QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery

Molecules

Volumn 20, Issue 3, 2015, Pages 4848-4873

  Pereira, Florbela ^a   Latino, Diogo A R S ^a,b   Gaudêncio, Susana P ^a  

^a DEPARTAMENTO DE QUÍMICA   (Portugal)

^b UNIVERSITY OF LISBON   (Portugal)

Author keywords

Antibiotic; Antitumor; Drug discovery; Marine natural products; Microbial natural products; Quantitative structure activity relationships (QSAR); Semi empirical quantum chemical descriptors

Indexed keywords

ANTIINFECTIVE AGENT; ANTINEOPLASTIC AGENT; BIOLOGICAL PRODUCT;

AQUATIC SPECIES; CHEMICAL DATABASE; CHEMICAL STRUCTURE; CHEMISTRY; DRUG DEVELOPMENT; DRUG SCREENING; ISOLATION AND PURIFICATION; MACHINE LEARNING; PROCEDURES; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

ANTI-BACTERIAL AGENTS; ANTINEOPLASTIC AGENTS; AQUATIC ORGANISMS; BIOLOGICAL PRODUCTS; DATABASES, CHEMICAL; DRUG DISCOVERY; DRUG EVALUATION, PRECLINICAL; MACHINE LEARNING; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84929587524     PISSN: None     EISSN: 14203049     Source Type: Journal    
DOI: 10.3390/molecules20034848     Document Type: Article

References (68)

1. Cragg, G.M.; Newman, D.J. Natural products: A continuing source of novel drug leads. Biochim. Biophys. Acta 2013, 1830, 3670-3695.
2. Bérdy, J. Bioactive microbial metabolites. J. Antibiot. 2005, 58, 1-26.
3. Fenical, W.; Jensen, P.R. Developing a new resource for drug discovery: Marine actinomycete bacteria. Nat. Chem. Biol. 2006, 2, 666-673.
4. Cragg, G.M.; Grothaus, P.G.; Newman, D.J. New horizons for old drugs and drug leads. J. Nat. Prod. 2014, 77, 703-723.
5. Mayer, S.; Glaser, K.B.; Cuevas, C.; Jacobs, R.S.; Kem, W.; Little, R.D.; McIntosh, J.; Newman, D.J.; Potts, B.C.; Shuster, D.E. The odyssey of marine pharmaceuticals: A current pipeline perspective. Trends Pharmacol. Sci. 2010, 31, 255-265.
6. MarinLit. Available online: http://www.chem.canterbury.ac.nz/marinlit/marinlit.shtml (accessed on 29 January 2015).
7. Gerwick, W.H.; Moore, B.S. Lessons from the past and charting the future of marine natural products drug discovery and chemical biology. Chem. Biol. 2012, 19, 85-98.
8. Koch, M.A.; Schuffenhauer, A.; Scheck, M.; Wetzel, S.; Casaulta, M.; Odermatt, A.; Ertl, P.; Waldmann, H. Charting biologically relevant chemical space: A structural classification of natural products (SCONP). Proc. Natl. Acad. Sci. USA 2005, 102, 17272-17277.
9. Ertl, P.; Roggo, S.; Schuffenhauer, A. Natural product-likeness score and its application for prioritization of compound libraries. J. Chem. Inf. Model. 2008, 46, 68-74.
10. Wetzel, S.; Bon, R.S.; Kumar, K.; Waldmann, H. Biology-oriented synthesis. Angew. Chem. Int. Ed. 2011, 50, 10800-10826.
11. Liu, B.; Zhou, J. SARS-CoV protease inhibitors design using virtual screening method from natural products libraries. J. Comput. Chem. 2005, 26, 484-490.
12. Wright, A.D.; de Nys, R.; Angerhofer, C.K.; Pezzuto, J.M.; Gurrath, M. Biological activities and 3D QSAR studies of a series of Delisea pulchra (cf. fimbriata) derived natural products. J. Nat. Prod. 2006, 69, 1180-1187.
13. Hussain, A.; Melville, J.L.; Hirst, J.D. Molecular docking and QSAR of aplyronine A and analogues: Potent inhibitors of actin. J. Comput. Aided Mol. Des. 2010, 24, 1-15.
14. Hassan, H.M.; Elnagar, A.Y.; Khanfar, M.A.; Sallam, A.A.; Mohammed, R.; Shaala, L.A.; Youssef, D.T.A.; Hifnawy, M.S.; El Sayed, K.A. Design of semisynthetic analogues and 3D-QSAR study of eunicellin-based diterpenoids as prostate cancer migration and invasion inhibitors. Eur. J. Med. Chem. 2011, 46, 1122-1130.
15. Pereira, F.; Latino, D.A.R.S.; Gaudêncio, S.P. A chemoinformatics approach to the discovery of lead-like molecules from marine and microbial sources en route to antitumor and antibiotic drugs. Mar. Drugs 2014, 12, 757-778.
16. Ma, D.-L.; Chan, D.S.-H.; Leung, C.-H. Drug repositioning by structure-based virtual screening. Chem. Soc. Rev. 2013, 42, 2130-2141.
17. AntiMarin Database; Blunt, J.W., Munro, M.H.G., Laatsch, H., Eds.; University of Canterbury: Christchurch, New Zealand; University of Göttingen: Göttingen, Germany, 2007.
18. AntiBase Home Page. Available online: http://wwwuser.gwdg.de/~hlaatsc/antibase.htm (accessed on 29 January 2015).
19. Karelson, M.; Lobanov, V.S.; Katritzky, A.R. Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev. 1996, 96, 1027-1044.
20. Benigni, R.; Giuliani, A.; Franke, R.; Gruska, A. Structure-activity relationships of mutagenic and carcinogenic aromatic amines. Chem. Rev. 2000, 100, 3697-3714.
21. Benigni, R. Structure-activity relationship studies of chemical mutagens and carcinogens: Mechanistic investigations and prediction approaches. Chem. Rev. 2005, 105, 1767-1800.
22. Benigni, R.; Bossa, C. Mechanisms of chemical carcinogenicity and mutagenicity: A review with implications for predictive toxicology. Chem. Rev. 2011, 111, 2507-2536.
23. Mendoza-Wilson, A.M.; Glossman-Mitnik, D. CHIH-DFT study of the electronic properties and chemical reactiviy of quercetin. J. Mol. Struct. Theochem. 2005, 716, 67-72.
24. Mendoza-Wilson, A.M.; Glossman-Mitnik, D. Theoretical study of the molecular properties and chemical reactivity of (+)-catechin and (-)-epicatechin related to their antioxidants ability. J. Mol. Struct. Theochem. 2005, 761, 97-106.
25. Laufer, R.S.; Dmitrienko, G.I. Diazo group electrophilicity in kinamycins and lomaiviticin A: Potential insights into the molecular mechanism of antibacterial and antitumor activity. J. Am. Chem. Soc. 2002, 124, 1854-1855.
26. Pasha, F.A.; Srivastava, H.K.; Beg, Y.; Singh, P.P. DFT based electrophilicity index and QSAR study of phenols as anti leukemia agent. Am. J. Immunol. 2006, 2, 23-28.
27. Wang, L.H.; Yang, X.Y.; Zhang, X.; Mihalic, K.; Fan, Y-X.; Xiao, W.; Howard, O.M.Z.; Appella, E.; Maynard, A.T.; Farrar, W.L. Suppression of breast cancer by chemical modulation of vulnerable zinc fingers in estrogen receptor. Nat. Med. 2004, 10, 40-47.
28. Echeverria, C.; Santibanez, J.F.; Donoso-Tauda, O.; Escobar, C.A.; Ramirez-Tagle, R. Structural antitumoral activity relationships of synthetic chalcones. Int. J. Mol. Sci. 2009, 10, 221-223.
29. Pasha, F.A.; Muddassar, M.; Lee, C.; Cho, S. Structural antitumoral activity relationships of synthetic chalcones. Environ. Toxicol. Pharmacol. 2008, 26, 128-135.
30. Soriano-Correa, C.; Raya, A.; Esquivel, R.O. Mechanism based QSAR studies of N-phenylbenzamides as antimicrobial agents. Int. J. Quant. Chem. 2008, 108, 1369-1379.
31. Pereira, F.; Latino, D.A.R.S.; Aires-de-Sousa, J. Estimation of mayr electrophilicity with a quantitative structure-property relationship approach using empirical and DFT descriptors. J. Org. Chem. 2011, 76, 9312-9319.
32. Lauria, A.; Ippolito, M.; Almerico, A.M. Combined use of PCA and QSAR/QSPR to predict the drugs mechanism of action. An application to the NCI ACAM database. QSAR Comb. Sci. 2009, 28, 387-395.
33. Larsen, S.B.; Omkvist, D.H.; Brodin, B.; Nielsen, C.U.; Steffansen, B.; Olsen, L.; Jorgensen, F.S. Discovery of ligands for the human intestinal di-tripeptide transporter (hPEPT1) using a QSAR-assisted virtual screening strategy. ChemMedChem 2009, 4, 1439-1445.
34. Fukui, K. Role of frontier orbitals in chemical reactions. Science 1982, 218, 747-754.
35. Bekhradnia, A.; Ala, S.; Bayat, B. Theoretical study on molecular orbital energy levels of h-2 blockers and correlation to their potency. Res. Pharm. Sci. 2012, 7, S615.
36. Mingeot-Leclercq, M.-P.; Glupczynski, Y.; Tulkens, P.M. Aminoglycosides: Activity and resistance. Antimicrob. Agents Chemother. 1999, 43, 727-737.
37. Umezawa, H.; Maeda, K.; Takeuchi, T.; Oakami, Y. New antibiotics bleomycin A and B. J. Antibiot. Ser. A 1996, 19, 200-209.
38. Hecht, S.M. Bleomycin: New perspectives on the mechanism of action. J. Nat. Prod. 2000, 63, 158-168.
39. Kruft, B.I.; Greer, A. Photosensitization reactions in vitro and in vivo. Photochem. Photobiol. 2011, 87, 1204-1213.
40. PubChem Bioassay Web Page. Cerulomycin - Compound Summary. Available online: http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=5381230 (accessed on 29 January 2015).
41. Mashima, T.; Sato, S.; Okabe, S.; Miyata, S.; Matsuura, M.; Sugimoto, Y.; Tsuruo, T.; Seimiya, H. Acyl-CoA synthetase as a cancer survival factor: Its inhibition enhances the efficacy of etoposide. Cancer Sci. 2009, 100, 1556-1562.
42. PubChem Bioassay Web Page. Pyrrolnitrin - Compound Summary. Available online: http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=13916 (accessed on 29 January 2015).
43. Oh, D.-C.; Strangman, W.K.; Kauffman, C.A.; Jensen, P.R.; Fenical, W. Thalassospiramides A and B, immunosuppressive peptides from the marine bacterium Thalassospira sp. Org. Lett. 2007, 9, 1525-1528.
44. Stanton, D.T.; Jurs, P.C. Development and use of charged partial surface area structural descriptors in computer assisted quantitative structure property relationship studies. Anal. Chem. 1990, 62, 2323-2329.
45. Ertl, P.; Rohde, B.; Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 2000, 43, 3714-3717.
46. Li, S.; Fedorowicz, A.; Singh, H.; Soderholm, S.C. Application of the random forest method in studies of local lymph node assay based skin sensitization data. J. Chem. Inf. Model. 2005, 45, 952-964.
47. Pearlman, R.S.; Smith, K.M. Metric validation and the receptor relevant subspace concept. J. Chem. Inf. Comput. Sci. 1999, 39, 28-35.
48. Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoinformatics, 2nd ed.; Wiley-VCH: Weinheim, Germany, 2009; Volumes 1 and 2.
49. Dutta, D.; Guha, R.; Wild, D.; Chen, T. Ensemble feature selection: Consistent descriptor subsets for multiple QSAR models. J. Chem. Inf. Model. 2007, 47, 989-997.
50. PubChem Home Page. Available online: http://pubchem.ncbi.nlm.nih.gov (accessed on 29 January 2015).
51. Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Zhou, Z.; Han, L.; Karapetyan, K.; Dracheva, S.; Shoemaker, B.A. PubChem's bioAssay database. Nucleic Acids Res. 2012, 40, D400-D412.
52. CDK Descriptor Calculator, Version 1.3.2. Available online: http://cdk.sourceforge.net/ (accessed on 29 January 2015).
53. Steinbeck, C.; Hoppe, C.; Kuhn, S.; Floris, M.; Guha, R.; Willighagen, E.L. Recent developments of the chemistry development kit (CDK) - An open-source java library for chemo- and bioinformatics. Curr. Pharm. Des. 2006, 12, 2111-2120.
54. Stewart, J.J.P. MOPAC2009. Stewart Computational Chemistry: Colorado Springs, CO, USA, 2008. Available online: http://openmopac.net (accessed on 29 January 2015).
55. Stewart, J.J.P. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J. Mol. Model. 2007, 13, 1173-1213.
56. Parr, R.G.; Szentpaly, L.V.; Liu, S. Electrophilicity index. J. Am. Chem. Soc. 1999, 121, 1922-1924.
57. Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19, 716-723.
58. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. The WEKA data mining software: An update. SIGKDD Explor. 2009, 11, 10-18.
59. Weka. Available online: http://www.cs.waikato.ac.nz/ml/weka/ (accessed on 29 January 2015).
60. Hall, M.A.; Smith, L.A. Correlation-based feature selection for machine learning. In Proceedings of the Twelfth International FLAIRS Conference, Orlando, FL, USA, 1-5 May 1999; AAAI Press: Menlo Park, CA, USA, 1999; pp. 235-239.
61. Aha, D.W.; Kibler, D.; Albert, M.K. Instance-based learning algorithms. Mach. Learn. 1991, 6, 37-66.
62. Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees; Chapman & Hall/CRC: Boca Raton, FL, USA, 2000.
63. Development Core Team, R. A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2004. Available online: http://www.r-project.org/ (accessed on 29 January 2015).
64. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
65. Cortes, C.; Vapnik, V. LIBSVM: A library for support vector machines. Mach. Learn. 1995, 20, 273-297.
66. Chang, C.-C.; Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011, 2, 1-27.
67. LIBSVM. Available online: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ (accessed on 29 January 2015).
68. Manzalawy, Y. El Honavar, V. WLSVM: Integrating LibSVM into Weka Environment 2005. Available online: http://www.cs.iastate.edu/~yasser/wlsvm/ (accessed on 29 January 2015).