Login Register Cart 0
Review Article

结核分枝杆菌中的烯丙基-[酰基载体蛋白]还原酶（InhA）抑制的结构基础

卷 27, 期 5, 2020

页: [745 - 759] 页: 15

弟呕挨: 10.2174/0929867326666181203125229

价格: $65

conference banner
摘要

背景：反式烯基-[酰基载体蛋白]还原酶（InhA）是开发抗结核药物的核心蛋白。该酶是前药异烟肼的靶标，异烟肼被过氧化氢酶过氧化物酶（KatG）催化而变得有活性。目的：我们的目标是从一般方面开始，对InhA的研究进行综述，重点是最近的结构研究，重点是涉及InhA和抑制剂的复合物的晶体结构。 方法：我们从文献综述开始，然后描述有关InhA晶体结构的最新研究。我们使用此结构信息来描述蛋白质-配体相互作用。我们还分析了抑制InhA的结构基础。此外，我们描述了基于配体的晶体学位置预测结合亲和力的计算方法的应用。 结果：与抑制剂复合物的结构分析表明，关键残基负责针对InhA的特异性。大多数分子间相互作用涉及疏水残基，但有两个残基，即残基Ser 94和Tyr158。相互作用的研究表明，在耐异烟肼分枝杆菌的InhA基因突变中发现了许多抑制剂结合的关键残基。结核。对InhA的结合亲和力的计算预测表明与实验值存在中等的上坡关系。 结论：对涉及InhA抑制剂的结构的分析表明，对这些分子进行小的修饰可调节其抑制作用，可用于设计针对多药耐药菌株的新型抗结核药物。

关键词: 晶体结构，蛋白质-配体相互作用，反式烯酰-[酰基载体蛋白]还原酶，药物设计，InhA抑制剂，酶。

« Previous Next »
[1]
Kaczor, A.A.; Polski, A.; Sobótka-Polska, K.; Pachuta-Stec, A.; Makarska-Bialokoz, M.; Pitucha, M. Novel antibacterial compounds and their drug targets-successes and challenges. Curr. Med. Chem., 2017, 24(18), 1948-1982.
[http://dx.doi.org/10.2174/0929867323666161213102127] [PMID: 27978802]
[2]
Ichikawa, S.; Yamaguchi, M.; Matsuda, A. Antibacterial nucleoside natural products inhibiting phospho-murnacpentapeptide translocase; chemistry and structure-activity relationship. Curr. Med. Chem., 2015, 22(34), 3951-3979.
[http://dx.doi.org/10.2174/0929867322666150818103502] [PMID: 26282943]
[3]
de Azevedo, W.F., Jr Protein targets for development of drugs against Mycobacterium tuberculosis. Curr. Med. Chem., 2011, 18(9), 1255-1257.
[http://dx.doi.org/10.2174/092986711795029564] [PMID: 21366537]
[4]
Meneghetti, F.; Villa, S.; Gelain, A.; Barlocco, D.; Chiarelli, L.R.; Pasca, M.R.; Costantino, L. Iron acquisition pathways as targets for antitubercular drugs. Curr. Med. Chem., 2016, 23(35), 4009-4026.
[http://dx.doi.org/10.2174/0929867323666160607223747] [PMID: 27281295]
[5]
Dos Santos Fernandes, G.F.; Jornada, D.H.; de Souza, P.C.; Chin, C.M.; Pavan, F.R.; Dos Santos, J.L. Current advances in antitubercular drug discovery: potent prototypes and new targets. Curr. Med. Chem., 2015, 22(27), 3133-3161.
[http://dx.doi.org/10.2174/0929867322666150818103836] [PMID: 26282941]
[6]
Fanzani, L.; Porta, F.; Meneghetti, F.; Villa, S.; Gelain, A.; Lucarelli, A.P.; Parisini, E. Mycobacterium tuberculosis low molecular weight phosphatases (mptpa and mptpb): from biological insight to inhibitors. Curr. Med. Chem., 2015, 22(27), 3110-3132.
[http://dx.doi.org/10.2174/0929867322666150812150036] [PMID: 26264920]
[7]
Coracini, J.D.; de Azevedo, W.F., Jr Shikimate kinase, a protein target for drug design. Curr. Med. Chem., 2014, 21(5), 592-604.
[http://dx.doi.org/10.2174/09298673113206660299] [PMID: 24164195]
[8]
Inturi, B.; Pujar, G.V.; Purohit, M.N. Recent advances and structural features of enoyl-ACP reductase inhibitors of Mycobacterium tuberculosis. Arch. Pharm. (Weinheim), 2016, 349(11), 817-826.
[http://dx.doi.org/10.1002/ardp.201600186] [PMID: 27775177]
[9]
de Azevedo, W.F., Jr Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr. Med. Chem., 2011, 18(9), 1353-1366.
[http://dx.doi.org/10.2174/092986711795029519] [PMID: 21366529]
[10]
Punkvang, A.; Saparpakorn, P.; Hannongbua, S.; Wolschann, P.; Beyer, A.; Pungpo, P. Investigating the structural basis of arylamides to improve potency against M. tuberculosis strain through molecular dynamics simulations. Eur. J. Med. Chem., 2010, 45(12), 5585-5593.
[http://dx.doi.org/10.1016/j.ejmech.2010.09.008] [PMID: 20888672]
[11]
Pan, P.; Tonge, P.J. Targeting InhA, the FASII enoyl-ACP reductase: SAR studies on novel inhibitor scaffolds. Curr. Top. Med. Chem., 2012, 12(7), 672-693.
[http://dx.doi.org/10.2174/156802612799984535] [PMID: 22283812]
[12]
Smith, S.; Witkowski, A.; Joshi, A.K. Structural and functional organization of the animal fatty acid synthase. Prog. Lipid Res., 2003, 42(4), 289-317.
[http://dx.doi.org/10.1016/S0163-7827(02)00067-X] [PMID: 12689621]
[13]
Parsons, J.B.; Rock, C.O. Is bacterial fatty acid synthesis a valid target for antibacterial drug discovery? Curr. Opin. Microbiol., 2011, 14(5), 544-549.
[http://dx.doi.org/10.1016/j.mib.2011.07.029] [PMID: 21862391]
[14]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[15]
Berman, H.M.; Battistuz, T.; Bhat, T.N.; Bluhm, W.F.; Bourne, P.E.; Burkhardt, K.; Feng, Z.; Gilliland, G.L.; Iype, L.; Jain, S.; Fagan, P.; Marvin, J.; Padilla, D.; Ravichandran, V.; Schneider, B.; Thanki, N.; Weissig, H.; Westbrook, J.D.; Zardecki, C. Crystallogr. The protein data bank. Acta Crystallogr. D Biol. Crystallogr., 2002, 58(Pt. 6 No. 1), 899-907.
[http://dx.doi.org/10.1107/S0907444902003451] [PMID: 12037327]
[16]
Westbrook, J.; Feng, Z.; Chen, L.; Yang, H.; Berman, H.M. The protein data bank and structural genomics. Nucleic Acids Res., 2003, 31(1), 489-491.
[http://dx.doi.org/10.1093/nar/gkg068] [PMID: 12520059]
[17]
Benson, M.L.; Smith, R.D.; Khazanov, N.A.; Dimcheff, B.; Beaver, J.; Dresslar, P.; Nerothin, J.; Carlson, H.A. Binding MOAD, a high-quality protein-ligand database. Nucleic Acids Res., 2008, 36(Database issue), D674-D678.
[http://dx.doi.org/10.1093/nar/gkm911] [PMID: 18055497 ]
[18]
Ahmed, A.; Smith, R.D.; Clark, J.J.; Dunbar, J.B., Jr; Carlson, H.A. Recent improvements to binding MOAD: a resource for protein-ligand binding affinities and structures. Nucleic Acids Res., 2015, 43(Database issue), D465-D469.
[http://dx.doi.org/10.1093/nar/gku1088] [PMID: 25378330]
[19]
Liu, T.; Lin, Y.; Wen, X.; Jorissen, R.N.; Gilson, M.K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., 2007, 35(Database issue), D198-D201.
[http://dx.doi.org/10.1093/nar/gkl999] [PMID: 17145705]
[20]
Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 2016, 44(D1), D1045-D1053.
[http://dx.doi.org/10.1093/nar/gkv1072] [PMID: 26481362]
[21]
Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem., 2004, 47(12), 2977-2980.
[http://dx.doi.org/10.1021/jm030580l] [PMID: 15163179 ]
[22]
Liu, Z.; Li, Y.; Han, L.; Li, J.; Liu, J.; Zhao, Z.; Nie, W.; Liu, Y.; Wang, R. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics, 2015, 31(3), 405-412.
[http://dx.doi.org/10.1093/bioinformatics/btu626] [PMID: 25301850]
[23]
Fadel, V.; Bettendorff, P.; Herrmann, T.; de Azevedo, W.F., Jr; Oliveira, E.B.; Yamane, T.; Wüthrich, K. Automated NMR structure determination and disulfide bond identification of the myotoxin crotamine from Crotalus durissus terrificus. Toxicon, 2005, 46(7), 759-767.
[http://dx.doi.org/10.1016/j.toxicon.2005.07.018] [PMID: 16185738]
[24]
Xavier, M.M.; Heck, G.S.; Avila, M.B.; Levin, N.M.B.; Pintro, V.O.; Carvalho, N.L.; Azevedo, W.F. SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb. Chem. High Throughput Screen., 2016, 19(10), 801-812.
[http://dx.doi.org/10.2174/1386207319666160927111347] [PMID: 27686428]
[25]
Humphrey, W.; Dalke, A.; Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph., 1996, 14(1), 33-38. 27- 28
[http://dx.doi.org/10.1016/0263-7855(96)00018-5] [PMID: 8744570]
[26]
Thomsen, R.; Christensen, M.H. MolDock: a new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321.
[http://dx.doi.org/10.1021/jm051197e] [PMID: 16722650]
[27]
Heberlé, G.; de Azevedo, W.F., Jr Bio-inspired algorithms applied to molecular docking simulations. Curr. Med. Chem., 2011, 18(9), 1339-1352.
[http://dx.doi.org/10.2174/092986711795029573] [PMID: 21366530]
[28]
De Azevedo, W.F., Jr MolDock applied to structure-based virtual screening. Curr. Drug Targets, 2010, 11(3) 327334
[http://dx.doi.org/10.2174/138945010790711941]
[29]
Azevedo, L.S.; Moraes, F.P.; Xavier, M.M.; Pantoja, E.O.; Villavicencio, B.; Finck, J.A.; Proenca, A.M.; Rocha, K.B.; de Azevedo, W.F., Jr Recent progress of molecular docking simulations applied to development of drugs. Curr. Bioinform., 2012, 7(4), 352-365.
[http://dx.doi.org/10.2174/157489312803901063]
[30]
Dias, M.V.; Vasconcelos, I.B.; Prado, A.M.; Fadel, V.; Basso, L.A.; de Azevedo, W.F., Jr; Santos, D.S. Crystallographic studies on the binding of isonicotinyl-NAD adduct to wild-type and isoniazid resistant 2-trans-enoyl-ACP (CoA) reductase from Mycobacterium tuberculosis. J. Struct. Biol., 2007, 159(3), 369-380.
[http://dx.doi.org/10.1016/j.jsb.2007.04.009] [PMID: 17588773]
[31]
Wallace, A.C.; Laskowski, R.A.; Thornton, J.M. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng., 1995, 8(2), 127-134.
[http://dx.doi.org/10.1093/protein/8.2.127] [PMID: 7630882]
[32]
Laskowski, R.A.; Swindells, M.B. LigPlot+: multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model., 2011, 51(10), 2778-2786.
[http://dx.doi.org/10.1021/ci200227u] [PMID: 21919503]
[33]
Schön, T.; Miotto, P.; Köser, C.U.; Viveiros, M.; Böttger, E.; Cambau, E. Mycobacterium tuberculosis drug-resistance testing: challenges, recent developments and perspectives. Clin. Microbiol. Infect., 2017, 23(3), 154-160.
[http://dx.doi.org/10.1016/j.cmi.2016.10.022] [PMID: 27810467]
[34]
Oliveira, J.S.; Pereira, J.H.; Canduri, F.; Rodrigues, N.C.; de Souza, O.N.; de Azevedo, W.F., Jr; Basso, L.A.; Santos, D.S. Crystallographic and pre-steady-state kinetics studies on binding of NADH to wild-type and isoniazid-resistant enoyl-ACP(CoA) reductase enzymes from Mycobacterium tuberculosis. J. Mol. Biol., 2006, 359(3), 646-666.
[http://dx.doi.org/10.1016/j.jmb.2006.03.055] [PMID: 16647717]
[35]
Sharma, S.K.; Kumar, G.; Kapoor, M.; Surolia, A. Combined effect of epigallocatechin gallate and triclosan on enoyl-ACP reductase of Mycobacterium tuberculosis. Biochem. Biophys. Res. Commun., 2008, 368(1), 12-17.
[http://dx.doi.org/10.1016/j.bbrc.2007.10.191] [PMID: 17996734]
[36]
Ghorab, M.M.; El-Gaby, M.S.A.; Soliman, A.M.; Alsaid, M.S.; Abdel-Aziz, M.M.; Elaasser, M.M. Synthesis, docking study and biological evaluation of some new thiourea derivatives bearing benzenesulfonamide moiety. Chem. Cent. J., 2017, 11(1), 42.
[http://dx.doi.org/10.1186/s13065-017-0271-7] [PMID: 29086825]
[37]
da Silva, A.D.; Bitencourt-Ferreira, G.; de Azevedo, W.F., Jr Taba: A tool to analyze the binding affinity. J. Comput. Chem., 2020, 41(1), 69-73.
[http://dx.doi.org/10.1002/jcc.26048] [PMID: 31410856]
[38]
Dhumal, S.T.; Deshmukh, A.R.; Bhosle, M.R.; Khedkar, V.M.; Nawale, L.U.; Sarkar, D.; Mane, R.A. Synthesis and antitubercular activity of new 1,3,4-oxadiazoles bearing pyridyl and thiazolyl scaffolds. Bioorg. Med. Chem. Lett., 2016, 26(15), 3646-3651.
[http://dx.doi.org/10.1016/j.bmcl.2016.05.093] [PMID: 27301367]
[39]
Desai, N.C.; Somani, H.; Trivedi, A.; Bhatt, K.; Nawale, L.; Khedkar, V.M.; Jha, P.C.; Sarkar, D. Synthesis, biological evaluation and molecular docking study of some novel indole and pyridine based 1,3,4-oxadiazole derivatives as potential antitubercular agents. Bioorg. Med. Chem. Lett., 2016, 26(7), 1776-1783.
[http://dx.doi.org/10.1016/j.bmcl.2016.02.043] [PMID: 26920799]
[40]
Herrera Acevedo, C.; Scotti, L.; Feitosa Alves, M.; Formiga Melo Diniz, M.F.; Scotti, M.T. Computer-aided drug design using sesquiterpene lactones as sources of new structures with potential sctivity against infectious neglected diseases. Molecules, 2017, 22(1), 79.
[http://dx.doi.org/10.3390/molecules22010079] [PMID: 28054952]
[41]
Alves, M.F.; Scotti, M.T.; Scotti, L.; Mendonça, F.J.B.; Filho, J.M.B.; de Melo, S.A.L.; Dos Santos, S.G. de F F M Diniz, M. de F F M Diniz, M. Secondary metabolites from cissampelos, a possible source for new leads with anti-inflammatory activity. Curr. Med. Chem., 2017, 24(16), 1629-1644.
[http://dx.doi.org/10.2174/0929867323666161227123411] [PMID: 28029072]
[42]
Lorenzo, V.P.; Lúcio, A.S.; Scotti, L.; Tavares, J.F.; Filho, J.M.; Lima, T.K.; Rocha, J.D.; Scotti, M.T. Structure- and ligand-based approaches to evaluate aporphynic alkaloids from annonaceae as multi-target agent against Leishmania donovani. Curr. Pharm. Des., 2016, 22(34), 5196-5203.
[http://dx.doi.org/10.2174/1381612822666160513144853] [PMID: 27174814]
[43]
Scotti, L.; Mendonca, Junior, F.J.; Ishiki, H.M.; Ribeiro, F.F.; Singla, R.K.; Barbosa Filho, J.M.; Da Silva, M.S.; Scotti, M.T. Docking studies for multi-target drugs. Curr. Drug Targets, 2017, 18(5), 592-604.
[http://dx.doi.org/10.2174/1389450116666150825111818] [PMID: 26302806]
[44]
Scotti, L.; Scotti, M.T. Computer aided drug design studies in the discovery of secondary metabolites targeted against age-related neurodegenerative diseases. Curr. Top. Med. Chem., 2015, 15(21), 2239-2252.
[http://dx.doi.org/10.2174/1568026615666150610143510] [PMID: 26059353]
[45]
Scotti, L.; Bezerra Mendonça, Junior, F.J.; Magalhaes Moreira, D.R.; da Silva, M.S.; Pitta, I.R.; Scotti, M.T. SAR, QSAR and docking of anticancer flavonoids and variants: a review. Curr. Top. Med. Chem., 2012, 12(24), 2785-2809.
[http://dx.doi.org/10.2174/1568026611212240007] [PMID: 23368103]
[46]
Shilpi, J.A.; Ali, M.T.; Saha, S.; Hasan, S.; Gray, A.I.; Seidel, V. Molecular docking studies on InhA, MabA and PanK enzymes from Mycobacterium tuberculosis of ellagic acid derivatives from Ludwigia adscendens and Trewia nudiflora. In Silico Pharmacol., 2015, 3(1), 10.
[http://dx.doi.org/10.1186/s40203-015-0014-1] [PMID: 26820895]
[47]
Stigliani, J.L.; Bernardes-Génisson, V.; Bernadou, J.; Pratviel, G. Cross-docking study on InhA inhibitors: a combination of Autodock Vina and PM6-DH2 simulations to retrieve bio-active conformations. Org. Biomol. Chem., 2012, 10(31), 6341-6349.
[http://dx.doi.org/10.1039/c2ob25602a] [PMID: 22751934]
[48]
Punkvang, A.; Saparpakorn, P.; Hannongbua, S.; Wolschann, P.; Pungpo, P. Elucidating drug-enzyme interactions and their structural basis for improving the affinity and potency of isoniazid and its derivatives based on computer modeling approaches. Molecules, 2010, 15(4), 2791-2813.
[http://dx.doi.org/10.3390/molecules15042791] [PMID: 20428080]
[49]
Stigliani, J.L.; Arnaud, P.; Delaine, T.; Bernardes-Génisson, V.; Meunier, B.; Bernadou, J. Binding of the tautomeric forms of isoniazid-NAD adducts to the active site of the Mycobacterium tuberculosis enoyl-ACP reductase (InhA): a theoretical approach. J. Mol. Graph. Model., 2008, 27(4), 536-545.
[http://dx.doi.org/10.1016/j.jmgm.2008.09.006] [PMID: 18955002]
[50]
Saharan, V.D.; Mahajan, S.S. Development of gallic acid formazans as novel enoyl acyl carrier protein reductase inhibitors for the treatment of tuberculosis. Bioorg. Med. Chem. Lett., 2017, 27(4), 808-815.
[http://dx.doi.org/10.1016/j.bmcl.2017.01.026] [PMID: 28117201]
[51]
Bhatt, J.D.; Chudasama, C.J.; Patel, K.D. Pyrazole clubbed triazolo[1,5-a]pyrimidine hybrids as an anti-tubercular agents: synthesis, in vitro screening and molecular docking study. Bioorg. Med. Chem., 2015, 23(24), 7711-7716.
[http://dx.doi.org/10.1016/j.bmc.2015.11.018] [PMID: 26631439]
[52]
Kinnings, S.L.; Liu, N.; Tonge, P.J.; Jackson, R.M.; Xie, L.; Bourne, P.E. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J. Chem. Inf. Model., 2011, 51(2), 408-419.
[http://dx.doi.org/10.1021/ci100369f] [PMID: 21291174]
[53]
Li, H.J.; Lai, C.T.; Pan, P.; Yu, W.; Liu, N.; Bommineni, G.R.; Garcia-Diaz, M.; Simmerling, C.; Tonge, P.J. A structural and energetic model for the slow-onset inhibition of the Mycobacterium tuberculosis enoyl-ACP reductase InhA. ACS Chem. Biol., 2014, 9(4), 986-993.
[http://dx.doi.org/10.1021/cb400896g] [PMID: 24527857]
[54]
Sullivan, T.J.; Truglio, J.J.; Boyne, M.E.; Novichenok, P.; Zhang, X.; Stratton, C.F.; Li, H.J.; Kaur, T.; Amin, A.; Johnson, F.; Slayden, R.A.; Kisker, C.; Tonge, P.J. High affinity InhA inhibitors with activity against drug-resistant strains of Mycobacterium tuberculosis. ACS Chem. Biol., 2006, 1(1), 43-53.
[http://dx.doi.org/10.1021/cb0500042] [PMID: 17163639]
[55]
Brünger, A.T. Free R value: a novel statistical quantity for assessing the accuracy of crystal structures. Nature, 1992, 355(6359), 472-475.
[http://dx.doi.org/10.1038/355472a0] [PMID: 18481394]
[56]
Brünger, A.T. Assessment of phase accuracy by cross validation: the free R value. Methods and applications. Acta Crystallogr. D Biol. Crystallogr., 1993, 49(Pt 1), 24-36.
[http://dx.doi.org/10.1107/S0907444992007352] [PMID: 15299543]
[57]
Delatorre, P.; de Azevedo, W.F., Jr Simulation of electron density maps for twodimensional crystal structures using Mathematica. J. Appl. Cryst., 2001, 34(5), 658-660.
[http://dx.doi.org/10.1107/S0021889801009724]
[58]
de Azevedo, W.F., Jr; Canduri, F.; Basso, L.A.; Palma, M.S.; Santos, D.S. Determining the structural basis for specificity of ligands using crystallographic screening. Cell Biochem. Biophys., 2006, 44(3), 405-411.
[http://dx.doi.org/10.1385/CBB:44:3:405] [PMID: 16679527]
[59]
Hartkoorn, R.C.; Pojer, F.; Read, J.A.; Gingell, H.; Neres, J.; Horlacher, O.P.; Altmann, K.H.; Cole, S.T. Pyridomycin bridges the NADH- and substrate-binding pockets of the enoyl reductase InhA. Nat. Chem. Biol., 2014, 10(2), 96-98.
[http://dx.doi.org/10.1038/nchembio.1405] [PMID: 24292073]
[60]
Persson, B.; Kallberg, Y.; Oppermann, U.; Jörnvall, H. Coenzyme-based functional assignments of short-chain dehydrogenases/reductases (SDRs). Chem. Biol. Interact., 2003, 143-144, 271-278.
[http://dx.doi.org/10.1016/S0009-2797(02)00223-5] [PMID: 12604213]
[61]
Parikh, S.L.; Xiao, G.; Tonge, P.J. Inhibition of InhA, the enoyl reductase from Mycobacterium tuberculosis, by triclosan and isoniazid. Biochemistry, 2000, 39(26), 7645-7650.
[http://dx.doi.org/10.1021/bi0008940] [PMID: 10869170]
[62]
Seifert, M.; Catanzaro, D.; Catanzaro, A.; Rodwell, T.C. Genetic mutations associated with isoniazid resistance in Mycobacterium tuberculosis: a systematic review. PLoS One, 2015, 10(3) e0119628
[http://dx.doi.org/10.1371/journal.pone.0119628] [PMID: 25799046]
[63]
Basso, L.A.; Zheng, R.; Musser, J.M.; Jacobs, W.R., Jr; Blanchard, J.S. Mechanisms of isoniazid resistance in Mycobacterium tuberculosis: enzymatic characterization of enoyl reductase mutants identified in isoniazid-resistant clinical isolates. J. Infect. Dis., 1998, 178(3), 769-775.
[http://dx.doi.org/10.1086/515362] [PMID: 9728546]
[64]
Bedewi Omer, Z.; Mekonnen, Y.; Worku, A.; Zewde, A.; Medhin, G.; Mohammed, T.; Pieper, R.; Ameni, G. Evaluation of the GenoType MTBDRplus assay for detection of rifampicin- and isoniazid-resistant Mycobacterium tuberculosis isolates in central Ethiopia. Int. J. Mycobacteriol., 2016, 5(4), 475-481.
[http://dx.doi.org/10.1016/j.ijmyco.2016.06.005] [PMID: 27931690]
[65]
Ahmad, B.; Idrees, M.; Ahmad, K.; Bashir, S.; Jamil, S. Molecular characterisation of isoniazid resistant clinical isolates of Mycobacterium tuberculosis from Khyber Pakhtunkhwa, Pakistan. J. Pak. Med. Assoc., 2017, 67(8), 1224-1227.
[PMID: 28839308]
[66]
Takawira, F.T.; Mandishora, R.S.D.; Dhlamini, Z.; Munemo, E.; Stray-Pedersen, B. Mutations in rpoB and katG genes of multidrug resistant Mycobacterium tuberculosis undetectable using genotyping diagnostic methods. Pan Afr. Med. J., 2017, 27(1), 145.
[http://dx.doi.org/10.11604/pamj.2017.27.145.10883] [PMID: 28904673]
[67]
Squeglia, F.; Romano, M.; Ruggiero, A.; Berisio, R. Molecular players in Tuberculosis drug development: another break in the cell wall. Curr. Med. Chem., 2017, 24(36), 3954-3969.
[http://dx.doi.org/10.2174/0929867324666170208150016] [PMID: 28183257]
[68]
Sgaragli, G.; Frosini, M. Human tuberculosis I. Epidemiology, diagnosis and pathogenetic mechanisms. Curr. Med. Chem., 2016, 23(25), 2836-2873.
[http://dx.doi.org/10.2174/0929867323666160607222854] [PMID: 27281297]
[69]
Chiarelli, L.R.; Mori, G.; Esposito, M.; Orena, B.S.; Pasca, M.R. New and old hot drug targets in tuberculosis. Curr. Med. Chem., 2016, 23(33), 3813-3846.
[http://dx.doi.org/10.2174/1389557516666160831164925] [PMID: 27666933]
[70]
Sgaragli, G.; Frosini, M. Human tuberculosis II. M. tuberculosis mechanisms of genetic and phenotypic resistance to anti-tuberculosis drugs. Curr. Med. Chem., 2016, 23(12), 1186-1216.
[http://dx.doi.org/10.2174/0929867323666160405112820] [PMID: 27048337]
[71]
Sharma, R.; Kaur, A.; Sharma, A.K.; Dilbaghi, N.; Sharma, A.K. Nano-based anti-tubercular drug delivery and therapeutic interventions in tuberculosis. Curr. Drug Targets, 2017, 18(1), 72-86.
[http://dx.doi.org/10.2174/1389450116666150804110238] [PMID: 26240053]
[72]
Freundlich, J.S.; Wang, F.; Vilchèze, C.; Gulten, G.; Langley, R.; Schiehser, G.A.; Jacobus, D.P.; Jacobs, W.R., Jr; Sacchettini, J.C. Triclosan derivatives: towards potent inhibitors of drug-sensitive and drug-resistant Mycobacterium tuberculosis. ChemMedChem, 2009, 4(2), 241-248.
[http://dx.doi.org/10.1002/cmdc.200800261] [PMID: 19130456]
[73]
Holas, O.; Ondrejcek, P.; Dolezal, M. Mycobacterium tuberculosis enoyl-acyl carrier protein reductase inhibitors as potential antituberculotics: development in the past decade. J. Enzyme Inhib. Med. Chem., 2015, 30(4), 629-648.
[http://dx.doi.org/10.3109/14756366.2014.959512] [PMID: 25383419]
[74]
Canduri, F.; Perez, P.C.; Caceres, R.A.; de Azevedo, W.F., Jr Protein kinases as targets for antiparasitic chemotherapy drugs. Curr. Drug Targets, 2007, 8(3), 389-398.
[http://dx.doi.org/10.2174/138945007780058979] [PMID: 17348832]
[75]
de Azevedo, W.F., Jr; Dias, R. Evaluation of ligand-binding affinity using polynomial empirical scoring functions. Bioorg. Med. Chem., 2008, 16(20), 9378-9382.
[http://dx.doi.org/10.1016/j.bmc.2008.08.014] [PMID: 18829335]
[76]
de Azevedo, W.F., Jr; Dias, R. Computational methods for calculation of ligand-binding affinity. Curr. Drug Targets, 2008, 9(12), 1031-1039.
[http://dx.doi.org/10.2174/138945008786949405] [PMID: 19128212]
[77]
Dias, R.; de Azevedo, W.F., Jr Molecular docking algorithms. Curr. Drug Targets, 2008, 9(12), 1040-1047.
[http://dx.doi.org/10.2174/138945008786949432] [PMID: 19128213]
[78]
Dias, R.; Timmers, L.F.; Caceres, R.A.; de Azevedo, W.F., Jr Evaluation of molecular docking using polynomial empirical scoring functions. Curr. Drug Targets, 2008, 9(12), 1062-1070.
[http://dx.doi.org/10.2174/138945008786949450] [PMID: 19128216]
[79]
de Azevedo, W.F., Jr; Dias, R. Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr. Drug Targets, 2008, 9(12), 1071-1076.
[http://dx.doi.org/10.2174/138945008786949441] [PMID: 19128217]
[80]
De Azevedo, W.F., Jr Structure-based virtual screening. Curr. Drug Targets, 2010, 11(3), 261-263.
[http://dx.doi.org/10.2174/138945010790711941] [PMID: 20214598]
[81]
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.
[http://dx.doi.org/10.1016/j.bbrc.2017.10.035] [PMID: 29017921]
[82]
Pintro, V.O.; de Azevedo, W.F. Optimized virtual screening workflow: towards target-based polynomial scoring functions for HIV-1 protease. Comb. Chem. High Throughput Screen., 2017, 20(9), 820-827.
[http://dx.doi.org/10.2174/1386207320666171121110019] [PMID: 29165067]
[83]
Heck, G.S.; Pintro, V.O.; Pereira, R.R.; de Ávila, M.B.; Levin, N.M.B.; de Azevedo, W.F. Supervised machine learning methods applied to predict ligand- binding affinity. Curr. Med. Chem., 2017, 24(23), 2459-2470.
[http://dx.doi.org/10.2174/0929867324666170623092503] [PMID: 28641555]
[84]
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.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[85]
Levin, N.M.B.; Pintro, V.O.; de Ávila, M.B.; de Mattos, B.B.; De Azevedo, W.F., Jr Understanding the structural basis for inhibition of cyclin-dependent kinases. new pieces in the molecular puzzle. Curr. Drug Targets, 2017, 18(9), 1104-1111.
[http://dx.doi.org/10.2174/1389450118666161116130155] [PMID: 27848884]
[86]
de Azevedo, W.F., Jr Opinion paper: targeting multiple cyclin-dependent kinases (cdks): a new strategy for molecular docking studies. Curr. Drug Targets, 2016, 17(1), 2.
[http://dx.doi.org/10.2174/138945011701151217100907] [PMID: 26687602]
[87]
Teles, C.B.; Moreira-Dill, L.S. Silva, Ade.A.; Facundo, V.A.; de Azevedo, W.F., Jr; da Silva, L.H.; Motta, M.C.; Stábeli, R.G.; Silva-Jardim, I. A lupane-triterpene isolated from Combretum leprosum Mart. fruit extracts that interferes with the intracellular development of Leishmania (L.) amazonensis in vitro. BMC Complement. Altern. Med., 2015, 15(1), 165.
[http://dx.doi.org/10.1186/s12906-015-0681-9] [PMID: 26048712]
[88]
de Ávila, M.B.; de Azevedo, W.F. Data mining of docking results. Application to 3-dehydroquinate dehydratase. Curr. Bioinform., 2014, 9(4), 361-379.
[http://dx.doi.org/10.2174/1574893609666140421205215]
[89]
Moraes, F.P.; de Azevedo, W.F., Jr Targeting imidazoline site on monoamine oxidase B through molecular docking simulations. J. Mol. Model., 2012, 18(8), 3877-3886.
[http://dx.doi.org/10.1007/s00894-012-1390-7] [PMID: 22426510]
[90]
Vianna, C.P.; de Azevedo, W.F., Jr Identification of new potential Mycobacterium tuberculosis shikimate kinase inhibitors through molecular docking simulations. J. Mol. Model., 2012, 18(2), 755-764.
[http://dx.doi.org/10.1007/s00894-011-1113-5] [PMID: 21594693]
[91]
Canduri, F.; de Azevedo, W.F. Protein crystallography in drug discovery. Curr. Drug Targets, 2008, 9(12), 1048-1053.
[http://dx.doi.org/10.2174/138945008786949423] [PMID: 19128214]
[92]
Morris, G.; Goodsell, D.; Halliday, R.; Huey, R.; Hart, W.; Belew, R.; Olson, A. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 1998, 19(14), 1639-1662.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19:14<1639:AID-JCC10>3.0.CO;2-B]
[93]
Amaral, M.E.A.; Nery, L.R.; Leite, C.E.; de Azevedo, Junior, W.F.; Campos, M.M. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest. New Drugs, 2018, 36(5), 782-796.
[http://dx.doi.org/10.1007/s10637-018-0568-y] [PMID: 29392539]
[94]
Levin, N.M.B.; Pintro, V.O.; Bitencourt-Ferreira, G.; de Mattos, B.B.; de Castro Silvério, A.; de Azevedo, W.F., Jr Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys. Chem., 2018, 235, 1-8.
[http://dx.doi.org/10.1016/j.bpc.2018.01.004] [PMID: 29407904]
[95]
Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: a free tool to discover chemistry for biology. J. Chem. Inf. Model., 2012, 52(7), 1757-1768.
[http://dx.doi.org/10.1021/ci3001277] [PMID: 22587354]
[96]
Irwin, J.J.; Shoichet, B.K. ZINC-a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45(1), 177-182.
[http://dx.doi.org/10.1021/ci049714+] [PMID: 15667143]
[97]
Sterling, T.; Irwin, J.J. ZINC 15-ligand discovery for everyone. J. Chem. Inf. Model., 2015, 55(11), 2324-2337.
[http://dx.doi.org/10.1021/acs.jcim.5b00559] [PMID: 26479676]
[98]
Gozalbes, R.; Pineda-Lucena, A. Small molecule databases and chemical descriptors useful in chemoinformatics: an overview. Comb. Chem. High Throughput Screen., 2011, 14(6), 548-458.
[http://dx.doi.org/10.2174/138620711795767857] [PMID: 21521149]
[99]
Ghasemi, J.B.; Shiri, F.; Pirhadi, S.; Heidari, Z. Discovery of new potential antimalarial compounds using virtual screening of ZINC database. Comb. Chem. High Throughput Screen., 2015, 18(2), 227-234.
[http://dx.doi.org/10.2174/1386207318666141229123705] [PMID: 25543686]
[100]
Patel, P.; Singh, A.; Patel, V.K.; Jain, D.K.; Veerasamy, R.; Rajak, H. Pharmacophore based 3D-QSAR, virtual screening and docking studies on novel series of HDAC inhibitors with thiophen linker as anticancer agents. Comb. Chem. High Throughput Screen., 2016, 19(9), 735-751.
[http://dx.doi.org/10.2174/1386207319666160801154415] [PMID: 27487787]

Rights & Permissions Print Cite
Article Metrics
42
Wayfinder Image
© 2024 Bentham Science Publishers | Privacy Policy