956
Views
40
CrossRef citations to date
0
Altmetric
Research Articles

Towards more effective acetylcholinesterase inhibitors: a comprehensive modelling study based on human acetylcholinesterase protein–drug complex

ORCID Icon
Pages 565-572 | Received 17 Jan 2019, Accepted 12 Feb 2019, Published online: 11 Mar 2019

References

  • Ambure, P., Bhat, J., Puzyn, T., & Roy, K. (2018). Identifying natural compounds as multi-target-directed ligands against Alzheimer’s disease: An in silico approach. Journal of Biomolecular Structure and Dynamics, 1–25. doi: 10.1080/07391102.2018.1456975
  • Basile, L. (2018). Virtual screening in the search of new and potent anti-Alzheimer agents. In K. Roy (Ed.), Computational modeling of drugs against Alzheimer’s disease (Vol. 132, pp. 107–137). New York, NY: Humana Press.
  • Basu, S., & Wallner, B. (2016). Finding correct protein–protein docking models using ProQDock. Bioinformatics, 32(12), i262–i270. doi: 10.1093/bioinformatics/btw257
  • Borges, N. M., Sartori, G. R., Ribeiro, J. F., Rocha, J. R., Martins, J. B., Montanari, C. A., & Gargano, R. (2018). Similarity search combined with docking and molecular dynamics for novel hAChE inhibitor scaffolds. Journal of Molecular Modeling, 24(1), 41. doi: 10.1007/s00894-017-3548-9
  • Braga, R. C., & Andrade, C. H. (2013). Assessing the performance of 3D pharmacophore models in virtual screening: How good are they? Current Topics in Medicinal Chemistry, 13(9), 1127–1138.
  • Carletti, E., Colletier, J.-P., Dupeux, F., Trovaslet, M., Masson, P., & Nachon, F. (2010). Structural evidence that human acetylcholinesterase inhibited by Tabun ages through O-dealkylation. Journal of Medicinal Chemistry, 53(10), 4002–4008. doi: 10.1021/jm901853b
  • Chen, Y., Fang, L., Peng, S., Liao, H., Lehmann, J., & Zhang, Y. (2012). Discovery of a novel acetylcholinesterase inhibitor by structure-based virtual screening techniques. Bioorganic & Medicinal Chemistry Letters, 22(9), 3181–3187. doi: 10.1016/j.bmcl.2012.03.046
  • Chen, Y., Lin, H., Yang, H., Tan, R., Bian, Y., Fu, T., … Sun, H. (2017). Discovery of new acetylcholinesterase and butyrylcholinesterase inhibitors through structure-based virtual screening. RSC Advances, 7(6), 3429–3438. doi: 10.1039/C6RA25887E
  • Cheung, J., Rudolph, M. J., Burshteyn, F., Cassidy, M. S., Gary, E. N., Love, J., … Height, J. J. (2012). Structures of human acetylcholinesterase in complex with pharmacologically important ligands. Journal of Medicinal Chemistry, 55(22), 10282–10286. doi: 10.1021/jm300871x
  • Choudhary, M. I. (2015). Drug design and discovery in Alzheimer’s disease. Elsevier.
  • Cole, J. C., Murray, C. W., Nissink, J. W. M., Taylor, R. D., & Taylor, R. (2005). Comparing protein–ligand docking programs is difficult. Proteins: Structure, Function, and Bioinformatics, 60(3), 325–332. doi: 10.1002/prot.20497
  • Cummings, J. L., Morstorf, T., & Zhong, K. (2014). Alzheimer’s disease drug-development pipeline: Few candidates, frequent failures. Alzheimer's Research & Therapy, 6(4), 37. doi: 10.1186/alzrt269
  • Del Rio, A., Barbosa, A. J. M., Caporuscio, F., & Mangiatordi, G. F. (2010). CoCoCo: A free suite of multiconformational chemical databases for high-throughput virtual screening purposes. Molecular Biosystems, 6(11), 2122–2128. doi: 10.1039/c0mb00039f
  • Dixon, S. L., Smondyrev, A. M., Knoll, E. H., Rao, S. N., Shaw, D. E., & Friesner, R. A. (2006). PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. Journal of Computer-Aided Molecular Design, 20(10–11), 647–671. doi: 10.1007/s10822-006-9087-6
  • Dixon, S. L., Smondyrev, A. M., & Rao, S. N. (2006). PHASE: A novel approach to pharmacophore modeling and 3D database searching. Chemical Biology Drug Design, 67(5), 370–372. doi: 10.1111/j.1747-0285.2006.00384.x
  • Dror, O., Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2009). Novel approach for efficient pharmacophore-based virtual screening: Method and applications. Journal of Chemical Information and Modeling, 49(10), 2333–2343. doi: 10.1021/ci900263d
  • Dvir, H., Silman, I., Harel, M., Rosenberry, T. L., & Sussman, J. L. (2010). Acetylcholinesterase: From 3D structure to function. Chemico-Biological Interactions, 187(1–3), 10–22. doi: 10.1016/j.cbi.2010.01.042
  • Ece, A. (2016). e-Pharmacophore mapping combined with virtual screening and molecular docking to identify potent and selective inhibitors of P90 ribosomal S6 kinase (RSK). Turkish Journal of Pharmaceutical Sciences, 13(2), 241–248. doi: 10.4274/tjps.28290
  • Ece, A., & Pejin, B. (2015). A computational insight into acetylcholinesterase inhibitory activity of a new lichen depsidone. Journal of Enzyme Inhibition and Medicinal Chemistry, 30(4), 528–532. doi: 10.3109/14756366.2014.949256
  • Ece, A., & Sevin, F. (2010). Exploring QSAR on 4-cyclohexylmethoxypyrimidines as antitumor agents for their inhibitory activity of cdk2. Letters in Drug Design & Discovery, 7(9), 625–631. doi: 10.2174/157018010792929612
  • Ece, A., & Sevin, F. (2013). The discovery of potential cyclin A/CDK2 inhibitors: A combination of 3D QSAR pharmacophore modeling, virtual screening, and molecular docking studies. Medicinal Chemistry Research, 22(12), 5832–5843. doi: 10.1007/s00044-013-0571-y
  • Ferreira Neto, D. C., Alencar Lima, J., Sobreiro Francisco Diz de Almeida, J., Costa França, T. C., Jorge do Nascimento, C., & Figueroa Villar, J. D. (2017). New semicarbazones as gorge-spanning ligands of acetylcholinesterase and potential new drugs against Alzheimer’s disease: Synthesis, molecular modeling, NMR, and biological evaluation. Journal of Biomolecular Structure and Dynamics, 1–15.. doi: 10.1080/07391102.2017.1407676
  • Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., … Shenkin, P. S. (2004). Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7), 1739–1749. doi: 10.1021/jm0306430
  • Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., … Mainz, D. T. (2006). Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein − ligand complexes. Journal of Medicinal Chemistry, 49(21), 6177–6196. doi: 10.1021/jm051256o
  • Greenwood, J. R., Calkins, D., Sullivan, A. P., & Shelley, J. C. (2010). Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. Journal of Computer-Aided Molecular Design, 24(6–7), 591–604. doi: 10.1007/s10822-010-9349-1
  • Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004). Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry, 47(7), 1750–1759. doi: 10.1021/jm030644s
  • Hevener, K. E., Zhao, W., Ball, D. M., Babaoglu, K., Qi, J., White, S. W., & Lee, R. E. (2009). Validation of molecular docking programs for virtual screening against dihydropteroate synthase. Journal of Chemical Information and Modeling, 49(2), 444–460. doi: 10.1021/ci800293n
  • Huang, N., Shoichet, B. K., & Irwin, J. J. (2006). Benchmarking sets for molecular docking. Journal of Medicinal Chemistry, 49(23), 6789–6801. doi: 10.1021/jm0608356
  • Huang, Z., & Wong, C. F. (2016). Inexpensive method for selecting receptor structures for virtual screening. Journal of Chemical Information and Modeling, 56(1), 21–34. doi: 10.1021/acs.jcim.5b00299
  • Ion, G. N. D., Mihai, D. P., Lupascu, G., & Nitulescu, G. M. (2018). Application of molecular framework-based data-mining method in the search for beta-secretase 1 inhibitors through drug repurposing. Journal of Biomolecular Structure and Dynamics, 1–12. doi: 10.1080/07391102.2018.1526115
  • Iqbal, S., Anantha Krishnan, D., & Gunasekaran, K. (2018). Identification of potential PKC inhibitors through pharmacophore designing, 3D-QSAR and molecular dynamics simulations targeting Alzheimer’s disease. Journal of Biomolecular Structure Dynamics, 36(15), 4029–4044. doi: 10.1080/07391102.2017.1406824
  • Jain, A. N. (2008). Bias, reporting, and sharing: Computational evaluations of docking methods. Journal of Computer-Aided Molecular Design, 22(3–4), 201–212. doi: 10.1007/s10822-007-9151-x
  • Jorgensen, W. L., Maxwell, D. S., & Tirado-Rives, J. (1996). Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. Journal of the American Chemical Society, 118(45), 11225–11236. doi: 10.1021/ja9621760
  • Jorgensen, W. L., & Tirado-Rives, J. (1988). The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 110(6), 1657–1666. doi: 10.1021/ja00214a001
  • Kryger, G., Harel, M., Giles, K., Toker, L., Velan, B., Lazar, A., … Sussman, J. L. (2000). Structures of recombinant native and E202Q mutant human acetylcholinesterase complexed with the snake-venom toxin fasciculin-II. Acta Crystallographica Section D Biological Crystallography, 56(11), 1385–1394. doi: 10.1107/S0907444900010659
  • Kumar, A., Srivastava, G., Negi, A. S., & Sharma, A. (2018). Docking, molecular dynamics, binding energy-MM-PBSA studies of naphthofuran derivatives to identify potential dual inhibitors against BACE-1 and GSK-3β. Journal of Biomolecular Structure and Dynamics, 1–16. doi: 10.1080/07391102.2018.1426043
  • Lipinski, C. A. (2000). Drug-like properties and the causes of poor solubility and poor permeability. Journal of Pharmacological and Toxicological Methods, 44(1), 235–249. doi: 10.1016/S1056-8719(00)00107-6
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23(1–3), 3–25. doi: 10.1016/S0169-409X(96)00423-1
  • Loving, K., Salam, N. K., & Sherman, W. (2009). Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation. Journal of Computer-Aided Molecular Design, 23(8), 541–554. doi: 10.1007/s10822-009-9268-1
  • Luisi, G., Stefanucci, A., Zengin, G., Dimmito, M., & Mollica, A. (2018). Anti-oxidant and tyrosinase inhibitory in vitro activity of amino acids and small peptides: New hints for the multifaceted treatment of neurologic and metabolic disfunctions. Antioxidants, 8(1), 7. doi: 10.3390/antiox8010007
  • Manoharan, P., Chennoju, K., & Ghoshal, N. (2018). Computational analysis of BACE1-ligand complex crystal structures and linear discriminant analysis for identification of BACE1 inhibitors with anti P-glycoprotein binding property. Journal of Biomolecular Structure and Dynamics, 36(1), 262–276. doi: 10.1080/07391102.2016.1276477
  • Manoharan, P., & Ghoshal, N. (2018). Fragment-based virtual screening approach and molecular dynamics simulation studies for identification of BACE1 inhibitor leads. Journal of Biomolecular Structure and Dynamics, 36(7), 1878–1892. doi: 10.1080/07391102.2017.1337590
  • Marcu, M. G., Chadli, A., Bouhouche, I., Catelli, M., & Neckers, L. M. (2000). The heat shock protein 90 antagonist novobiocin interacts with a previously unrecognized ATP-binding domain in the carboxyl terminus of the chaperone. Journal of Biological Chemistry, 275(47), 37181–37186. doi: 10.1074/jbc.M003701200
  • Pascoini, A. L., Federico, L. B., Arêas, A., Verde, B. A., Freitas, P., & Camps, I. (2018). In silico development of new acetylcholinesterase inhibitors. Journal of Biomolecular Structure and Dynamics, 1–15. doi: 10.1080/07391102.2018.1447513
  • Perry, E. K. (1986). The cholinergic hypothesis—Ten years on. British Medical Bulletin, 42(1), 63–69. doi: 10.1093/oxfordjournals.bmb.a072100
  • Rastelli, G., Rio, A. D., Degliesposti, G., & Sgobba, M. (2010). Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA. Journal of Computational Chemistry, 31(4), 797–810. doi: 10.1002/jcc.21372
  • Salam, N. K., Nuti, R., & Sherman, W. (2009). Novel method for generating structure-based pharmacophores using energetic analysis. Journal of Chemical Information and Modeling, 49(10), 2356–2368. doi: 10.1021/ci900212v
  • Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27(3), 221–234. doi: 10.1007/s10822-013-9644-8
  • Schrödinger. (2015). Small-molecule drug discovery suite (Version 2015-3). New York, NY: Schrödinger, LLC.
  • Schrödinger. (2018). Maestro (version 2018-4). New York, NY: Schrödinger, LLC.
  • Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007). Epik: A software program for pK a prediction and protonation state generation for drug-like molecules. Journal of Computer-Aided Molecular Design, 21(12), 681–691. doi: 10.1007/s10822-007-9133-z
  • Shi, J., Tu, W., Luo, M., & Huang, C. (2017). Molecular docking and molecular dynamics simulation approaches for identifying new lead compounds as potential AChE inhibitors. Molecular Simulation, 43(2), 102–109. doi: 10.1080/08927022.2016.1237022
  • Shiri, F., Pirhadi, S., & Ghasemi, J. B. (2018). Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors. Journal of Biomolecular Structure and Dynamics, 1–13. doi: 10.1080/07391102.2018.1468281
  • Taft, C. A., da Silva, V. B., & da Silva, C. H. (2008). Current topics in computer‐aided drug design. Journal of Pharmaceutical Sciences, 97(3), 1089–1098. doi: 10.1002/jps.21293
  • Truchon, J.-F., & Bayly, C. I. (2007). Evaluating virtual screening methods: Good and bad metrics for the “early recognition” problem. Journal of Chemical Information and Modeling, 47(2), 488–508. doi: 10.1021/ci600426e
  • Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615–2623. doi: 10.1021/jm020017n
  • Zengin, G., Rodrigues, M. J., Abdallah, H. H., Custodio, L., Stefanucci, A., Aumeeruddy, M. Z., … Mahomoodally, M. F. (2018). Combination of phenolic profiles, pharmacological properties and in silico studies to provide new insights on Silene salsuginea from Turkey. Computational Biology and Chemistry, 77, 178–186. doi: 10.1016/j.compbiolchem.2018.10.005
  • Zengin, G., Stefanucci, A., Rodrigues, M. J., Mollica, A., Custodio, L., Aumeeruddy, M. Z., & Mahomoodally, M. F. (2019). Scrophularia lucida L. as a valuable source of bioactive compounds for pharmaceutical applications: In vitro antioxidant, anti-inflammatory, enzyme inhibitory properties, in silico studies, and HPLC profiles. Journal of Pharmaceutical Biomedical Analysis, 162, 225–233. doi: 10.1016/j.jpba.2018.09.035

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.