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Identification of new promising Plasmodium falciparum superoxide dismutase allosteric inhibitors through hierarchical pharmacophore-based virtual screening and molecular dynamics

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Abstract

Malaria is the world’s most widespread protozoan infection, being responsible for more than 445,000 annual deaths. Among the malaria parasites, Plasmodium falciparum is the most prevalent and lethal. In this context, the search for new antimalarial drugs is urgently needed. P. falciparum superoxide dismutase (PfSOD) is an important enzyme involved in the defense mechanism against oxidative stress. The goal of this study was to identify through hierarchical screening on pharmacophore models and molecular dynamics (MD), promising allosteric PfSOD inhibitors that do not show structural requirements for human inhibition. MD simulations of 1000 ps were performed on PfSOD using GROMACS 5.1.2. For this, the AMBER99SB-ILDN force field was adapted to describe the metal-containing system. The simulations indicated stability in the developed system. Therefore, a covariance matrix was generated, in which it was possible to identify residues with correlated and anticorrelated movements with the active site. These results were associated with the results found in the predictor of allosteric sites, AlloSitePro, which affirmed the ability of these residues to delimit an allosteric site. Then, after successive filtering of the Sigma-Aldrich® compounds database for HsSOD1 and PfSOD pharmacophores, 152 compounds were selected, also obeying Lipinski’s rule of 5. Further filtering of those compounds based on molecular docking results, toxicity essays, availability, and price filtering led to the selection of a best compound, which was then submitted to MD simulations of 20,000 ps on the allosteric site. The study concludes that the ZINC00626080 compound could be assayed against SODs.

Plasmodium falciparum superoxide dismutase

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References

  1. WHO HOW (2017) World Malaria Report 2017

  2. Cowman AF, Healer J, Marapana D, Marsh K (2016) Malaria: biology and disease. Cell 167:610–624

    Article  CAS  PubMed  Google Scholar 

  3. Lasonder E, Green JL, Grainger M, Langsley G, Holder AA (2015) Extensive differential protein phosphorylation as intraerythrocytic Plasmodium falciparum schizonts develop into extracellular invasive merozoites. Proteomics 15:2716–2729

    Article  CAS  PubMed  Google Scholar 

  4. Leite FHA, Da Fonseca AL, Nunes RR, Comar Júnior M, Varotti FDP, Taranto AG (2013) Malária: Dos velhos fármacos aos novos alvos moleculares. Biochem Biotechnol Rep 2:59

    Article  Google Scholar 

  5. Braga CB e, Martins AC, Cayotopa ADE, Klein WW, Schlosser AR, Silva AF da, Souza MN de, Andrade BWB, Filgueira-Júnior JA, Pinto W de J, da Silva-Nunes M (2015) Side effects of chloroquine and primaquine and symptom reduction in malaria endemic area (Mâncio Lima, Acre, Brazil). Interdiscip Perspect Infect Dis 2015:1–7

  6. Antony HA, Parija SC (2016) Antimalarial drug resistance: an overview. Trop Parasitol 6:30–41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Spitzmüller A, Mestres J (2013) Prediction of the P. Falciparum target space relevant to malaria drug discovery. PLoS Comput Biol 9:e1003257

    Article  PubMed  PubMed Central  Google Scholar 

  8. Guido RVC, Oliva G, Andricopulo AD (2012) Structure- and ligand-based drug design approaches for neglected tropical diseases. Pure Appl Chem 84:1857–1866

    Article  CAS  Google Scholar 

  9. Sterling T, Irwin JJ (2015) ZINC 15 - ligand discovery for everyone. J Chem Inf Model 55:2324–2337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Walters WP, Stahl MT, Murcko MA (1998) Virtual screening—an overview. Drug Discov Today 3:160–178

    Article  CAS  Google Scholar 

  11. Lavecchia A, Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839–2860

    Article  CAS  PubMed  Google Scholar 

  12. Batool S, Ferdous S, M a K, Iftikhar H, Rashid S (2013) In silico screening for identification of novel aurora kinase inhibitors by molecular docking , dynamics simulations and ligand-based hypothesis approaches. Enzyme Eng 2:1–12

    Google Scholar 

  13. Shibi IG, Aswathy L, Jisha RS, Masand VH, Gajbhiye JM (2016) Virtual screening techniques to probe the antimalarial activity of some traditionally used phytochemicals. Comb Chem High Throughput Screen 19:572–591

    Article  CAS  PubMed  Google Scholar 

  14. Nunes RR, dos Santos Costa M, dos Reis Santos B, da Fonseca AL, Ferreira LS, Russo Chagas RC, da Silva AM, de Pilla Varotti F, Taranto AG (2016) Successful application of virtual screening and molecular dynamics simulations against antimalarial molecular targets. Mem Inst Oswaldo Cruz 111:721–730

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Carillon J, Rouanet J-M, Cristol J-P, Brion R (2013) Superoxide dismutase administration, a potential therapy against oxidative stress related diseases: several routes of supplementation and proposal of an original mechanism of action. Pharm Res 30:2718–2728

    Article  CAS  PubMed  Google Scholar 

  16. van Schalkwyk DA, Saliba KJ, Biagini GA, Bray PG, Kirk K (2013) Loss of pH control in Plasmodium falciparum parasites subjected to oxidative stress. PLoS One 8:e58933

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cacciapuoti F (2016) Oxidative stress as “mother” of many human diseases at strong clinical impact. J Cardiovasc Med Cardiol 3:001–006

    Article  Google Scholar 

  18. Banci L, Bertini I, Cantini F, Kozyreva T, Massagni C, Palumaa P, Rubino JT, Zovo K (2012) Human superoxide dismutase 1 (hSOD1) maturation through interaction with human copper chaperone for SOD1 (hCCS). Proc Natl Acad Sci 109:13555–13560

    Article  PubMed  Google Scholar 

  19. Yuan Y, Pei J, Lai L (2013) Binding site detection and druggability prediction of protein targets for structure-based drug design. Curr Pharm Des 19:2326–2333

    Article  CAS  PubMed  Google Scholar 

  20. Feng Z, Hu G, Ma S, Xie X-Q (2015) Computational advances for the development of allosteric modulators and bitopic ligands in G protein-coupled receptors. AAPS J 17:1080–1095

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lee C-H, Huang H-C, Juan H-F (2011) Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor. Int J Mol Sci 12:5304–5318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718

    Article  CAS  Google Scholar 

  23. Soulère L, Delplace P, Davioud-Charvet E, Py S, Sergheraert C, Périé J, Ricard I, Hoffmann P, Dive D (2003) Screening of Plasmodium falciparum iron superoxide dismutase inhibitors and accuracy of the SOD-assays. Bioorg Med Chem 11:4941–4944

    Article  CAS  PubMed  Google Scholar 

  24. Berendsen HJC, Grigera JR, Straatsma TP (1987) The missing term in effective pair potentials. J Phys Chem 91:6269–6271

    Article  CAS  Google Scholar 

  25. Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: a linear constraint solver for molecular simulations. Chem 18:1463–1472

    CAS  Google Scholar 

  26. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N · log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092

    Article  CAS  Google Scholar 

  27. Daura X, van Gunsteren WF, Mark AE (1999) Folding-unfolding thermodynamics of a beta-heptapeptide from equilibrium simulations. Proteins Struct Funct Genet 34:269–280

    Article  CAS  PubMed  Google Scholar 

  28. Huang W, Lu S, Huang Z, Liu X, Mou L, Luo Y, Zhao Y, Liu Y, Chen Z, Hou T, Zhang J (2013) Allosite: a method for predicting allosteric sites. Bioinformatics 29:2357–2359

    Article  CAS  PubMed  Google Scholar 

  29. Dong X, Zhang Z, Zhao J, Lei J, Chen Y, Li X, Chen H, Tian J, Zhang D, Liu C, Liu C (2016) The rational design of specific SOD1 inhibitors via copper coordination and their application in ROS signaling research. Chem Sci 00:1–12

    Google Scholar 

  30. Chemaxon (2013) MarvinSketch. https://chemaxon.com/products/marvin. Accessed 3 Jan 2015.

  31. Tripos Inc. (2012) SYBYL-X: discovery software for computational chemistry and molecular modelling. Tripos Inc, Saint Louis

  32. Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582–6594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development. Adv Drug Deliv Rev 46:3–26

    Article  CAS  PubMed  Google Scholar 

  34. Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. de Souza, BC (2017) Triagem virtual hierárquica para seleção de potenciais inibidores seletivos para a superóxido dismutase de P. falciparum. Universidade Estadual de Feira de Santana

  36. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera - a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    Article  CAS  PubMed  Google Scholar 

  37. Trott O, Olson AJ (2009) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461

  38. Allen WJ, Balius TE, Mukherjee S, Brozell SR, Moustakas DT, Lang PT, Case DA, Kuntz ID, Rizzo RC (2015) DOCK 6: impact of new features and current docking performance. J Comput Chem 36:1132–1156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Frishman D, Argos P (1995) Knowledge-based protein secondary structure assignment. Proteins Struct Funct Genet 23:566–579

    Article  CAS  PubMed  Google Scholar 

  41. Ferrin TE, Huang CC, Jarvis LE, Langridge R (1988) The MIDAS display system. J Mol Graph 6:13–27

    Article  CAS  Google Scholar 

  42. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288

    Article  CAS  PubMed  Google Scholar 

  43. Meng EC, Shoichet BK, Kuntz ID (1992) Automated docking with grid-based energy evaluation. J Comput Chem 13:505–524

    Article  CAS  Google Scholar 

  44. Patlewicz G, Jeliazkova N, Safford RJ, Worth AP, Aleksiev B (2008) An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res 19:495–524

    Article  CAS  PubMed  Google Scholar 

  45. Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M (2015) PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Res 43:W443–W447

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Malde AK, Zuo L, Breeze M, Stroet M, Poger D, Nair PC, Oostenbrink C, Mark AE (2011) An automated force field topology builder (ATB) and repository: version 1.0. J Chem Theory Comput 7:4026–4037

    Article  CAS  PubMed  Google Scholar 

  47. Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins S8:1950–8

  48. Schrödinger LLC (2016) PyMol: the PyMOL molecular graphics system, version 2.0. Schrödinger LLC., New York

  49. Li P, Roberts BP, Chakravorty DK, Merz KM (2013) Rational Design of Particle Mesh Ewald Compatible Lennard-Jones Parameters for +2 metal cations in explicit solvent. J Chem Theory Comput 9:2733–2748

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Verli H (2017) Bioinformática: da Biologia à Flexibilidade Moleculares

  51. Branco RJF, Fernandes PA, Ramos MJ (2006) Molecular dynamics simulations of the enzyme cu, Zn superoxide dismutase. J Phys Chem B 110:16754–16762

    Article  CAS  PubMed  Google Scholar 

  52. Petrov D, Daura X, Zagrovic B (2016) Effect of oxidative damage on the stability and dimerization of superoxide dismutase 1. Biophys J 110:1499–1509

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Lobanov MY, Bogatyreva NS, Galzitskaya OV (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42:623–628

    Article  CAS  Google Scholar 

  54. Boucher IW, Brzozowski AM, Brannigan JA, Schnick C, Smith DJ, Kyes SA, Wilkinson AJ (2006) The crystal structure of superoxide dismutase from Plasmodium falciparum. BMC Struct Biol 6:1–10

    Article  CAS  Google Scholar 

  55. Naithani A, Taylor P, Erman B, Walkinshaw MD (2015) A molecular dynamics study of allosteric transitions in Leishmania mexicana pyruvate kinase. Biophys J 109:1149–1156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Tommaso P, Di MS, Xenarios I, Orobitg M, Notredame C (2011) T-coffee : a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension. Nucleic Acids Res 39:13–17

    Article  CAS  Google Scholar 

  57. Croft AM, Jacquerioz FA, Jones KL (2010) Human parasitic diseases drugs to prevent malaria in Travellers: a systematic review of randomized controlled trials. Hum Parasit Dis 2:1–19

    Article  CAS  Google Scholar 

  58. Meslamani J, Li J, Sutter J, Stevens A, Bertrand H-O, Rognan D (2012) Protein–ligand-based pharmacophores: generation and utility assessment in computational ligand profiling. J Chem Inf Model 52:943–955

    Article  CAS  PubMed  Google Scholar 

  59. Poptodorov K, Luu T, Hoffmann RD (2006) Pharmacophore model generation software tools. In: Langer T, Hoffmann RD (eds) Pharmacophores and pharmacophore searches, 1st edn, pp 15–47

    Chapter  Google Scholar 

  60. Caballero J (2010) 3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds. J Mol Graph Model 29:363–371

    Article  CAS  PubMed  Google Scholar 

  61. Zhao X, Yuan M, Huang B, Ji H, Zhu L (2010) Ligand-based pharmacophore model of N-aryl and N-Heteroaryl piperazine α1A-adrenoceptors antagonists using GALAHAD. J Mol Graph Model 29:126–136

    Article  CAS  PubMed  Google Scholar 

  62. Brito CCB (2013) Avaliação e identificação de inibidores da enzima superóxido dismutase de tripanosomatídeos. Universidade Federal da Bahia

  63. Sánchez-Moreno M, Gómez-Contreras F, Navarro P, Marín C, Ramírez-Macías I, Olmo F, Sanz AM, Campayo L, Cano C, Yunta MJR (2012) In vitro leishmanicidal activity of imidazole- or pyrazole-based benzo[g]phthalazine derivatives against Leishmania infantum and Leishmania braziliensis species. J Antimicrob Chemother 67:387–397

    Article  CAS  PubMed  Google Scholar 

  64. Braga RC, Andrade CH (2013) Assessing the performance of 3D pharmacophore models in virtual screening: how good are they? Curr Top Med Chem 13:1127–1138

    Article  CAS  PubMed  Google Scholar 

  65. Jain AN, Nicholls A (2008) Recommendations for evaluation of computational methods. J Comput Aided Mol Des 22:133–139

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Empereur-mot C, Guillemain H, Latouche A, Zagury J-F, Viallon V, Montes M (2015) Predictiveness curves in virtual screening. J Cheminform 7:52

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Hart PJ, Balbirnie MM, Ogihara NL, Nersissian AM, Weiss MS, Valentine JS, Eisenberg D (1999) A structure-based mechanism for copper−zinc superoxide dismutase. Biochemistry 38:2167–2178

    Article  CAS  PubMed  Google Scholar 

  68. Teixeira C, Vale N, Pérez B, Gomes A, Gomes JRB, Gomes P (2014) “Recycling” classical drugs for malaria. Chem Rev 114:11164–11220

    Article  CAS  PubMed  Google Scholar 

  69. Fracasso D, Fuentefria AM, Teixeira ML (2009) Avaliação toxicológica e quantificação de agentes antioxidantes em vinhos tintos comercializados no município de Concórdia. Revista Brasileira de Produtos Agroindustriais, 11:181–189

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Acknowledgments

The authors would like to thank the Universidade Estadual de Feira de Santana for computational resources and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for financial support.

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Authors and Affiliations

  1. Programa de pós-graduação em Ciências Farmacêuticas, Universidade Estadual de Feira de Santana, Bahia, Brazil

    Janay Stefany Carneiro Araujo, David Bacelar Costa Junior, Larissa de Mattos Oliveira, Manoelito Coelho dos Santos Junior & Franco Henrique Andrade Leite

  2. Programa de pós-graduação em Biotecnologia, Universidade Estadual de Feira de Santana, Bahia, Brazil

    Bruno Cruz de Souza, Isis Bugia Santana, Manoelito Coelho dos Santos Junior & Franco Henrique Andrade Leite

  3. Programa de pós-graduação em Computação Aplicada, Universidade Estadual de Feira de Santana, Bahia, Brazil

    Angelo Amâncio Duarte

  4. Serviço Social da Indústria, Bahia, Brazil

    Pedro Sousa Lacerda

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  1. Janay Stefany Carneiro Araujo
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  2. Bruno Cruz de Souza
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Correspondence to Franco Henrique Andrade Leite.

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This paper belongs to Topical Collection XIX - Brazilian Symposium of Theoretical Chemistry (SBQT2017)

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Araujo, J.S.C., de Souza, B.C., Costa Junior, D.B. et al. Identification of new promising Plasmodium falciparum superoxide dismutase allosteric inhibitors through hierarchical pharmacophore-based virtual screening and molecular dynamics. J Mol Model 24, 220 (2018). https://doi.org/10.1007/s00894-018-3746-0

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