全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

A Novel Inhibitor of Mammalian Triosephosphate Isomerase Found by an In Silico Approach

DOI: 10.1155/2014/469125

Full-Text   Cite this paper   Add to My Lib

Abstract:

Triosephosphate isomerase (TIM) is an essential, highly conserved component of glycolysis. Tumors are often dependent on glycolysis for energy and metabolite production (the Warburg effect). Glycolysis inhibitors thus show promise as cancer treatments. TIM inhibition, unlike inhibition of other glycolysis enzymes, also produces toxic methylglyoxal targeted to regions of high glycolysis, an effect that might also be therapeutically useful. Thus TIM is an attractive drug target. A total of 338,562 lead-like molecules were analyzed computationally to find TIM inhibitors by an efficient “double screen” approach. The first fragment-sized compounds were studied using structure-based virtual screening to identify binding motifs for mammalian TIM. Subsequently, larger compounds, filtered to meet the binding criteria developed in the first analysis, were ranked using a second round of structure-based virtual screening. A compound was found that inhibited mammalian TIM in vitro in the micromolar range. Docking and molecular dynamics (MD) suggested that the inhibitor made hydrogen bond contacts with TIM catalytic residues. In addition, hydrophobic contacts were made throughout the binding site. All predicted inhibitor-TIM interactions involved TIM residues that were highly conserved. The discovered compound may provide a scaffold for elaboration of other inhibitors. 1. Introduction Glycolysis plays a central role in some tumor types. Many cancer cells are especially dependent on aerobic glycolysis for energy and metabolites. This dependence is known as the Warburg effect [1]. Antiglycolytic drugs acting at various steps of the glycolysis pathway have shown potential to kill or impede tumors alone or in combination with classic drugs [2–4]. To date, no TIM inhibitors suitable for targeting mammalian TIM have been reported. The cell can control glucose metabolism to some extent via TP53 [5]. In a cellular process, TP53 signaling can inhibit the Warburg effect and shift tumor glycolysis flux, converting cells to a less transformed phenotype [6]. In part this normalization is due to a shift of glucose metabolism away from glycolysis and into oxidative phosphorylation and the pentose phosphate pathways [5, 6]. TIM is a key enzyme in glycolysis catalyzing the conversion of dihydroxyacetone phosphate to glyceraldehyde-3-phosphate [7]. TIM is an essential protein, and partial function mutations in hTPI1 are incompletely tolerated in humans [8]. Deficiency phenotypes for TIM are complicated by the accumulation of its substrate, dihydroxyacetone phosphate, which is

References

[1]  R. A. Gatenby and R. J. Gillies, “Glycolysis in cancer: a potential target for therapy,” International Journal of Biochemistry and Cell Biology, vol. 39, no. 7-8, pp. 1358–1366, 2007.
[2]  M. Seo, J.-D. Kim, D. Neau, I. Sehgal, and Y.-H. Lee, “Structure-based development of small molecule PFKFB3 inhibitors: a framework for potential cancer therapeutic agents targeting the Warburg effect,” PLoS ONE, vol. 6, no. 9, Article ID e24179, 2011.
[3]  J. Xie, B. S. Wang, D. H. Yu et al., “Dichloroacetate shifts the metabolism from glycolysis to glucose oxidation and exhibits synergistic growth inhibition with cisplatin in HeLa cells,” International Journal of Oncology, vol. 38, no. 2, pp. 409–417, 2011.
[4]  S. Ganapathy-Kanniappan, R. Kunjithapatham, and J. F. Geschwind, “Glyceraldehyde-3-phosphate dehydrogenase: a promising target for molecular therapy in hepatocellular carcinoma,” Oncotarget, vol. 3, no. 9, pp. 940–953, 2012.
[5]  E. Madan, R. Gogna, M. Bhatt, U. Pati, P. Kuppusamy, and A. A. Mahdi, “Regulation of glucose metabolism by p53: emerging new roles for the tumor suppressor,” Oncotarget, vol. 2, no. 12, pp. 948–957, 2011.
[6]  C. Wanka, J. P. Steinbach, and J. Rieger, “Tp53-induced glycolysis and apoptosis regulator (TIGAR) protects glioma cells from starvation-induced cell death by up-regulating respiration and improving cellular redox homeostasis,” The Journal of Biological Chemistry, vol. 287, no. 40, pp. 33436–33446, 2012.
[7]  M. K. Go, A. Koudelka, T. L. Amyes, and J. P. Richard, “Role of Lys-12 in catalysis by triosephosphate isomerase: a two-part substrate approach,” Biochemistry, vol. 49, no. 25, pp. 5377–5389, 2010.
[8]  F. Orosz, J. Oláh, and J. Ovádi, “Triosephosphate isomerase deficiency: new insights into an enigmatic disease,” Biochimica et Biophysica Acta—Molecular Basis of Disease, vol. 1792, no. 12, pp. 1168–1174, 2009.
[9]  R. Aparicio, S. T. Ferreira, and I. Polikarpov, “Closed conformation of the active site loop of rabbit muscle triosephosphate isomerase in the absence of substrate: evidence of conformational heterogeneity,” Journal of Molecular Biology, vol. 334, no. 5, pp. 1023–1041, 2003.
[10]  V. Olivares-Illana, A. Rodríguez-Romero, I. Becker et al., “Perturbation of the dimer interface of triosephosphate isomerase and its effect on Trypanosoma cruzi,” PLoS Neglected Tropical Diseases, vol. 1, no. 1, article e1, 2007.
[11]  G. álvarez, B. Aguirre-López, J. Varela et al., “Massive screening yields novel and selective Trypanosoma cruzi triosephosphate isomerase dimer-interface-irreversible inhibitors with anti-trypanosomal activity,” European Journal of Medicinal Chemistry, vol. 45, no. 12, pp. 5767–5772, 2010.
[12]  M. M. Mysinger, D. R. Weiss, J. J. Ziarek et al., “Structure-based ligand discovery for the protein-protein interface of chemokine receptor CXCR4,” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 14, pp. 5517–5522, 2012.
[13]  V. Katritch, V. P. Jaakola, J. R. Lane et al., “Structure-based discovery of novel chemotypes for adenosine A2A receptor antagonists,” Journal of Medicinal Chemistry, vol. 53, no. 4, pp. 1799–1809, 2010.
[14]  A. Lavecchia, G. C. Di, A. Pesapane et al., “Discovery of new inhibitors of Cdc25B dual specificity phosphatases by structure-based virtual screening,” Journal of Medicinal Chemistry, vol. 55, no. 9, pp. 4142–4158, 2012.
[15]  S. Li, X. Sun, H. Zhao, Y. Tang, and M. Lan, “Discovery of novel EGFR tyrosine kinase inhibitors by structure-based virtual screening,” Bioorganic and Medicinal Chemistry Letters, vol. 22, no. 12, pp. 4004–4009, 2012.
[16]  D. A. Gshwend, A. C. Good, and I. D. Kuntz, “Molecular docking towards drug discovery,” Journal of Molecular Recognition, vol. 9, no. 2, pp. 175–186, 1996.
[17]  M. W. Chang, C. Ayeni, S. Breuer, and B. E. Torbett, “Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina,” PLoS ONE, vol. 5, no. 8, Article ID e11955, 2010.
[18]  O. Trott and A. J. Olson, “Software news and update AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading,” Journal of Computational Chemistry, vol. 31, no. 2, pp. 455–461, 2010.
[19]  J. J. Irwin and B. K. Shoichet, “ZINC—a free database of commercially available compounds for virtual screening,” Journal of Chemical Information and Modeling, vol. 45, no. 1, pp. 177–182, 2005.
[20]  D. A. Case, T. E. Cheatham III, T. Darden et al., “The Amber biomolecular simulation programs,” Journal of Computational Chemistry, vol. 26, no. 16, pp. 1668–1688, 2005.
[21]  B. Kuhn and P. A. Kollman, “Binding of a diverse set of ligands to avidin and streptavidin: an accurate quantitative prediction of their relative affinities by a combination of molecular mechanics and continuum solvent models,” Journal of Medicinal Chemistry, vol. 43, no. 20, pp. 3786–3791, 2000.
[22]  E. Lolis and G. A. Petsko, “Crystallographic analysis of the complex between triosephosphate isomerase and 2-phosphoglycolate at 2.5-? resolution: implications for catalysis,” Biochemistry, vol. 29, no. 28, pp. 6619–6625, 1990.
[23]  R. Gozalbes, R. J. Carbajo, and A. Pineda-Lucena, “Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery,” Current Medicinal Chemistry, vol. 17, no. 17, pp. 1769–1794, 2010.
[24]  M. Hennig, A. Ruf, and W. Huber, “Combining biophysical screening and X-ray crystallography for fragment-based drug discovery,” Topics in Current Chemistry, vol. 317, pp. 115–143, 2012.
[25]  G. Rastelli, A. Del Rio, G. Degliesposti, and M. Sgobba, “Fast and accurate predictions of binding free energies using MM-PBSA and MM-GBSA,” Journal of Computational Chemistry, vol. 31, no. 4, pp. 797–810, 2010.
[26]  W. J. Egan, “Predicting ADME properties in drug discovery,” in Drug Design: Structure- and Ligand-Based Approaches, K. M. Mertz, D. Ringe, and C. H. Reynolds, Eds., pp. 165–180, Cambridge University Press, New York, NY, USA, 2010.
[27]  M. Salin, E. G. Kapetaniou, M. Vaismaa et al., “Crystallographic binding studies with an engineered monomeric variant of triosephosphate isomerase,” Acta Crystallographica D: Biological Crystallography, vol. 66, no. 8, pp. 934–944, 2010.
[28]  S. Surade and T. L. Blundell, “Structural biology and drug discovery of difficult targets: the limits of ligandability,” Chemistry and Biology, vol. 19, no. 1, pp. 42–50, 2012.
[29]  J. Kyte and R. F. Doolittle, “A simple method for displaying the hydropathic character of a protein,” Journal of Molecular Biology, vol. 157, no. 1, pp. 105–132, 1982.
[30]  D. G. Higgins and W. R. Taylor, “Multiple sequence alignment,” Methods in Molecular Biology, vol. 143, pp. 1–18, 2000.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133