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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Assessing the Performance of GOLD, Glide and MM-GBSA on a Dataset of Hydrazide-hydrazone-based Tuberculostatics

Author(s): Emilio Mateev*, Maya Georgieva and Alexander Zlatkov

Volume 20, Issue 10, 2023

Published on: 18 August, 2022

Page: [1557 - 1568] Pages: 12

DOI: 10.2174/1570180819666220512115015

Price: $65

Abstract

Background: Tuberculosis is considered a global health problem; hence, the screening and synthesis of novel tuberculostatic drugs are a necessity. Molecular docking could drastically reduce the time of hit identification; however, initial validation is required to reduce the false-positive results.

Objective: Assessment of several searching and scoring algorithms for a custom dataset of hydrazidehydrazone- based tuberculostatics was conducted to obtain a reliable docking protocol for future virtual screening.

Methods: Modification in the scoring functions, size of the grid space, and presence of active waters of a GOLD 5.3 docking protocol was conducted. Subsequently, side-chain flexibility and ensemble docking were carried out to assess the role of protein flexibility in the correlation coefficient. In addition, docking simulations with Glide and free binding energy calculations with MM-GBSA were implemented. The Pearson correlation coefficient between the experimental and the acquired in silico data was calculated after each work step. The major interactions between the top-scored ligands and the active site of 2X22 were visualized applying Discovery Studio.

Results: An optimized GOLD 5.3 docking protocol led to a drastically enhanced Pearson correlation coefficient of the training set, from 0.461 to 0.823, as well as an excellent pairwise correlation coefficient in the test set - 0,8405. Interestingly, the Glide docking scores and the free binding energy calculations with MM-GBSA did not achieve reliable results. During the visualization of the top-ranked compounds, it was observed that Lys165 played a major role in the formation of stable complexes.

Conclusion: It could be concluded that the performance of the optimized GOLD 5.3 docking protocol demonstrated significantly higher reliability against the hydrazide-hydrazone dataset when compared to Glide docking simulations and MM-GBSA free binding energy calculations. The results could be utilized for future virtual screenings.

Keywords: Tuberculostatics, molecular docking, GOLD, glide, MM-GBSA, protocol optimization, pearson correlation coefficient.

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[1]
Silva, F.S.B.D.; Castilho, A.L.; Maltempe, F.G.; Pina, R.Z.; Takao, E.K.H.; Siqueira, V.L.D.; Cardoso, R.F.; Caleffi-Ferracioli, K.R. Use of the Ogawa-Kudoh method to isolate mycobacteria in a tuberculosis reference laboratory in northwestern Paraná, Brazil. Braz. J. Pharm. Sci., 2013, 49(3), 567-570.
[http://dx.doi.org/10.1590/S1984-82502013000300018]
[2]
Bhatt, K.; Verma, S.; Ellner, J.J.; Salgame, P. Quest for correlates of protection against tuberculosis. Clin. Vaccine Immunol., 2015, 22(3), 258-266.
[http://dx.doi.org/10.1128/CVI.00721-14] [PMID: 25589549]
[3]
Durão, P.; Trindade, S.; Sousa, A.; Gordo, I. Multiple resistance at No cost: Rifampicin and streptomycin a dangerous liaison in the spread of antibiotic resistance. Mol. Biol. Evol., 2015, 32(10), 2675-2680.
[http://dx.doi.org/10.1093/molbev/msv143] [PMID: 26130082]
[4]
Mandewale, M.C.; Patil, U.C.; Shedge, S.V.; Dappadwad, U.R.; Yamgar, R.S. A review on quinoline hydrazone derivatives as a new class of potent antitubercular and anticancer agents. Beni. Suef Univ. J. Basic Appl. Sci., 2017, 6(4), 354-361.
[http://dx.doi.org/10.1016/j.bjbas.2017.07.005]
[5]
Thorat, B.R.; Rani, D.; Yamgar, R.S.; Mali, S.N. Synthesis, spectroscopic, in-vitro and computational analysis of hydrazones as potential antituberculosis agents: (Part-I). Comb. Chem. High Throughput Screen., 2020, 23(5), 392-401.
[http://dx.doi.org/10.2174/1386207323999200325125858] [PMID: 32209038]
[6]
Dey, R.; Nandi, S.; Samadder, A.; Saxena, A.; Saxena, A.K. Exploring the potential inhibition of candidate drug molecules for clinical investigation based on their docking or crystallographic analyses against M. tuberculosis enzyme targets. Curr. Top. Med. Chem., 2020, 20(29), 2662-2680.
[http://dx.doi.org/10.2174/1568026620666200903163921] [PMID: 32885754]
[7]
Jena, L.; Waghmare, P.; Kashikar, S.; Kumar, S.; Harinath, B.C. Computational approach to understanding the mechanism of action of isoniazid, an anti-TB drug. Int. J. Mycobacteriol., 2014, 3(4), 276-282.
[http://dx.doi.org/10.1016/j.ijmyco.2014.08.003] [PMID: 26786627]
[8]
He, X.; Alian, A.; Ortiz de Montellano, P.R. Inhibition of the Mycobacterium tuberculosis enoyl acyl carrier protein reductase InhA by arylamides. Bioorg. Med. Chem., 2007, 15(21), 6649-6658.
[http://dx.doi.org/10.1016/j.bmc.2007.08.013] [PMID: 17723305]
[9]
Usha, T.; Shanmugarajan, D.; Goyal, A.K.; Kumar, C.S.; Middha, S.K. Recent updates on computer-aided drug discovery: Time for a paradigm shift. Curr. Top. Med. Chem., 2017, 17(30), 3296-3307.
[http://dx.doi.org/10.2174/1568026618666180101163651] [PMID: 29295698]
[10]
Pinzi, L.; Rastelli, G. Molecular docking: Shifting paradigms in drug discovery. Int. J. Mol. Sci., 2019, 20(18), 4331.
[http://dx.doi.org/10.3390/ijms20184331] [PMID: 31487867]
[11]
Awuni, Y.; Mu, Y. Reduction of false positives in structure-based virtual screening when receptor plasticity is considered. Molecules, 2015, 20(3), 5152-5164.
[http://dx.doi.org/10.3390/molecules20035152] [PMID: 25808156]
[12]
Matthews, N.; Kitao, A.; Laycock, S.; Hayward, S. Haptic-assisted interactive molecular docking incorporating receptor flexibility. J. Chem. Inf. Model., 2019, 59(6), 2900-2912.
[http://dx.doi.org/10.1021/acs.jcim.9b00112] [PMID: 30969763]
[13]
Wang, R.; Lu, Y.; Fang, X.; Wang, S. An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. J. Chem. Inf. Comput. Sci., 2004, 44(6), 2114-2125.
[http://dx.doi.org/10.1021/ci049733j] [PMID: 15554682]
[14]
Garah, F.B.; Stigliani, J.L.; Coslédan, F.; Meunier, B.; Robert, A. Docking studies of structurally diverse antimalarial drugs targeting PfATP6: no correlation between in silico binding affinity and in vitro antimalarial activity. ChemMedChem, 2009, 4(9), 1469-1479.
[http://dx.doi.org/10.1002/cmdc.200900200] [PMID: 19645001]
[15]
Cheng, T.; Li, X.; Li, Y.; Liu, Z.; Wang, R. Comparative assessment of scoring functions on a diverse test set. J. Chem. Inf. Model., 2009, 49(4), 1079-1093.
[http://dx.doi.org/10.1021/ci9000053] [PMID: 19358517]
[16]
Dohutia, C.; Chetia, D.; Gogoi, K.; Bhattacharyya, D.R.; Sarma, K. Molecular docking, synthesis and in vitro antimalarial evaluation of certain novel curcumin analogues. Braz. J. Pharm. Sci., 2018, 53(4)
[http://dx.doi.org/10.1590/s2175-97902017000400084]
[17]
Mozziconacci, J.C.; Arnoult, E.; Bernard, P.; Do, Q.T.; Marot, C.; Morin-Allory, L. Optimization and validation of a docking-scoring protocol; application to virtual screening for COX-2 inhibitors. J. Med. Chem., 2005, 48(4), 1055-1068.
[http://dx.doi.org/10.1021/jm049332v] [PMID: 15715473]
[18]
Atanasova, M.; Yordanov, N.; Dimitrov, I.; Berkov, S.; Doytchinova, I. Molecular docking study on galantamine derivatives as cholinesterase inhibitors. Mol. Inform., 2015, 34(6-7), 394-403.
[http://dx.doi.org/10.1002/minf.201400145] [PMID: 27490385]
[19]
Battisti, A.; Zamuner, S.; Sarti, E.; Laio, A. Toward a unified scoring function for native state discrimination and drug-binding pocket recognition. Phys. Chem. Chem. Phys., 2018, 20(25), 17148-17155.
[http://dx.doi.org/10.1039/C7CP08170G] [PMID: 29900428]
[20]
Beveridge, D.L.; DiCapua, F.M. Free energy via molecular simulation: applications to chemical and biomolecular systems. Annu. Rev. Biophys. Biophys. Chem., 1989, 18(1), 431-492.
[http://dx.doi.org/10.1146/annurev.bb.18.060189.002243] [PMID: 2660832]
[21]
Greenidge, P.A.; Kramer, C.; Mozziconacci, J.C.; Sherman, W. Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA. J. Chem. Inf. Model., 2014, 54(10), 2697-2717.
[http://dx.doi.org/10.1021/ci5003735] [PMID: 25266271]
[22]
Zhang, X.; Wong, S.E.; Lightstone, F.C. Toward fully automated high performance computing drug discovery: a massively parallel virtual screening pipeline for docking and molecular mechanics/generalized Born surface area rescoring to improve enrichment. J. Chem. Inf. Model., 2014, 54(1), 324-337.
[http://dx.doi.org/10.1021/ci4005145] [PMID: 24358939]
[23]
Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J. Med. Chem., 2012, 55(14), 6582-6594.
[http://dx.doi.org/10.1021/jm300687e] [PMID: 22716043]
[24]
Sun, H.; Li, Y.; Tian, S.; Xu, L.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys., 2014, 16(31), 16719-16729.
[http://dx.doi.org/10.1039/C4CP01388C] [PMID: 24999761]
[25]
Peddi, S.R.; Sivan, S.K.; Manga, V. Molecular dynamics and MM/GBSA-integrated protocol probing the correlation between biological activities and binding free energies of HIV-1 TAR RNA inhibitors. J. Biomol. Struct. Dyn., 2018, 36(2), 486-503.
[http://dx.doi.org/10.1080/07391102.2017.1281762] [PMID: 28081678]
[26]
Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov., 2015, 10(5), 449-461.
[http://dx.doi.org/10.1517/17460441.2015.1032936] [PMID: 25835573]
[27]
El Khoury, L.; Santos-Martins, D.; Sasmal, S.; Eberhardt, J.; Bianco, G.; Ambrosio, F.A.; Solis-Vasquez, L.; Koch, A.; Forli, S.; Mobley, D.L. Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4. J. Comput. Aided Mol. Des., 2019, 33(12), 1011-1020.
[http://dx.doi.org/10.1007/s10822-019-00240-w] [PMID: 31691919]
[28]
Jones, G.; Willett, P.; Glen, R.C.; Leach, A.R.; Taylor, R. Development and validation of a genetic algorithm for flexible docking 11Edited by F. E. Cohen. In: J. Mol. Biol; , 1997; 2674, pp. (3)727-748.
[http://dx.doi.org/10.1006/jmbi.1996.0897]
[29]
Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47(7), 1739-1749.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
[30]
Bijev, A. Synthesis and in vitro evaluation of new hydrazones as pyrrole derivatives with anti-tubercular activity. Arzneimittelforschung, 2011, 59(01), 34-41.
[http://dx.doi.org/10.1055/s-0031-1296362]
[31]
Bijev, A. Synthesis and preliminary screening of carbohydrazides and hydrazones of pyrrole derivatives as potential tuberculostatics. Arzneimittelforschung, 2006, 56(2), 96-103.
[http://dx.doi.org/10.1055/s-0031-1296708] [PMID: 16572924]
[32]
Bijev, A. New Heterocyclic Hydrazones in the Search for Antitubercular Agents: Synthesis and in vitro Evaluations. Lett. Drug Des. Discov., 2006, 3(7), 506-512.
[http://dx.doi.org/10.2174/157018006778194790]
[33]
Goodsell, D.S.; Zardecki, C.; Di Costanzo, L.; Duarte, J.M.; Hudson, B.P.; Persikova, I.; Segura, J.; Shao, C.; Voigt, M.; Westbrook, J.D.; Young, J.Y.; Burley, S.K. RCSB Protein Data Bank: Enabling biomedical research and drug discovery. Protein Sci., 2020, 29(1), 52-65.
[http://dx.doi.org/10.1002/pro.3730] [PMID: 31531901]
[34]
Salmaso, V.; Sturlese, M.; Cuzzolin, A.; Moro, S. Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2. J. Comput. Aided Mol. Des., 2018, 32(1), 251-264.
[http://dx.doi.org/10.1007/s10822-017-0051-4] [PMID: 28840418]
[35]
Lovell, S.C.; Word, J.M.; Richardson, J.S.; Richardson, D.C. The penultimate rotamer library. Proteins, 2000, 40(3), 389-408.
[http://dx.doi.org/10.1002/1097-0134(20000815)40:3<389:AID-PROT50>3.0.CO;2-2] [PMID: 10861930]
[36]
Gupta, S.; Bajaj, A.V. Extra precision glide docking, free energy calculation and molecular dynamics studies of 1,2-diarylethane derivatives as potent urease inhibitors. J. Mol. Model., 2018, 24(9), 261.
[http://dx.doi.org/10.1007/s00894-018-3787-4] [PMID: 30159776]
[37]
Lyne, P.D.; Lamb, M.L.; Saeh, J.C. Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring. J. Med. Chem., 2006, 49(16), 4805-4808.
[http://dx.doi.org/10.1021/jm060522a] [PMID: 16884290]
[38]
Kalirajan, R.; Pandiselvi, A.; Gowramma, B.; Balachandran, P. In-silico design, ADMET screening, MM-GBSA binding free energy of some novel isoxazole substituted 9-anilinoacridines as HER2 inhibitors targeting breast cancer. Curr. Drug Res. Rev., 2019, 11(2), 118-128.
[http://dx.doi.org/10.2174/2589977511666190912154817] [PMID: 31513003]
[39]
Spitzer, R.; Jain, A.N. Surflex-Dock: Docking benchmarks and real-world application. J. Comput. Aided Mol. Des., 2012, 26(6), 687-699.
[http://dx.doi.org/10.1007/s10822-011-9533-y] [PMID: 22569590]
[40]
Sastry, G.M.; Adzhigirey, M.; Day, T.; Annabhimoju, R.; Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des., 2013, 27(3), 221-234.
[http://dx.doi.org/10.1007/s10822-013-9644-8] [PMID: 23579614]
[41]
Scotti, L.; Viana, J.; Scotti, M. Molecular docking studies of benzothiazinone derivatives in the search for new tuberculostatic agents. Proceedings of MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition,MDPI, 2017.
[http://dx.doi.org/10.3390/mol2net-03-05052]
[42]
Kokh, D.B.; Wenzel, W. Flexible side chain models improve enrichment rates in in silico screening. J. Med. Chem., 2008, 51(19), 5919-5931.
[http://dx.doi.org/10.1021/jm800217k] [PMID: 18771256]
[43]
Ollikainen, N.; de Jong, R.M.; Kortemme, T. Coupling protein side-chain and backbone flexibility improves the re-design of protein-ligand specificity. PLOS Comput. Biol., 2015, 11(9), e1004335.
[http://dx.doi.org/10.1371/journal.pcbi.1004335] [PMID: 26397464]
[44]
Chen, Y.C. Beware of docking! Trends Pharmacol. Sci., 2015, 36(2), 78-95.
[http://dx.doi.org/10.1016/j.tips.2014.12.001] [PMID: 25543280]
[45]
Zhang, X.; Perez-Sanchez, H.; Lightstone, F.C. A comprehensive docking and MM/GBSA rescoring study of ligand recognition upon binding antithrombin. Curr. Top. Med. Chem., 2017, 17(14), 1631-1639.
[http://dx.doi.org/10.2174/1568026616666161117112604] [PMID: 27852201]
[46]
Wichapong, K.; Rohe, A.; Platzer, C.; Slynko, I.; Erdmann, F.; Schmidt, M.; Sippl, W. Application of docking and QM/MM-GBSA rescoring to screen for novel Myt1 kinase inhibitors. J. Chem. Inf. Model., 2014, 54(3), 881-893.
[http://dx.doi.org/10.1021/ci4007326] [PMID: 24490903]
[47]
Tao, H.; Zhang, Y.; Huang, S.Y. Improving protein-peptide docking results via pose-clustering and rescoring with a combined knowledge-based and MM-GBSA scoring function. J. Chem. Inf. Model., 2020, 60(4), 2377-2387.
[http://dx.doi.org/10.1021/acs.jcim.0c00058] [PMID: 32267149]
[48]
Mishra, S.K. Koča, J. Assessing the performance of MM/PBSA, MM/GBSA, and QM-MM/GBSA approaches on protein/carbohydrate complexes: Effect of implicit solvent models, QM methods, and entropic contributions. J. Phys. Chem. B, 2018, 122(34), 8113-8121.
[http://dx.doi.org/10.1021/acs.jpcb.8b03655] [PMID: 30084252]
[49]
Wang, E.; Sun, H.; Wang, J.; Wang, Z.; Liu, H.; Zhang, J.Z.H.; Hou, T. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chem. Rev., 2019, 119(16), 9478-9508.
[http://dx.doi.org/10.1021/acs.chemrev.9b00055] [PMID: 31244000]
[50]
Yadav, D.K.; Ahmad, I.; Shukla, A.; Khan, F.; Negi, A.S.; Gupta, A. QSAR and docking studies on chalcone derivatives for antitubercular activity againstM. tuberculosisH37Rv. J. Chemometr., 2014, 28(6), 499-507.
[http://dx.doi.org/10.1002/cem.2606]

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