Fragment based group QSAR and molecular dynamics mechanistic studies on arylthioindole derivatives targeting the α-β interfacial site of human tubulin

BMC Genomics

Volumn 15, Issue , 2014, Pages

  Tyagi, Chetna ^a   Gupta, Ankita ^b   Goyal, Sukriti ^c   Dhanjal, Jaspreet Kaur ^a   Grover, Abhinav ^a  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

^b DELHI TECHNOLOGICAL UNIVERSITY   (India)

^c BANASTHALI UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALPHA TUBULIN; ANTINEOPLASTIC AGENT; ARYLTHIOINDOLE DERIVATIVE; BETA TUBULIN; UNCLASSIFIED DRUG; COLCHICINE; INDOLE DERIVATIVE; TUBULIN; TUBULIN MODULATOR;

ANTINEOPLASTIC ACTIVITY; ARTICLE; BINDING AFFINITY; BINDING SITE; CONTROLLED STUDY; DRUG DETERMINATION; DRUG MECHANISM; DRUG PROTEIN BINDING; DRUG STRUCTURE; MATHEMATICAL MODEL; MOLECULAR DOCKING; MOLECULAR DYNAMICS; MOLECULAR MODEL; PREDICTION; PROCESS DEVELOPMENT; PROTEIN FUNCTION; PROTEIN INTERACTION; PROTEIN TARGETING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STRUCTURE ANALYSIS; VALIDATION PROCESS; APOPTOSIS; CHEMISTRY; DRUG DEVELOPMENT; DRUG EFFECTS; HUMAN; METABOLISM; MICROTUBULE; PROTEIN MULTIMERIZATION; PROTEIN QUATERNARY STRUCTURE; SEQUENCE HOMOLOGY;

APOPTOSIS; BINDING SITES; COLCHICINE; DRUG DISCOVERY; HUMANS; INDOLES; MICROTUBULES; MOLECULAR DOCKING SIMULATION; MOLECULAR DYNAMICS SIMULATION; PROTEIN MULTIMERIZATION; PROTEIN STRUCTURE, QUATERNARY; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SEQUENCE HOMOLOGY, AMINO ACID; TUBULIN; TUBULIN MODULATORS;

EID: 84932631623     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/1471-2164-15-S9-S3     Document Type: Article

References (68)

1. Checchi PM, Nettles JH, Zhou J, Snyder JP, Joshi HC: Microtubule-interacting drugs for cancer treatment. Trends Pharmacol Sci 2003, 24(7):361-365.
2. Nicolaou K, Vourloumis D, Li T, Pastor J, Winssinger N, He Y, Ninkovic S, Sarabia F, Vallberg H, Roschangar F: Designed epothilones: Combinatorial synthesis, tubulin assembly properties, abd cytotoxic action against taxol-resistant tumor ells. Angew Chem Int Edit 1997, 36(19):2097-2103.
3. Pettit GR, Singh SB, Niven ML, Hamel E, Schmidt JM: Isolation, structure, and synthesis of combretastatins A-1 and B-1, potent new inhibitors of microtubule assembly, derived from Combretum caffrum. J Nat Prod 1987, 50(1):119-131.
4. Mooberry SL: Microtubules as a target for anticancer drugs. New Frontiers and Treatment Paradigms for Metastatic Breast Cancer 2011, 28:7.
5. Lockwood AH: Tubulin assembly protein: immunochemical and immunofluorescent studies on its function and distribution in microtubules and cultured cells. Cell 1978, 13(4):613-627.
6. Nogales E: Structural insights into microtubule function. Annu Rev Biochem 2000, 69(1):277-302.
7. Hammond JW, Cai D, Verhey KJ: Tubulin modifications and their cellular functions. Curr Opin Cell Biol 2008, 20(1):71-76.
8. Pellegrini F, Budman DR: Review: tubulin function, action of antitubulin drugs, and new drug development. Cancer Invest 2005, 23(3):264-273.
9. Prinz H, Ishii Y, Hirano T, Stoiber T, Camacho Gomez JA, Schmidt P, Düssmann H, Burger AM, Prehn JH, Günther EG: Novel benzylidene-9 (10 H)-anthracenones as highly active antimicrotubule agents. Synthesis, antiproliferative activity, and inhibition of tubulin polymerization. J Med Chem 2003, 46(15):3382-3394.
10. Prakasham A, Saxena A, Luqman S, Chanda D, Kaur T, Gupta A, Yadav D, Chanotiya C, Shanker K, Khan F: Synthesis and anticancer activity of 2- benzylidene indanones through inhibiting tubulin polymerization. Bioorgan Med Chem 2012, 20(9):3049-3057.
11. Wilson L, Meza I: The mechanism of action of colchicine. Colchicine binding properties of sea urchin sperm tail outer doublet tubulin. J Cell Biol 1973, 58(3):709-719.
12. Ravelli RB, Gigant B, Curmi PA, Jourdain I, Lachkar S, Sobel A, Knossow M:Insight into tubulin regulation from a complex with colchicine and a stathmin-like domain. Nature 2004, 428(6979):198-202.
13. Jordan MA, Thrower D, Wilson L: Effects of vinblastine, podophyllotoxin and nocodazole on mitotic spindles. Implications for the role of microtubule dynamics in mitosis. J cell Sci 1992, 102(3):401-416.
14. Lin CM, Ho HH, Pettit GR, Hamel E: Antimitotic natural products combretastatin A-4 and combretastatin A-2: studies on the mechanism of their inhibition of the binding of colchicine to tubulin. Biochemistry 1989, 28(17):6984-6991.
15. Shan YS, Zhang J, Liu Z, Wang M, Dong Y: Developments of combretastatin A-4 derivatives as anticancer agents. Curr Med Chem 2011, 18(4):523-538.
16. Risinger AL, Mooberry SL: Microtubules as a target in cancer therapy. Cytoskeleton and Human Disease Springer; 2012, 203-221.
17. Beckers T, Mahboobi S: Natural, semisynthetic and synthetic microtubule inhibitors for cancer therapy. Drugs Future 2003, 28:767-785.
18. Mani S, Macapinlac M Jr, Goel S, Verdier-Pinard D, Fojo T, Rothenberg M, Colevas D: The clinical development of new mitotic inhibitors that stabilize the microtubule. Anti-cancer Drug 2004, 15(6):553-558.
19. De Martino G, La Regina G, Coluccia A, Edler MC, Barbera MC, Brancale A, Wilcox E, Hamel E, Artico M, Silvestri R: Arylthioindoles, potent inhibitors of tubulin polymerization. J Med Chem 2004, 47(25):6120-6123.
20. De Martino G, Edler MC, La Regina G, Coluccia A, Barbera MC, Barrow D, Nicholson RI, Chiosis G, Brancale A, Hamel E: New arylthioindoles: potent inhibitors of tubulin polymerization. 2. Structure-activity relationships and molecular modeling studies. J Med Chem 2006, 49(3):947-954.
21. Coluccia A, Sabbadin D, Brancale A: Molecular modelling studies on arylthioindoles as potent inhibitors of tubulin polymerization. Eur J Med Chem 2011, 46(8):3519-3525.
22. Lu Y, Chen J, Xiao M, Li W, Miller DD: An overview of tubulin inhibitors that interact with the colchicine binding site. Pharm Res 2012, 29(11):2943-2971.
23. Massarotti A, Coluccia A, Silvestri R, Sorba G, Brancale A: The tubulin colchicine domain: a molecular modeling perspective. Chem Med Chem 2012, 7(1):33-42.
24. Nogales E, Wolf SG, Downing KH: Structure of the αβ tubulin dimer by electron crystallography. Nature 1998, 391(6663):199-203.
25. Liao Sy, Miao Tf, Chen Jc, Lu Hl, Zheng Kc: Molecular modeling and design of arylthioindole derivatives as tubulin inhibitors. Chin J Chem Phy 2009, 22(5):473-480.
26. Brancale A, Silvestri R: Indole, a core nucleus for potent inhibitors of tubulin polymerization. Med Res Rev 2007, 27(2):209-238.
27. Goyal M, Dhanjal JK, Goyal S, Tyagi C, Hamid R, Grover A: Development of dual inhibitors against alzheimer's disease using fragment-based QSAR and molecular docking. BioMed Res Int 2014, 2014:12.
28. Ajmani S, Jadhav K, Kulkarni SA: Group-Based QSAR (G-QSAR): Mitigating interpretation challenges in QSAR. QSAR Comb Sci 2009, 28(1):36-51.
29. Goodarzi M, da Cunha EF, Freitas MP, Ramalho TC: QSAR and docking studies of novel antileishmanial diaryl sulfides and sulfonamides. Eur J Med Chem 2010, 45(11):4879-4889.
30. Goyal S, Dhanjal JK, Tyagi C, Goyal M, Grover A: Novel fragment-based QSAR modeling and combinatorial design of pyrazole-derived CRK3 inhibitors as potent antileishmanials. Chem Biol Drug Des 2014, 84(1):54-62.
31. Goyal S, Grover S, Dhanjal JK, Tyagi C, Goyal M, Grover A: Group-based QSAR and molecular dynamics mechanistic analysis revealing the mode of action of novel piperidinone derived protein-protein inhibitors of p53-MDM2. J Mol Graph Model 2014, 51:64-72.
32. Tyagi C, Grover S, Dhanjal J, Goyal S, Goyal M, Grover A: Mechanistic insights into mode of action of novel natural cathepsin L inhibitors. BMC Genomics 2013, 14(Suppl 8):S10.
33. Ajmani S, Kulkarni SA: Application of GQSAR for scaffold hopping and lead optimization in multitarget inhibitors. Mol Inform 2012, 31(6-7):473-490.
34. Giansanti V, Piscitelli F, Camboni T, Prosperi E, La Regina G, Parks M, Silvestri R, Scovassi AI: Arylthioindoles: Promising compounds against cancer cell proliferation. Oncol Lett 2010, 1(1):109-112.
35. La Regina G, Edler MC, Brancale A, Kandil S, Coluccia A, Piscitelli F, Hamel E, De Martino G, Matesanz R, Díaz JF: Arylthioindole inhibitors of tubulin polymerization. 3. Biological evaluation, structure-activity relationships and molecular modeling studies. J Med Chem 2007, 50(12):2865-2874.
36. MarvinSketch: v. 5.12.1, ChemAxon; 2013 [http://www.chemaxon.com].
37. VLifeMDS: Molecular Design Suite: v. 4.3, VLife Sciences Technologies Pvt. Ltd., Pune, India; 2010.
38. Wold S, Sjöström M, Eriksson L: PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab 2001, 58(2):109-130.
39. Rucker C, Rucker G, Meringer M: y-Randomization and its variants in QSPR/QSAR. J Chem Inf Model 2007, 47(6):2345-2357.
40. Schrodinger suite: Schrodinger, LLC, New York, NY; 2009.
41. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK: 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.
42. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL:Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004, 47(7):1750-1759.
43. Sali A, Blundell TL: Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 1993, 234(3):779-815.
44. Fiser A, Do RKG, Šali A: Modeling of loops in protein structures. Protein Sci 2000, 9(9):1753-1773.
45. Eswar N, Webb B, Marti-Renom MA, Madhusudhan M, Eramian D, Shen My, Pieper U, Sali A: Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics 2006, 5.6.1-5.6.30.
46. Martí-Renom MA, Stuart AC, Fiser A, Sánchez R, Melo F, Šali A: Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Bio 2000, 29(1):291-325.
47. Bhattacharya A, Tejero R, Montelione GT: Evaluating protein structures determined by structural genomics consortia. Proteins 2007, 66(4):778-795.
48. Joosten RP, Te Beek TA, Krieger E, Hekkelman ML, Hooft RW, Schneider R, Sander C, Vriend G: A series of PDB related databases for everyday needs. Nucleic Acids Res 2011, 39(suppl 1):D411-D419.
49. Kabsch W, Sander C: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22(12):2577-2637.
50. Zimmerman DE, Kulikowski CA, Huang Y, Feng W, Tashiro M, Shimotakahara S, Chien C-y, Powers R, Montelione GT: Automated analysis of protein NMR assignments using methods from artificial intelligence. J Mol Biol 1997, 269(4):592-610.
51. Huang YJ, Powers R, Montelione GT: Protein NMR recall, precision, and F-measure scores (RPF scores): structure quality assessment measures based on information retrieval statistics. J Am Chem Soc 2005, 127(6):1665-1674.
52. Huang YJ, Tejero R, Powers R, Montelione GT: A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins 2006, 62(3):587-603.
53. Eisenberg D, Lüthy R, Bowie JU: VERIFY3D: assessment of protein models with three-dimensional profiles. Method Enzymol 1997, 277:396.
54. Sippl MJ: Recognition of errors in three-dimensional structures of proteins. Proteins 1993, 17(4):355-362.
55. Laskowski RA, MacArthur MW, Moss DS, Thornton JM: PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993, 26(2):283-291.
56. Davis IW, Leaver-Fay A, Chen VB, Block JN, Kapral GJ, Wang X, Murray LW, Arendall WB, Snoeyink J, Richardson JS: MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res 2007, 35(suppl 2):W375-W383.
57. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W: Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aid Mol Des 2013, 27(3):221-234.
58. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT: Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 2006, 49(21):6177-6196.
59. Goyal M, Grover S, Dhanjal JK, Goyal S, Tyagi C, Chacko S, Grover A: Novel natural structure corrector of ApoE4 for checking Alzheimer's disease:benefits from high throughput screening and molecular dynamics simulations. Biomed Res Int 2013, 2013:620793.
60. Dhanjal JK, Grover S, Sharma S, Singh A, Grover A: Structural insights into mode of actions of novel natural Mycobacterium protein tyrosine phosphatase B inhibitors. BMC Genomics 2014, 15(Suppl 1):S3.
61. Dhanjal JK, Goyal S, Sharma S, Hamid R, Grover A: Mechanistic insights into mode of action of potent natural antagonists of BACE-1 for checking Alzheimer's plaque pathology. Biochem Biophys Res Commun 2014, 443(3):1054-1059.
62. Wallace AC, Laskowski RA, Thornton JM: LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 1995, 8(2):127-134.
63. Beckstein O, Fourrier A, Iorga BI: Prediction of hydration free energies for the SAMPL4 diverse set of compounds using molecular dynamics simulations with the OPLS-AA force field. J Comput Aid Mol Des 2014, 28(3):265-276.
64. Guo Z, Mohanty U, Noehre J, Sawyer TK, Sherman W, Krilov G: Probing the alpha-helical structural stability of stapled p53 peptides: molecular dynamics simulations and analysis. Chem Biol Drug Des 2010, 75(4):348-359.
65. Bowers KJ, Chow E, Xu H, Dror RO, Eastwood MP, Gregersen BA, Klepeis JL, Kolossvary I, Moraes MA, Sacerdoti FD: Scalable algorithms for molecular dynamics simulations on commodity clusters. SC 2006 Conference, Proceedings of the ACM/IEEE: 2006 IEEE; 2006, 43-43.
66. Essmann U, Perera L, Berkowitz ML, Darden T, Lee H, Pedersen LG: A smooth particle mesh Ewald method. J Chem Phys 1995, 103(19):8577-8593.
67. Quikprop: v. 3.4, Schrodinger, LLC, New York, NY; 2011.
68. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, Lee PW, Tang Y: admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 2012, 52(11):3099-3105.