Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6

Detalhes bibliográficos
Autor(a) principal: Rocha, Sheisi Fonseca Leite da Silva
Data de Publicação: 2014
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Biblioteca Digital de Teses e Dissertações da UFRRJ
Texto Completo: https://tede.ufrrj.br/jspui/handle/jspui/4163
Resumo: Urease is an important enzyme for the research in agriculture, environment and medicine. This enzyme catalyzes the hydrolysis of urea to ammonia and carbamate, which decomposes spontaneously, yielding a second molecule of ammonia, causing a significant increase of pH solution. In order to develop theoretical models for the prediction of activities of urease inhibitors, we initially studied the enzyme?s spin multiplicity, which contains two Ni(II) ?ons, and the state of protonation of the oxygen located between the nickel ions. The results indicate that the system is best represented by the triplet or quintet state and the oxygen atom located between the nickel ions, probably is a hydroxyl ion. Based on these results, the construction of the models was based on literature proposals about the use of thermodynamic cycles for the calcultation of the free energy of binding between ligands and enzymes. In the present work, parameters such as the interaction enthalpy, the Gibbs free energy required for the inhibitor to go from the aqueous phase to the interior of the enzyme and the entropic losses associated to the freezing of bonds after the binding of the inhibitors to the enzyme were used to develop correlations with the measured experimental Ki values. The quantification of these parameters for some phosphinic acids derivatives from the literature allowed us to obtain a good empirical model for the correlation between experimental activity data and the theoretical parameters (r=0.92). The model was employed for the prediction of the relative activity of a series of new proposed compounds by the organophosphorous synthesis group of UFRRJ. It was possible to identify which compounds are the most promising and which are the main factors that should be modified in order to optimize the urease inhibition profile by these compounds.
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spelling Sant'Anna, Carlos Mauricio Rabello de827232227-72http://lattes.cnpq.br/2087099684752643Bauerfeldt, Glauco FavillaMachado, S?rgio de Paula122348897-74http://lattes.cnpq.br/4206525243279971Rocha, Sheisi Fonseca Leite da Silva2020-11-19T13:29:49Z2014-04-15Rocha, Sheisi Fonseca Leite da Silva. Desenvolvimento de um modelo emp?rico de predi??o da atividade de inibidores da Urease utilizando o m?todo Semi-Emp?rico PM6. 2014. [73 f.]. Disserta??o (Programa de P?s-Gradua??o em Qu?mica) - Universidade Federal Rural do Rio de Janeiro, [Serop?dica - RJ] .https://tede.ufrrj.br/jspui/handle/jspui/4163Urease is an important enzyme for the research in agriculture, environment and medicine. This enzyme catalyzes the hydrolysis of urea to ammonia and carbamate, which decomposes spontaneously, yielding a second molecule of ammonia, causing a significant increase of pH solution. In order to develop theoretical models for the prediction of activities of urease inhibitors, we initially studied the enzyme?s spin multiplicity, which contains two Ni(II) ?ons, and the state of protonation of the oxygen located between the nickel ions. The results indicate that the system is best represented by the triplet or quintet state and the oxygen atom located between the nickel ions, probably is a hydroxyl ion. Based on these results, the construction of the models was based on literature proposals about the use of thermodynamic cycles for the calcultation of the free energy of binding between ligands and enzymes. In the present work, parameters such as the interaction enthalpy, the Gibbs free energy required for the inhibitor to go from the aqueous phase to the interior of the enzyme and the entropic losses associated to the freezing of bonds after the binding of the inhibitors to the enzyme were used to develop correlations with the measured experimental Ki values. The quantification of these parameters for some phosphinic acids derivatives from the literature allowed us to obtain a good empirical model for the correlation between experimental activity data and the theoretical parameters (r=0.92). The model was employed for the prediction of the relative activity of a series of new proposed compounds by the organophosphorous synthesis group of UFRRJ. It was possible to identify which compounds are the most promising and which are the main factors that should be modified in order to optimize the urease inhibition profile by these compounds.A urease ? uma enzima importante para as pesquisas relacionadas com a agricultura, meio ambiente e medicina. Ela catalisa a rea??o de hidr?lise da ur?ia para formar am?nia e carbamato, o qual se decomp?e espontaneamente, produzindo uma segunda mol?cula de am?nia e di?xido de carbono, provocando um significativo aumento do pH da solu??o. Com o objetivo de desenvolver modelos de predi??o da atividade de inibidores da urease, estudou-se inicialmente a multiplicidade de spin da enzima, que cont?m dois ?ons Ni(II), e o estado de protona??o do oxig?nio localizado entre estes ?ons. Os resultados indicaram que o sistema ? melhor representado pelo estado tripleto ou quinteto e o oxig?nio localizado entre os ?ons de n?quel provavelmente ? um ?on hidroxila. A partir destes resultados, a constru??o dos modelos se baseou em propostas da literatura sobre o uso de ciclos termodin?micos para se calcular a energia livre de intera??o entre ligantes e enzimas. No presente estudo, foram combinados termos referentes ? entalpia de intera??o entre o inibidor e a enzima, a energia livre de Gibbs necess?ria para o inibidor passar do meio aquoso para o interior da enzima e as perdas entr?picas devido a restri??es rotacionais ap?s a intera??o do mesmo com a enzima para se obter fun??es de correla??o com constantes inibit?rias (Ki) obtidas experimentalmente. A quantifica??o destes par?metros para alguns derivados do ?cido fosf?nico da literatura nos possibilitou o desenvolvimento de um modelo para determina??o da atividade com boa correla??o com dados experimentais (r=0,92). Este modelo foi utilizado na predi??o da atividade relativa de novas dialquilfosforilidrazonas, sintetizadas pelo grupo de s?ntese de organofosforados da UFRRJ. Foi poss?vel identificar quais compostos s?o os mais promissores da s?rie proposta e quais fatores devem ser alterados para otimizar o perfil de inibi??o da urease.Submitted by Sandra Pereira (srpereira@ufrrj.br) on 2020-11-19T13:29:49Z No. of bitstreams: 1 2014 - Sheisi Fonseca Leite da Silva Rocha.pdf: 988981 bytes, checksum: 49abe58d9422ae29356979cf6629dec8 (MD5)Made available in DSpace on 2020-11-19T13:29:49Z (GMT). No. of bitstreams: 1 2014 - Sheisi Fonseca Leite da Silva Rocha.pdf: 988981 bytes, checksum: 49abe58d9422ae29356979cf6629dec8 (MD5) Previous issue date: 2014-04-15Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior, CAPES, Brasil.application/pdfhttps://tede.ufrrj.br/retrieve/63070/2014%20-%20Sheisi%20Fonseca%20Leite%20da%20Silva%20Rocha.pdf.jpgporUniversidade Federal Rural do Rio de JaneiroPrograma de P?s-Gradua??o em Qu?micaUFRRJBrasilInstituto de Ci?ncias ExatasABRAHAM, D. J. Burger?s: medicinal chemistry and drug discovery. Virg?nia: Wiley Interscience, p.1125. 2003. ALC?CER, L. Introdu??o ? Qu?mica qu?ntica computacional. Rio de Janeiro. Editora: IST Press, Lisboa, 2007. 305p. (Ensino da Ci?ncia e da Tecnologia, 20) ALLINGER, N.L. Conformational analysis. 130. MM2. A hydrocarbon force field utilizing V1 and V2 torsional terms. J. Am. Chem. Soc., v.99, p.8127-8134, 1977. ALONSO, H. et al. Combining docking and molecular dynamic simulations in drug design. Med. Res. Rev., v. 26, p. 531- ASI, A. M. et al. Application o binding free energy values of Escherichia coli wild terminal domain (ArgRc)? - Mol. Graphics Model., v.22, n.4, p.249 BACCHI, A. et al. Antimicrobial and mutagenic properties of organotin(IV) complexes with isatin and N-alkylisatin bisthiocarbonohydrazones. J. Inorg. Biochem. v.99, p.397. BARREIRO, E. J. & FRAGA, C. A. M. Qu?mica Porto Alegre, 2002. BENINI, S. et al. A new proposal for urease mechanism based on the crystal structures of the native and inhibited enzyme from Struct. Fold. Des. v.7, p.205. 1999. BENINI, S. et al. The complex of Bacillus pasteurii urease with acetohydroxamate anion from X-ray data at 1.55 A resolution. BENINI, S. et al. Molecular Details of Urease Inhibition by Boric Acid: Catalytic Mechanism. J. Am. Ch B?HM, H.J.. LUDI: Rule-based automatic design of new substituents for enzyme inhibitor leads. Journal of Computer-Aided Molecular Design BORN, M. & OPPENHEIMER, J. R p.457-484, 1927. BONET, B. et al. A robust and fast selection mechanism for planning. 14th National Conference on Artificial Intelligence (American Intelligence), p. 714-719, 1997. BRAVO, I.G et al. Kinetic properties of the Acylneuraminate Cytidylytransferase from Pasteurella haemolytica A. Biochem. J., v.358, p.585 BREMNER, J. M. Recent Research on Problems in the Use of Urea as a Nitrog Fert. Res. v.42, p.321-329. 1995. BROOKS, C. L. et al. A theoretical perspective of dynamics, structure, and thermodynamics. Advances in chemical physics; John Wiley: New York, v.LXXI. 1988. -568, 2006. of the linear interaction energy method (LIE) to estimate the wild-type and mutant arginine repressor C arginine and ArgRc? -citrulline protein?ligand complexes ., 249-262, 2004. . Medicinal. Porto Alegre. Editora: Artmed, Bacillus pasteurii: why urea hydrolysis costs two nickels. old. J. Biol. Inorg. Chem. v.1, n.5, p.110, 2000. . Insights into the Chem. Soc., v.12, n.126, p.3714?3715, 2004. Design, v.8, p. 593-606. 1992 ER, R. Zur Quantentheorie der Molekeln. Ann. Phys., v.84, Proceedings of the Association for Artificial 585-598. 2001. 66 f Cligand complexes. J. 2005. : . Nitrogen Fertilizer. 67 BROOKS, B.R. et. al. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem., v.4, p.187-217, 1983. BROOIJMANS, N. & KUNTZ, I.D.. Molecular recognition and docking algorithms. Annual Reviews of Biophysical and Biomolecular Structures, v.32, p.335-373. 2003. CLARK, T. A handbook of computational chemistry: a practical guide to chemical structure and energy calculations. New York: Wiley. p.352. 1985. CARLSSON, H. & NORDLANDER, E. Computacional Modeling of the Mechanism of Urease. Bioinorganic Chem. And Applic. 2010. CARMONA, G. et al. Temperature and low concentration effects of the urease inhibitor N- (n-butyl) thiophosphoric triamide (NBPT) on ammonia volatilisation from urea. Soil Biol. Biochem. v.22, p.933?937. 1990. COWAN, J. A. Inorganic Biochemistry ? An Introducion. 2a Ed. New York: Ed. Wiley-VCH, 1993. DA COSTA, J. B. N. et al. Compostos Organofosforados Pentavalentes: Hist?rico, M?todos Sint?ticos de Prepara??o e Aplica??es como Inseticida e Agente Atitumorais. Qu?mica Nova, v.30, p.159. 2007. DA COSTA, P. A. & POPPI, R. J. Genetic algorithm in chemistry. Quimica Nova, v.22, p.405- 411. 1999. DEWAR, M. J. S. et al. Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc., v.107, p.3902-3909, 1985. DEWAR, M. J. S. & THIEL, W. Ground states of molecules. 38. The MNDO method. Approximations and parameters. J. Am. Chem. Soc., v.99, p.4899-4907, 1977. DIXON, N. E. et al. Jack Bean Urease (EC 3.5..1.5) a Metalloenzyme. A simple Biological Role for Nickel? J. Am. Chem. Soc. 97: 4131-133, 1975. DOM?NGUEZ, M. J. et al. Design, Synthesis, and Biological Evaluation of Phosphoramide Derivatives as Urease Inhibitors J. Agric. Food Chem. v.56, p.3721?3731. 2008. ELDRIDGE, M.D., et al.Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of Computer-Aided Molecular Design, v.11, p. 425-445, 1997. 68 ESTIU, G. & MERZ JR, K. M. Competitive Hydrolytic and Elimination Mechanisms in the Urease Catalyzed Decompos?tion of Urea. J. Phys. Chem. v.111, p.10263-10274. 2007. FENTON, D. E. Biocoordination Chemistry. New York: Oxford Chemistry Primers, 1995. FERREIRA, S. B. et al. B lapachona: sua import?ncia em qu?mica medicinal e suas modifica??es estruturais. Rev. Virtual de Qu?mica, v.2, p.140-160. 2010. FONG, Y. et al. Assembly of Preactivation Complex for Urease Maturation in Helicobacter pylori: Crystal Structure of UreF-UreH Protein Complex. Journal of Biological Chemistry, v.50, p.286. 2011. GILL, J.S. et al. Efficiency of N-(n-butyl) thiophosphoric triamide in retarding hydrolysis of urea and ammonia volatilization losses in a flooded sandy loam soil amended with organic materials. Nutr. Cycl. Agroecosyst. v.53,p.203?207. 1997. GOHLKE, H., HENDLICH, M. & KLEBE, G.. Knowledge-based scpring function to predict protein-ligand interactions. Journal of Molecular Biology, v.295, p.337-356. 2000. HAQ, Z. & WADOOD, A. Prediction of Binding Affinities for Hydroxamic Acid Derivatives as Urease Inhibitors by Molecular Docking and 3D-QSAR Studies. Letters in Drug Design and Discovery, v.6, p. 93-100. 2009. HANSSON T. et al. Ligand binding affinity prediction by linear interaction energy methods. J. Comput.-Aided Mol. Des., v.12, n.1, p. 27-35, 1998. HAYAKAWA, K. et al. Determina??o de atividades espec?ficas e constantes cin?ticas de biotinidase e lipoamidase em LEW rato e Lactobacillus casei (Shirota). J Chrom Analyt Tec. Biomed., v.2, p.240-50. 2006. HARTREE, D. R.; Proc. Cambridge. Phil. Soc., v.21, p.625, 1923. HARTREE, D. R.; Proc. Cambridge. Phil. Soc., v.22, p.464, 1924. H?LTJE, H. D. & FOLKERS, G. Molecular modeling: basic principles and applications. Weinheim: VCH. p.194. 1996. HOUSE, J. E. Fundamentals of Quantum Chemistry, Elsevier, San Diego, 2004. HYPERCHEM e (TM) Computational chemistry manual, 1994, (@c) hypercube inc. JABRI, E. et al. The crystal structure of urease from Klebsiella aerogenes. Science. v.268, p. 998-1004. 1995. JENSEN, F. Introduction to computational chemistry. Chichester: John Wiley & Sons, p.429. 1999. 69 JORGENSEN, W. L. & TIRADO-RIVES, J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc., v.110, p.1657-1666, 1988. KARPLUS, P. A. et al. 70 Years of Crystalline Urease: What Have We Learned? Acc. Chem. Res, v.30, p.330. 1997. KITCHEN, D.B., et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews in Drug Discovery, v.3, p.935-949. 2004, KLEBE, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discovery Today, v. 11, p.580-594, 2006. KOLLMAN P. A. Free energy calculations: applications to chemical and biochemical phenomena. Chem. Rev., v.93, p.2395-23417, 1993. KORB, O., et al. An ant colony optimization approach to flexible protein-ligand docking. Swarm Intelligence, v.1, 2, p.115-134, 2007. KORB, O., et al. Empirical scoring function for advanced protein ligand docking with PLANTS. Journal of Chemical Information and Modeling, v.49, n.1,p.84-96, 2009. KUNTZ, I.D. et al. Geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, v.161, p.269-288. 1982. LINEWEAVER, H & BURK, D. Determina??o de Constantes de inibi??o de enzimas. J. A. Chem, v.3, p.53. 1934. LEACH, A. R. Molecular Modelling - Principles and Applications. England: Person Prentice Hall, p.744. 2001. Li, L. et al. On the Dielectric ?Constant? of Proteins: Smooth Dielectric Function for Macromolecular Modeling and Its Implementation in DelPhi, J. Chem. Theory Comput., v.9, 2126?2136. 2013 LINDEN, R. Algoritmos Gen?ticos: Uma Importante Ferramenta da Intelig?ncia Computacional. Rio de Janeiro: Editora Brasport, p.428. 2006. MCCARTY G. W. & BREMNER J. M., Laboratory evaluation of dicyandiamide as a soil nitrification inhibitor. Commun. Soil Sci. Plant Anal. V.20, p.2049?2065. 1989. MARSHALL, B. J. & WARREN, J. R. Unidentified Curved Bacilli in the Stomach of Patients with Gastritis and Peptic Ulceration. Lancet, p. 1311-1315. 1984. MITCHELL, M. An Introduction a Genetic Algorithm. London: MIT Press, 1998. 70 MONKS, T. J. et al. Quinone, Chemistry and Toxicology. Appl. Pharmacol. v.2, p.112. 1992. MOYO, C.C. et al. Temperature Effects on Soil Urease Activity. Soil Biol. and Biochem. v.21, n.7, p.935-938. 1989. MORGON, N. H. & COUTINHO, K. M?todos de Qu?mica Te?rica e Modelagem Molecular. S?o Paulo: Livraria da F?sica, p.539. 2007. MORRIS, G.M., et al. Automated docking using a Lamarckian Genetic Algorithm and an empirical binding free energy function. Journal of Computational Chemistry, v.19, p.1639- 1662. 1998. MUEGGE, I. & MARTIN, Y.C.. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. Journal of Medicinal Chemistry, v.42, p.791- 804. 1999. MUSIANI, F. et al. Structure-based computational study of the catalytic and inhibition mechanisms of urease. J. Biol. Inorg. Chem. v.3, p.300?314, 2001. OLIVEIRA, F. G. et al. Molecular docking study and development of na empirical binding free energy model for phosphodiesterase 4 inhibitors. Bioorg. Med. Chem., v.14, p.6001- 6011, 2006. OVERREIN, L.N. & MOE, P.G. Factors affecting urea hydrolysis and ammonia volatilization in soil. Soil Science Society of America Proceedings, v.31, p. 57-61. 1967. OPREA, T. I. Chemoinformatics in drug discovery. Weinheim: Wiley-VCH, p.493. 2005. PEARSON, M. A. et al. Biochemistry, v.36, p.8164-8172. 1997. PEARSON, M. A. et al. Structures of Cys319 Variants and Acetohydroxamate-Inhibited Klebsiella aerogenes Urease. Biochemistry, , V.36, n.26, p.8164?8172,1997. POPLE, J. A. & NESBET, R. K. Self-consistent orbitals for radicals. J. Chem. Phys., v.22, n.3, p.571-572, 1954. POPLE, J. A. BEVERIDGE, D. L. DOBOSH, P. A. Approximate Self-consistent Molecular Orbital Theory V. Intermediate Neglect of Differential Overlap,. J. Chem. Phys., v.6, n.47, p.2026, 1967. VASSILIOU, S. et al. Computer-Aided Optimization of Phosphinic Inhibitors of Bacterial Ureases. J. Med. Chem., v. 53 (15), p.5597?5606. 2010. 71 VASSILIOU, S. et al. Design, Synthesis and Evaluation of Novel Organophosphorus Inhibitors of Bacterial Ureases. J. Med. Chem. v.51, p.5736-5744. 2008. RAIJ, B. V. et al. An?lise qu?mica para avalia??o da fertilidade de solos tropicais. Campinas, Instituto Agron?mico, p.285. 2001. REEVES, C. R. Modern Heuristic Techniques for Combinatorial Problems. London: McGraw-Hill, 1995. ROCHA JR., J. G. Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Esterol 14 -Desmetilase (CYP51) utilizando o M?todo Semi-Emp?rico PM6. Disserta??o. 2009. ROSENZWEIG, A. C. & DOOLEY, D. M. Bioinorganic chemistry: Editorial overview. Current Opinion in Chemical Biology, v. 10, p.89-90, 2006. ROOTHAAN, C.C.J. New developments in molecular orbital theory. Rev. Mod. Phys., v.23, n.2, p.69-89, 1951. ROOTHAAN, C. C. J. Self consistent field functions for the atomics configurations 1S2, 1S22S, e 1S22S2S. Rev. Mod. Phys., v.32, v.2, p.179-185, 1960. SANT?ANNA, C. M. R. Gloss?rio de Termos Usados no Planejamento de f?rmacos (Recomenda??es da IUPAC para 1997). Qu?m. Nov. v.25, n.3, p.505. 2002. SANT?ANNA, C. M. R. M?todos de Modelagem Molecular para Estudo e Planejamento de compostos bioativos: Uma Introdu??o. Ver. Virtual Quim. v.1, n.1, p.49-57. 2009. SCHNEIDER, G. & B?HM, H-J. Virtual screening and fast automated docking methods. Drug Discovery Today, v.7, p.64-70. 2002. SERGEEVA, M. V. & CATHERS, B. E., Biochem. Pharmacol. v.65, p.823. 2003. SILVA, C. M. & BISCAIA, E. C. Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors. Comp. Chem. Eng. v.27, p.1329- 1344. 2003. S?tio http://www.dockthor.lncc.br, acessado em novembro de 2013. S?tio http://www.graphpad.com, acessado em janeiro de 2014. S?tio http://openmopac.net/index.html, acessado em outubro de 2013. 72 S?tio http://openbabel.org, acessado em 2012. SLATER, J. C. The theory of complex spectra. Phys. Rev., v.34, p.1293-1322, 1929. SMYJ, R. P. A Conformational Analysis Study of a Nickel(II) Enzyme: Urease. J. Mol. Struct. v.391, n.3, p. 207. 1997. SMOOT, D.T. et al. Helicobacter Pylori Urease Activity is Toxic to Human Gastric Epithelial Cells. Infect. Immun. v.58, p1992-1994, 1990. STEWART, J. J. P. Optimization of parameters for semiempirical methods I. Method. J. Comput. Chem., v.10, n.2, p.209-220, 1989. STEWART, J. J. P. Application of localized molecular orbitals to the solution of semiempirical self-consistent field equations, Int. J. Quantum Chem. 58, 133-146, 1996. STEWART, J. J. P. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J. Mol. Model., v.13, p.1173-1213, 2007. STEWART, J. J. P. Optimization of parameters for semiempirical methods VI: more modifications to NDDO aproximations and re-otimization of parameters. J. Mol. Model., v. 19, p.1-32. 2013. SUAREZ, D. et al. Ureases: Quantum Chemical Calculations on Cluster Models. J. Am. Chem. Soc. v.125, p.15325. 2003. SUMNER, J.B. The Isolation and Crystallization of the Enzyme Urease. J. Biol. Chem. 69:435-441. 1926 TERAMOTO, R. & FUKUNISHI, H. Supervised consensus scoring for docking and virtual screening. Journal of Chemical Information and Modeling, v. 47, p. 526-534, 2007. THIEL, W. & VOITYUK, A. A. Extension of the MNDO formalism to d-orbitals - Integral approximations and preliminary numerical results. Theor. Chim. Acta, v.81, p.391-404, 1992a. THIEL, W. & VOITYUK, A. A. Extension of MNDO to d-orbitals - Parameters and results for the halogens. Int. J. Quantum Chem., v. 44, p.807-829, 1992b. 73 VERDONK, M. et al. Improved protein-ligand docking using GOLD. Proteins-Structure Function and Genetics, v. 52, p. 609-623, 2003. VERDONK, M.L., et al. Improved protein-ligand docking using GOLD. Proteins, v.52, p. 609- 623. 2003. VOTANO, J. R. et al. Prediction of Aqueous Solubility Based on Large Datasets Using Several QSPR Models Utilizing Topological Structure Representation. J. Chem Info. Comput. Sci., v.1, n.11, p.1829-1841, 2004. WANG, S. et al. Protein Kinase C. Modeling of the Binding Site and Prediction of Binding Constants. J. Med. Chem., v.37, p.1326-1338, 1994. WANG, R. X. et al. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. v. 46, p. 2287-2303, 2003. WEINER, S.J. et al. A new force field for molecular mechanical simulation of nucleic acids and proteins. J. Am. Chem. Soc., v.106, p.765-784, 1984. WOLFENDEN, R. & SNIDER, M. J. The Depth of Chemical Time and the Power of Enzymes as Catalysts. Acc. Chem. Res, v.34, p.938, 2001. YURIEV, E. & RAMSLAND, A. P. Latest developments in molecular docking: 2010-2011 in review. J. Mol. Recognit, v. 26, p.215?239. 2013. ZAMAN, M. et al. Reducing NH3, N2O and NO3 ? - N losses from a pasture soil with urease or nitrification inhibitors and elemental S-amended nitrogenous fertilizers, Biol. Fertil. Soils. v.44, p.693?705. 2008. ZAMBELLI, B. et al. UreG, a chaperone in the urease assembly process, is an intrinsically unstructured GTPase that specifically binds Zn2+. J Biol Chem. v.280, p.4684. 2005. ZIMMER, M. Are Classical Molecular Machanics Calculations Still Useful in Bioinorganic Simulations. Coord. Chem. Ver. 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dc.title.por.fl_str_mv Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
title Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
spellingShingle Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
Rocha, Sheisi Fonseca Leite da Silva
Organofosforados
Modelo de energia livre
M?todo semi-emp?rico
Urease
Organophosphorus compounds
Free energy models
Semi-empirical method
Qu?mica
title_short Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
title_full Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
title_fullStr Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
title_full_unstemmed Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
title_sort Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Urease utilizando o M?todo Semi-Emp?rico PM6
author Rocha, Sheisi Fonseca Leite da Silva
author_facet Rocha, Sheisi Fonseca Leite da Silva
author_role author
dc.contributor.advisor1.fl_str_mv Sant'Anna, Carlos Mauricio Rabello de
dc.contributor.advisor1ID.fl_str_mv 827232227-72
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/2087099684752643
dc.contributor.referee1.fl_str_mv Bauerfeldt, Glauco Favilla
dc.contributor.referee2.fl_str_mv Machado, S?rgio de Paula
dc.contributor.authorID.fl_str_mv 122348897-74
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/4206525243279971
dc.contributor.author.fl_str_mv Rocha, Sheisi Fonseca Leite da Silva
contributor_str_mv Sant'Anna, Carlos Mauricio Rabello de
Bauerfeldt, Glauco Favilla
Machado, S?rgio de Paula
dc.subject.por.fl_str_mv Organofosforados
Modelo de energia livre
M?todo semi-emp?rico
topic Organofosforados
Modelo de energia livre
M?todo semi-emp?rico
Urease
Organophosphorus compounds
Free energy models
Semi-empirical method
Qu?mica
dc.subject.eng.fl_str_mv Urease
Organophosphorus compounds
Free energy models
Semi-empirical method
dc.subject.cnpq.fl_str_mv Qu?mica
description Urease is an important enzyme for the research in agriculture, environment and medicine. This enzyme catalyzes the hydrolysis of urea to ammonia and carbamate, which decomposes spontaneously, yielding a second molecule of ammonia, causing a significant increase of pH solution. In order to develop theoretical models for the prediction of activities of urease inhibitors, we initially studied the enzyme?s spin multiplicity, which contains two Ni(II) ?ons, and the state of protonation of the oxygen located between the nickel ions. The results indicate that the system is best represented by the triplet or quintet state and the oxygen atom located between the nickel ions, probably is a hydroxyl ion. Based on these results, the construction of the models was based on literature proposals about the use of thermodynamic cycles for the calcultation of the free energy of binding between ligands and enzymes. In the present work, parameters such as the interaction enthalpy, the Gibbs free energy required for the inhibitor to go from the aqueous phase to the interior of the enzyme and the entropic losses associated to the freezing of bonds after the binding of the inhibitors to the enzyme were used to develop correlations with the measured experimental Ki values. The quantification of these parameters for some phosphinic acids derivatives from the literature allowed us to obtain a good empirical model for the correlation between experimental activity data and the theoretical parameters (r=0.92). The model was employed for the prediction of the relative activity of a series of new proposed compounds by the organophosphorous synthesis group of UFRRJ. It was possible to identify which compounds are the most promising and which are the main factors that should be modified in order to optimize the urease inhibition profile by these compounds.
publishDate 2014
dc.date.issued.fl_str_mv 2014-04-15
dc.date.accessioned.fl_str_mv 2020-11-19T13:29:49Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv Rocha, Sheisi Fonseca Leite da Silva. Desenvolvimento de um modelo emp?rico de predi??o da atividade de inibidores da Urease utilizando o m?todo Semi-Emp?rico PM6. 2014. [73 f.]. Disserta??o (Programa de P?s-Gradua??o em Qu?mica) - Universidade Federal Rural do Rio de Janeiro, [Serop?dica - RJ] .
dc.identifier.uri.fl_str_mv https://tede.ufrrj.br/jspui/handle/jspui/4163
identifier_str_mv Rocha, Sheisi Fonseca Leite da Silva. Desenvolvimento de um modelo emp?rico de predi??o da atividade de inibidores da Urease utilizando o m?todo Semi-Emp?rico PM6. 2014. [73 f.]. Disserta??o (Programa de P?s-Gradua??o em Qu?mica) - Universidade Federal Rural do Rio de Janeiro, [Serop?dica - RJ] .
url https://tede.ufrrj.br/jspui/handle/jspui/4163
dc.language.iso.fl_str_mv por
language por
dc.relation.references.por.fl_str_mv ABRAHAM, D. J. Burger?s: medicinal chemistry and drug discovery. Virg?nia: Wiley Interscience, p.1125. 2003. ALC?CER, L. Introdu??o ? Qu?mica qu?ntica computacional. Rio de Janeiro. Editora: IST Press, Lisboa, 2007. 305p. (Ensino da Ci?ncia e da Tecnologia, 20) ALLINGER, N.L. Conformational analysis. 130. MM2. A hydrocarbon force field utilizing V1 and V2 torsional terms. J. Am. Chem. Soc., v.99, p.8127-8134, 1977. ALONSO, H. et al. Combining docking and molecular dynamic simulations in drug design. Med. Res. Rev., v. 26, p. 531- ASI, A. M. et al. Application o binding free energy values of Escherichia coli wild terminal domain (ArgRc)? - Mol. Graphics Model., v.22, n.4, p.249 BACCHI, A. et al. Antimicrobial and mutagenic properties of organotin(IV) complexes with isatin and N-alkylisatin bisthiocarbonohydrazones. J. Inorg. Biochem. v.99, p.397. BARREIRO, E. J. & FRAGA, C. A. M. Qu?mica Porto Alegre, 2002. BENINI, S. et al. A new proposal for urease mechanism based on the crystal structures of the native and inhibited enzyme from Struct. Fold. Des. v.7, p.205. 1999. BENINI, S. et al. The complex of Bacillus pasteurii urease with acetohydroxamate anion from X-ray data at 1.55 A resolution. BENINI, S. et al. Molecular Details of Urease Inhibition by Boric Acid: Catalytic Mechanism. J. Am. Ch B?HM, H.J.. LUDI: Rule-based automatic design of new substituents for enzyme inhibitor leads. Journal of Computer-Aided Molecular Design BORN, M. & OPPENHEIMER, J. R p.457-484, 1927. BONET, B. et al. A robust and fast selection mechanism for planning. 14th National Conference on Artificial Intelligence (American Intelligence), p. 714-719, 1997. BRAVO, I.G et al. Kinetic properties of the Acylneuraminate Cytidylytransferase from Pasteurella haemolytica A. Biochem. J., v.358, p.585 BREMNER, J. M. Recent Research on Problems in the Use of Urea as a Nitrog Fert. Res. v.42, p.321-329. 1995. BROOKS, C. L. et al. A theoretical perspective of dynamics, structure, and thermodynamics. Advances in chemical physics; John Wiley: New York, v.LXXI. 1988. -568, 2006. of the linear interaction energy method (LIE) to estimate the wild-type and mutant arginine repressor C arginine and ArgRc? -citrulline protein?ligand complexes ., 249-262, 2004. . Medicinal. Porto Alegre. Editora: Artmed, Bacillus pasteurii: why urea hydrolysis costs two nickels. old. J. Biol. Inorg. Chem. v.1, n.5, p.110, 2000. . Insights into the Chem. Soc., v.12, n.126, p.3714?3715, 2004. Design, v.8, p. 593-606. 1992 ER, R. Zur Quantentheorie der Molekeln. Ann. Phys., v.84, Proceedings of the Association for Artificial 585-598. 2001. 66 f Cligand complexes. J. 2005. : . Nitrogen Fertilizer. 67 BROOKS, B.R. et. al. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem., v.4, p.187-217, 1983. BROOIJMANS, N. & KUNTZ, I.D.. Molecular recognition and docking algorithms. Annual Reviews of Biophysical and Biomolecular Structures, v.32, p.335-373. 2003. CLARK, T. A handbook of computational chemistry: a practical guide to chemical structure and energy calculations. New York: Wiley. p.352. 1985. CARLSSON, H. & NORDLANDER, E. Computacional Modeling of the Mechanism of Urease. Bioinorganic Chem. And Applic. 2010. CARMONA, G. et al. Temperature and low concentration effects of the urease inhibitor N- (n-butyl) thiophosphoric triamide (NBPT) on ammonia volatilisation from urea. Soil Biol. Biochem. v.22, p.933?937. 1990. COWAN, J. A. Inorganic Biochemistry ? An Introducion. 2a Ed. New York: Ed. Wiley-VCH, 1993. DA COSTA, J. B. N. et al. Compostos Organofosforados Pentavalentes: Hist?rico, M?todos Sint?ticos de Prepara??o e Aplica??es como Inseticida e Agente Atitumorais. Qu?mica Nova, v.30, p.159. 2007. DA COSTA, P. A. & POPPI, R. J. Genetic algorithm in chemistry. Quimica Nova, v.22, p.405- 411. 1999. DEWAR, M. J. S. et al. Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc., v.107, p.3902-3909, 1985. DEWAR, M. J. S. & THIEL, W. Ground states of molecules. 38. The MNDO method. Approximations and parameters. J. Am. Chem. Soc., v.99, p.4899-4907, 1977. DIXON, N. E. et al. Jack Bean Urease (EC 3.5..1.5) a Metalloenzyme. A simple Biological Role for Nickel? J. Am. Chem. Soc. 97: 4131-133, 1975. DOM?NGUEZ, M. J. et al. Design, Synthesis, and Biological Evaluation of Phosphoramide Derivatives as Urease Inhibitors J. Agric. Food Chem. v.56, p.3721?3731. 2008. ELDRIDGE, M.D., et al.Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of Computer-Aided Molecular Design, v.11, p. 425-445, 1997. 68 ESTIU, G. & MERZ JR, K. M. Competitive Hydrolytic and Elimination Mechanisms in the Urease Catalyzed Decompos?tion of Urea. J. Phys. Chem. v.111, p.10263-10274. 2007. FENTON, D. E. Biocoordination Chemistry. New York: Oxford Chemistry Primers, 1995. FERREIRA, S. B. et al. B lapachona: sua import?ncia em qu?mica medicinal e suas modifica??es estruturais. Rev. Virtual de Qu?mica, v.2, p.140-160. 2010. FONG, Y. et al. Assembly of Preactivation Complex for Urease Maturation in Helicobacter pylori: Crystal Structure of UreF-UreH Protein Complex. Journal of Biological Chemistry, v.50, p.286. 2011. GILL, J.S. et al. Efficiency of N-(n-butyl) thiophosphoric triamide in retarding hydrolysis of urea and ammonia volatilization losses in a flooded sandy loam soil amended with organic materials. Nutr. Cycl. Agroecosyst. v.53,p.203?207. 1997. GOHLKE, H., HENDLICH, M. & KLEBE, G.. Knowledge-based scpring function to predict protein-ligand interactions. Journal of Molecular Biology, v.295, p.337-356. 2000. HAQ, Z. & WADOOD, A. Prediction of Binding Affinities for Hydroxamic Acid Derivatives as Urease Inhibitors by Molecular Docking and 3D-QSAR Studies. Letters in Drug Design and Discovery, v.6, p. 93-100. 2009. HANSSON T. et al. Ligand binding affinity prediction by linear interaction energy methods. J. Comput.-Aided Mol. Des., v.12, n.1, p. 27-35, 1998. HAYAKAWA, K. et al. Determina??o de atividades espec?ficas e constantes cin?ticas de biotinidase e lipoamidase em LEW rato e Lactobacillus casei (Shirota). J Chrom Analyt Tec. Biomed., v.2, p.240-50. 2006. HARTREE, D. R.; Proc. Cambridge. Phil. Soc., v.21, p.625, 1923. HARTREE, D. R.; Proc. Cambridge. Phil. Soc., v.22, p.464, 1924. H?LTJE, H. D. & FOLKERS, G. Molecular modeling: basic principles and applications. Weinheim: VCH. p.194. 1996. HOUSE, J. E. Fundamentals of Quantum Chemistry, Elsevier, San Diego, 2004. HYPERCHEM e (TM) Computational chemistry manual, 1994, (@c) hypercube inc. JABRI, E. et al. The crystal structure of urease from Klebsiella aerogenes. Science. v.268, p. 998-1004. 1995. JENSEN, F. Introduction to computational chemistry. Chichester: John Wiley & Sons, p.429. 1999. 69 JORGENSEN, W. L. & TIRADO-RIVES, J. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc., v.110, p.1657-1666, 1988. KARPLUS, P. A. et al. 70 Years of Crystalline Urease: What Have We Learned? Acc. Chem. Res, v.30, p.330. 1997. KITCHEN, D.B., et al. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews in Drug Discovery, v.3, p.935-949. 2004, KLEBE, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discovery Today, v. 11, p.580-594, 2006. KOLLMAN P. A. Free energy calculations: applications to chemical and biochemical phenomena. Chem. Rev., v.93, p.2395-23417, 1993. KORB, O., et al. An ant colony optimization approach to flexible protein-ligand docking. Swarm Intelligence, v.1, 2, p.115-134, 2007. KORB, O., et al. Empirical scoring function for advanced protein ligand docking with PLANTS. Journal of Chemical Information and Modeling, v.49, n.1,p.84-96, 2009. KUNTZ, I.D. et al. Geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, v.161, p.269-288. 1982. LINEWEAVER, H & BURK, D. Determina??o de Constantes de inibi??o de enzimas. J. A. Chem, v.3, p.53. 1934. LEACH, A. R. Molecular Modelling - Principles and Applications. England: Person Prentice Hall, p.744. 2001. Li, L. et al. On the Dielectric ?Constant? of Proteins: Smooth Dielectric Function for Macromolecular Modeling and Its Implementation in DelPhi, J. Chem. Theory Comput., v.9, 2126?2136. 2013 LINDEN, R. Algoritmos Gen?ticos: Uma Importante Ferramenta da Intelig?ncia Computacional. Rio de Janeiro: Editora Brasport, p.428. 2006. MCCARTY G. W. & BREMNER J. M., Laboratory evaluation of dicyandiamide as a soil nitrification inhibitor. Commun. Soil Sci. Plant Anal. V.20, p.2049?2065. 1989. MARSHALL, B. J. & WARREN, J. R. Unidentified Curved Bacilli in the Stomach of Patients with Gastritis and Peptic Ulceration. Lancet, p. 1311-1315. 1984. MITCHELL, M. An Introduction a Genetic Algorithm. London: MIT Press, 1998. 70 MONKS, T. J. et al. Quinone, Chemistry and Toxicology. Appl. Pharmacol. v.2, p.112. 1992. MOYO, C.C. et al. Temperature Effects on Soil Urease Activity. Soil Biol. and Biochem. v.21, n.7, p.935-938. 1989. MORGON, N. H. & COUTINHO, K. M?todos de Qu?mica Te?rica e Modelagem Molecular. S?o Paulo: Livraria da F?sica, p.539. 2007. MORRIS, G.M., et al. Automated docking using a Lamarckian Genetic Algorithm and an empirical binding free energy function. Journal of Computational Chemistry, v.19, p.1639- 1662. 1998. MUEGGE, I. & MARTIN, Y.C.. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. Journal of Medicinal Chemistry, v.42, p.791- 804. 1999. MUSIANI, F. et al. Structure-based computational study of the catalytic and inhibition mechanisms of urease. J. Biol. Inorg. Chem. v.3, p.300?314, 2001. OLIVEIRA, F. G. et al. Molecular docking study and development of na empirical binding free energy model for phosphodiesterase 4 inhibitors. Bioorg. Med. Chem., v.14, p.6001- 6011, 2006. OVERREIN, L.N. & MOE, P.G. Factors affecting urea hydrolysis and ammonia volatilization in soil. Soil Science Society of America Proceedings, v.31, p. 57-61. 1967. OPREA, T. I. Chemoinformatics in drug discovery. Weinheim: Wiley-VCH, p.493. 2005. PEARSON, M. A. et al. Biochemistry, v.36, p.8164-8172. 1997. PEARSON, M. A. et al. Structures of Cys319 Variants and Acetohydroxamate-Inhibited Klebsiella aerogenes Urease. Biochemistry, , V.36, n.26, p.8164?8172,1997. POPLE, J. A. & NESBET, R. K. Self-consistent orbitals for radicals. J. Chem. Phys., v.22, n.3, p.571-572, 1954. POPLE, J. A. BEVERIDGE, D. L. DOBOSH, P. A. Approximate Self-consistent Molecular Orbital Theory V. Intermediate Neglect of Differential Overlap,. J. Chem. Phys., v.6, n.47, p.2026, 1967. VASSILIOU, S. et al. Computer-Aided Optimization of Phosphinic Inhibitors of Bacterial Ureases. J. Med. Chem., v. 53 (15), p.5597?5606. 2010. 71 VASSILIOU, S. et al. Design, Synthesis and Evaluation of Novel Organophosphorus Inhibitors of Bacterial Ureases. J. Med. Chem. v.51, p.5736-5744. 2008. RAIJ, B. V. et al. An?lise qu?mica para avalia??o da fertilidade de solos tropicais. Campinas, Instituto Agron?mico, p.285. 2001. REEVES, C. R. Modern Heuristic Techniques for Combinatorial Problems. London: McGraw-Hill, 1995. ROCHA JR., J. G. Desenvolvimento de um Modelo Emp?rico de Predi??o da Atividade de Inibidores da Esterol 14 -Desmetilase (CYP51) utilizando o M?todo Semi-Emp?rico PM6. Disserta??o. 2009. ROSENZWEIG, A. C. & DOOLEY, D. M. Bioinorganic chemistry: Editorial overview. Current Opinion in Chemical Biology, v. 10, p.89-90, 2006. ROOTHAAN, C.C.J. New developments in molecular orbital theory. Rev. Mod. Phys., v.23, n.2, p.69-89, 1951. ROOTHAAN, C. C. J. Self consistent field functions for the atomics configurations 1S2, 1S22S, e 1S22S2S. Rev. Mod. Phys., v.32, v.2, p.179-185, 1960. SANT?ANNA, C. M. R. Gloss?rio de Termos Usados no Planejamento de f?rmacos (Recomenda??es da IUPAC para 1997). Qu?m. Nov. v.25, n.3, p.505. 2002. SANT?ANNA, C. M. R. M?todos de Modelagem Molecular para Estudo e Planejamento de compostos bioativos: Uma Introdu??o. Ver. Virtual Quim. v.1, n.1, p.49-57. 2009. SCHNEIDER, G. & B?HM, H-J. Virtual screening and fast automated docking methods. Drug Discovery Today, v.7, p.64-70. 2002. SERGEEVA, M. V. & CATHERS, B. E., Biochem. Pharmacol. v.65, p.823. 2003. SILVA, C. M. & BISCAIA, E. C. Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors. Comp. Chem. Eng. v.27, p.1329- 1344. 2003. S?tio http://www.dockthor.lncc.br, acessado em novembro de 2013. S?tio http://www.graphpad.com, acessado em janeiro de 2014. S?tio http://openmopac.net/index.html, acessado em outubro de 2013. 72 S?tio http://openbabel.org, acessado em 2012. SLATER, J. C. The theory of complex spectra. Phys. Rev., v.34, p.1293-1322, 1929. SMYJ, R. P. A Conformational Analysis Study of a Nickel(II) Enzyme: Urease. J. Mol. Struct. v.391, n.3, p. 207. 1997. SMOOT, D.T. et al. Helicobacter Pylori Urease Activity is Toxic to Human Gastric Epithelial Cells. Infect. Immun. v.58, p1992-1994, 1990. STEWART, J. J. P. Optimization of parameters for semiempirical methods I. Method. J. Comput. Chem., v.10, n.2, p.209-220, 1989. STEWART, J. J. P. Application of localized molecular orbitals to the solution of semiempirical self-consistent field equations, Int. J. Quantum Chem. 58, 133-146, 1996. STEWART, J. J. P. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J. Mol. Model., v.13, p.1173-1213, 2007. STEWART, J. J. P. Optimization of parameters for semiempirical methods VI: more modifications to NDDO aproximations and re-otimization of parameters. J. Mol. Model., v. 19, p.1-32. 2013. SUAREZ, D. et al. Ureases: Quantum Chemical Calculations on Cluster Models. J. Am. Chem. Soc. v.125, p.15325. 2003. SUMNER, J.B. The Isolation and Crystallization of the Enzyme Urease. J. Biol. Chem. 69:435-441. 1926 TERAMOTO, R. & FUKUNISHI, H. Supervised consensus scoring for docking and virtual screening. Journal of Chemical Information and Modeling, v. 47, p. 526-534, 2007. THIEL, W. & VOITYUK, A. A. Extension of the MNDO formalism to d-orbitals - Integral approximations and preliminary numerical results. Theor. Chim. Acta, v.81, p.391-404, 1992a. THIEL, W. & VOITYUK, A. A. Extension of MNDO to d-orbitals - Parameters and results for the halogens. Int. J. Quantum Chem., v. 44, p.807-829, 1992b. 73 VERDONK, M. et al. Improved protein-ligand docking using GOLD. Proteins-Structure Function and Genetics, v. 52, p. 609-623, 2003. VERDONK, M.L., et al. Improved protein-ligand docking using GOLD. Proteins, v.52, p. 609- 623. 2003. VOTANO, J. R. et al. Prediction of Aqueous Solubility Based on Large Datasets Using Several QSPR Models Utilizing Topological Structure Representation. J. Chem Info. Comput. Sci., v.1, n.11, p.1829-1841, 2004. WANG, S. et al. Protein Kinase C. Modeling of the Binding Site and Prediction of Binding Constants. J. Med. Chem., v.37, p.1326-1338, 1994. WANG, R. X. et al. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. v. 46, p. 2287-2303, 2003. WEINER, S.J. et al. A new force field for molecular mechanical simulation of nucleic acids and proteins. J. Am. Chem. Soc., v.106, p.765-784, 1984. WOLFENDEN, R. & SNIDER, M. J. The Depth of Chemical Time and the Power of Enzymes as Catalysts. Acc. Chem. Res, v.34, p.938, 2001. YURIEV, E. & RAMSLAND, A. P. Latest developments in molecular docking: 2010-2011 in review. J. Mol. Recognit, v. 26, p.215?239. 2013. ZAMAN, M. et al. Reducing NH3, N2O and NO3 ? - N losses from a pasture soil with urease or nitrification inhibitors and elemental S-amended nitrogenous fertilizers, Biol. Fertil. Soils. v.44, p.693?705. 2008. ZAMBELLI, B. et al. UreG, a chaperone in the urease assembly process, is an intrinsically unstructured GTPase that specifically binds Zn2+. J Biol Chem. v.280, p.4684. 2005. ZIMMER, M. Are Classical Molecular Machanics Calculations Still Useful in Bioinorganic Simulations. Coord. Chem. Ver. V.253, n5, p.817-826. 2009.
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