomol. Struct. Dyn., 2020, 39, 1-16.
[PMID: 32295479]
[30]
Grant, B.J.; Rodrigues, A.P.C.; ElSawy, K.M.; McCammon, J.A.; Caves, L.S.D. Bio3d: An R package for the comparative analysis of protein structures. Bioinformatics, 2006, 22(21), 2695-2696.
[http://dx.doi.org/10.1093/bioinformatics/btl461] [PMID: 16940322]
[31]
Amadei, A.; Linssen, A.B.M.; Berendsen, H.J.C. Essential dynamics of proteins. Proteins, 1993, 17(4), 412-425.
[http://dx.doi.org/10.1002/prot.340170408] [PMID: 8108382]
[32]
Ndagi, U.; Abdullahi, M.; Hamza, A.N.; Soliman, M.E. An analogue of a kinase inhibitor exhibits subjective characteristics that contribute to its inhibitory activities as a potential anti-cancer candidate: Insights through computational biomolecular modelling of UM-164 binding with lyn protein. RSC Advances, 2020, 10(1), 145-161.
[http://dx.doi.org/10.1039/C9RA07204G] [PMID: 35492550]
[33]
Yesudhas, D.; Anwar, M.A.; Panneerselvam, S.; Durai, P.; Shah, M.; Choi, S. Structural mechanism behind distinct efficiency of Oct4/Sox2 proteins in differentially spaced DNA complexes. PLoS One, 2016, 11(1), e0147240.
[http://dx.doi.org/10.1371/journal.pone.0147240] [PMID: 26790000]
[34]
Kumar, S.; Rosenberg, J.M.; Bouzida, D.; Swendsen, R.H.; Kollman, P.A. THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem., 1992, 13(8), 1011-1021.
[http://dx.doi.org/10.1002/jcc.540130812]
[35]
Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem., 1999, 1(1), 55-68.
[http://dx.doi.org/10.1021/cc9800071] [PMID: 10746014]
[36]
Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45(12), 2615-2623.
[http://dx.doi.org/10.1021/jm020017n] [PMID: 12036371]
[37]
Egan, W.J.; Merz, K.M., Jr; Baldwin, J.J. Prediction of drug absorption using multivariate statistics. J. Med. Chem., 2000, 43(21), 3867-3877.
[http://dx.doi.org/10.1021/jm000292e] [PMID: 11052792]
[38]
Muegge, I.; Heald, S.L.; Brittelli, D. Simple selection criteria for drug-like chemical matter. J. Med. Chem., 2001, 44(12), 1841-1846.
[http://dx.doi.org/10.1021/jm015507e] [PMID: 11384230]
[39]
Mittal, L.; Kumari, A.; Srivastava, M.; Singh, M.; Asthana, S. Identification of potential molecules against COVID-19 main protease through structure-guided virtual screening approach. J. Biomol. Struct. Dyn., 2021, 39(10), 3662-3680.
[40]
Ndombera, F.T. Revisiting cheminformatics and mechanisms of action of chloroquine and Hy-droxychloroquine in targeting COVID-19. J. Bioinfo. Comp. Genom., 2020, 3, 1-11.
[41]
Ahmed, M.C.; Crehuet, R.; Lindorff-Larsen, K. Computing, analyzing, and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins. Methods Mol. Biol., 2020, 2141, 429-445.
[http://dx.doi.org/10.1007/978-1-0716-0524-0_21] [PMID: 32696370]
[42]
Shayan, S.; Jamaran, S.; Askandar, R.H.; Rahimi, A.; Elahi, A.; Farshadfar, C.; Ardalan, N. The SARS-CoV-2 Proliferation blocked by a novel and potent main protease inhibitor via computer-aided drug design. Iran. J. Pharm. Res., 2021, 20(3), 399-418.
[PMID: 34903997]
[43]
Chen, J.; Sawyer, N.; Regan, L. Protein-protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area. Protein Sci., 2013, 22(4), 510-515.
[http://dx.doi.org/10.1002/pro.2230] [PMID: 23389845]
[44]
Siebenmorgen, T.; Zacharias, M. Computational prediction of protein–protein binding affinities. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2020, 10(3), e1448.
[http://dx.doi.org/10.1002/wcms.1448]
[45]
Zhang, D.; Lazim, R. Application of conventional molecular dynamics simulation in evaluating the stability of apomyoglobin in urea solution. Sci. Rep., 2017, 7(1), 44651.
[http://dx.doi.org/10.1038/srep44651] [PMID: 28300210]
[46]
Noorbakhsh, A.; Askandar, R.H.; Alhagh, M.S.; Farshadfar, C.; Seyedi, S.H.; Ahmadizad, M.; Rahimi, A.; Ardalan, N.; Koushki, E.H. Prevention of SARS-CoV-2 proliferation with a novel and potent main protease inhibitor by docking, ADMET, MM-PBSA, and molecular dynamics simulation. J. Comput. Biophys. Chem., 2021, 20(3), 305-322.
[http://dx.doi.org/10.1142/S2737416521500149]
[47]
Koushki, E.H.; Abolghasemi, S.; Mollica, A.; Aghaeepoor, M.; Moosavi, S.S.; Farshadfar, C.; Hasanpour, B.; Feyzi, B.; Abdi, F.; Mirzaie, S. Structure-based virtual screening, molecular docking and dynamics studies of natural product and classical inhibitors against human dihydrofolate reductase. Netw. Model. Anal. Health Inform. Bioinform., 2020, 9(1), 49.
[http://dx.doi.org/10.1007/s13721-020-00244-9]
[48]
Fiser, A.; Sali, A. ModLoop: Automated modeling of loops in protein structures. Bioinformatics, 2003, 19(18), 2500-2501.
[http://dx.doi.org/10.1093/bioinformatics/btg362] [PMID: 14668246]
[49]
Sk, M.F.; Roy, R.; Kar, P. Exploring the potency of currently used drugs against HIV-1 protease of subtype D variant by using multiscale simulations. J. Biomol. Struct. Dyn., 2020, 2020, 1-16.
[PMID: 32000612]
[50]
Kumar, A.; Choudhir, G.; Shukla, S.K.; Sharma, M.; Tyagi, P.; Bhushan, A. Identification of phytochemical inhibitors against main protease of COVID-19 using molecular modeling approaches. J. Biomol. Struct. Dyn., 2021, 39(10), 3760-3770.
[PMID: 32448034]
[51]
Dhankhar, P.; Dalal, V.; Mahto, J.K.; Gurjar, B.R.; Tomar, S.; Sharma, A.K.; Kumar, P. Characterization of dye-decolorizing peroxidase from Bacillus subtilis. Arch. Biochem. Biophys., 2020, 693, 108590.
[http://dx.doi.org/10.1016/j.abb.2020.108590] [PMID: 32971035]
[52]
Panigrahi, S.K.; Desiraju, G.R. Strong and weak hydrogen bonds in the protein-ligand interface. Proteins, 2007, 67(1), 128-141.
[http://dx.doi.org/10.1002/prot.21253] [PMID: 17206656]
[53]
Pyrkov, T.V.; Pyrkova, D.V.; Balitskaya, E.D.; Efremov, R.G. The role of stacking interactions in complexes of proteins with adenine and Guanine fragments of ligands. Acta Nat., 2009, 1(1), 124-127.
[http://dx.doi.org/10.32607/20758251-2009-1-1-124-127] [PMID: 22649598]
[54]
Wu, R.; McMahon, T.B. Investigation of cation-pi interactions in biological systems. J. Am. Chem. Soc., 2008, 130(38), 12554-12555.
[http://dx.doi.org/10.1021/ja802117s] [PMID: 18759391]
[55]
Efremov, R.; Chugunov, A.; Pyrkov, T.; Priestle, J.; Arseniev, A.; Jacoby, E. Molecular lipophilicity in protein modeling and drug design. Curr. Med. Chem., 2007, 14(4), 393-415.
[http://dx.doi.org/10.2174/092986707779941050] [PMID: 17305542]
[56]
Kukić, P.; Nielsen, J.E. Electrostatics in proteins and protein–ligand complexes. Future Med. Chem., 2010, 2(4), 647-666.
[http://dx.doi.org/10.4155/fmc.10.6] [PMID: 21426012]
[57]
Lu, Y.; Wang, Y.; Zhu, W. Nonbonding interactions of organic halogens in biological systems: Implications for drug discovery and biomolecular design. Phys. Chem. Chem. Phys., 2010, 12(18), 4543-4551.
[http://dx.doi.org/10.1039/b926326h] [PMID: 20428531]
[58]
Walsh, J. Mathematica for students: The essential tool for math and science learning: Microsoft Windows user interface guide, 3rd ed; Wolfram Research: Champaign, 1994.
[59]
Khan, M.T.; Khan, A.; Rehman, A.U.; Wang, Y.; Akhtar, K.; Malik, S.I.; Wei, D.Q. Structural and free energy landscape of novel mutations in ribosomal protein S1 (rpsA) associated with pyrazinamide resistance. Sci. Rep., 2019, 9(1), 7482.
[http://dx.doi.org/10.1038/s41598-019-44013-9] [PMID: 31097767]
[60]
Farshadfar, C.; Mollica, A.; Rafii, F.; Noorbakhsh, A.; Nikzad, M.; Seyedi, S.H.; Abdi, F.; Verki, S.A.; Mirzaie, S. Novel potential inhibitor discovery against tyrosyl-tRNA synthetase from Staphylococcus aureus by virtual screening, molecular dynamics, MMPBSA and QMMM simulations. Mol. Simul., 2020, 46(7), 507-520.
[http://dx.doi.org/10.1080/08927022.2020.1726911]
[61]
Rasafar, N.; Barzegar, A.; Mehdizadeh Aghdam, E. Design and development of high affinity dual anticancer peptide-inhibitors against p53-MDM2/X interaction. Life Sci., 2020, 245, 117358.
[http://dx.doi.org/10.1016/j.lfs.2020.117358] [PMID: 32001262]
[62]
Ryde, U.; Söderhjelm, P. Ligand-binding affinity estimates supported by quantum-mechanical methods. Chem. Rev., 2016, 116(9), 5520-5566.
[http://dx.doi.org/10.1021/acs.chemrev.5b00630] [PMID: 27077817]
[63]
Ngo, S.T. Estimating the LIGAND‐BINDING affinity via λ‐dependent umbrella sampling simulations. J. Comput. Chem., 2021, 42(2), 117-123.
[http://dx.doi.org/10.1002/jcc.26439] [PMID: 33078419]

Rights & Permissions Print Cite
Article Metrics
28
1
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy