Prediction of cytochrome P450 mediated metabolism

Advanced Drug Delivery Reviews

Volumn 86, Issue , 2015, Pages 61-71

  Olsen, Lars ^a   Oostenbrink, Chris ^b   Jørgensen, Flemming Steen ^a  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b UNIVERSITY OF NATURAL RESOURCES AND LIFE SCIENCES   (Austria)

Author keywords

Cytochrome P450; Docking; Ligand based models; Machine learning; Molecular dynamics; Reactivity; Site of metabolism; Structure based models

Indexed keywords

BIOCHEMISTRY; DOCKING; ENZYMES; FORECASTING; LEARNING SYSTEMS; LIGANDS; METABOLITES; MOLECULAR DYNAMICS; REACTION KINETICS; REACTIVITY (NUCLEAR);

ACTIVE SITE; CYTOCHROME P450; CYTOCHROME P450 ENZYMES; HEME GROUP; IN-SILICO; PRODUCT FORMATION; REGIO-SELECTIVE; STRUCTURE-BASED;

METABOLISM;

CYTOCHROME P450; CYTOCHROME P450 1A2; CYTOCHROME P450 2D6; CYTOCHROME P450 3A4; DRUG; LIGAND;

BINDING AFFINITY; DRUG METABOLISM; DRUG PROTEIN BINDING; DRUG STRUCTURE; ENANTIOSELECTIVITY; ENZYME ACTIVE SITE; ENZYME ACTIVITY; ENZYME ANALYSIS; ENZYME STRUCTURE; ENZYME SUBSTRATE COMPLEX; LIGAND BINDING; MOLECULAR DOCKING; MOLECULAR DYNAMICS; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; QUANTUM MECHANICS; REVIEW; STRUCTURE ACTIVITY RELATION; BIOLOGICAL MODEL; CHEMICAL STRUCTURE; HUMAN; METABOLISM;

CYTOCHROME P-450 ENZYME SYSTEM; HUMANS; LIGANDS; MODELS, BIOLOGICAL; MODELS, MOLECULAR; PHARMACEUTICAL PREPARATIONS;

EID: 84937642098     PISSN: 0169409X     EISSN: 18728294     Source Type: Journal    
DOI: 10.1016/j.addr.2015.04.020     Document Type: Review

References (121)

1. Guengerich F.P. Cytochrome p450 and chemical toxicology. Chem. Res. Toxicol. 2008, 21:70-83.
2. Schlichting I., Berendzen J., Chu K., Stock A.M., Maves S.A., Benson D.E., Sweet R.M., Ringe D., Petsko G.A., Sligar S.G. The catalytic pathway of cytochrome P450cam at atomic resolution. Science 2000, 287:1615-1622.
3. Shaik S., Cohen S., Wang Y., Chen H., Kumar D., Thiel W. P450 enzymes: their structure, reactivity, and selectivity-modeled by QM/MM calculations. Chem. Rev. 2010, 110:949-1017.
4. Tarcsay A., Keseru G.M. In silico site of metabolism prediction of cytochrome P450-mediated biotransformations. Expert Opin. Drug Metab. Toxicol. 2011, 7:299-312.
5. Yu X., Cojocaru V., Wade R.C. Conformational diversity and ligand tunnels of mammalian cytochrome P450s. Biotechnol. Appl. Biochem. 2013, 60:134-145.
6. Vermeulen N.P. Prediction of drug metabolism: the case of cytochrome P450 2D6. Curr. Top. Med. Chem. 2003, 3:1227-1239.
7. de Groot M.J., Kirton S.B., Sutcliffe M.J. In silico methods for predicting ligand binding determinants of cytochromes P450. Curr. Top. Med. Chem. 2004, 4:1803-1824.
8. de Graaf C., Vermeulen N.P., Feenstra K.A. Cytochrome p450 in silico: an integrative modeling approach. J. Med. Chem. 2005, 48:2725-2755.
9. Stjernschantz E., Vermeulen N.P., Oostenbrink C. Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450. Expert Opin. Drug Metab. Toxicol. 2008, 4:513-527.
10. Pelkonen O., Turpeinen M., Hakkola J., Honkakoski P., Hukkanen J., Raunio H. Inhibition and induction of human cytochrome P450 enzymes: current status. Arch. Toxicol. 2008, 82:667-715.
11. Mishra N.K. Computational modeling of P450s for toxicity prediction. Expert Opin. Drug Metab. Toxicol. 2011, 7:1211-1231.
12. Rydberg P., Olsen L., Ryde U. Quantum-mechanical studies of reactions performed by cytochrome P450 enzymes. Curr. Inorg. Chem. 2012, 2:292-315.
13. Rydberg P., Jørgensen F.S., Olsen L. Use of density functional theory in drug metabolism studies. Expert Opin. Drug Metab. Toxicol. 2014, 10:215-227.
14. Kirchmair J., Williamson M.J., Tyzack J.D., Tan L., Bond P.J., Bender A., Glen R.C. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J. Chem. Inf. Model. 2012, 52:617-648.
15. Reynald R.L., Sansen S., Stout C.D., Johnson E.F. Structural characterization of human cytochrome P450 2C19: active site differences between P450s 2C8, 2C9, and 2C19. J. Biol. Chem. 2012, 287:44581-44591.
16. Dong D., Wu B., Chow D., Hu M. Substrate selectivity of drug-metabolizing cytochrome P450s predicted from crystal structures and in silico modeling. Drug Metab. Rev. 2012, 44:192-208.
17. Chen B., Wild D.J. PubChem BioAssays as a data source for predictive models. J. Mol. Graph. Model. 2010, 28:420-426.
18. Xie X.Q. Exploiting PubChem for virtual screening. Expert Opin. Drug Discov. 2010, 5:1205-1220.
19. Xie X.Q., Chen J.Z. Data mining a small molecule drug screening representative subset from NIH PubChem. J. Chem. Inf. Model. 2008, 48:465-475.
20. Lapins M., Worachartcheewan A., Spjuth O., Georgiev V., Prachayasittikul V., Nantasenamat C., Wikberg J.E. A unified proteochemometric model for prediction of inhibition of cytochrome p450 isoforms. PLoS One 2013, 8:e66566.
21. Vasanthanathan P., Taboureau O., Oostenbrink C., Vermeulen N.P., Olsen L., Jørgensen F.S. Classification of cytochrome P450 1A2 inhibitors and noninhibitors by machine learning techniques. Drug Metab. Dispos. 2009, 37:658-664.
22. Veith H., Southall N., Huang R., James T., Fayne D., Artemenko N., Shen M., Inglese J., Austin C.P., Lloyd D.G., Auld D.S. Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries. Nat. Biotechnol. 2009, 27:1050-1055.
23. Cheng F., Yu Y., Shen J., Yang L., Li W., Liu G., Lee P.W., Tang Y. Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J. Chem. Inf. Model. 2011, 51:996-1011.
24. Sun H., Veith H., Xia M., Austin C.P., Huang R. Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data. J. Chem. Inf. Model. 2011, 51:2474-2481.
25. Sun H., Veith H., Xia M., Austin C.P., Tice R.R., Huang R. Prediction of cytochrome P450 profiles of environmental chemicals with QSAR models built from drug-like molecules. Mol. Inform. 2012, 31:783-792.
26. Rostkowski M., Spjuth O., Rydberg P. WhichCyp: prediction of cytochromes P450 inhibition. Bioinformatics 2013, 29:2051-2052.
27. Li Z.R., Lin H.H., Han L.Y., Jiang L., Chen X., Chen Y.Z. PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res. 2006, 34:W32-W37.
28. Didziapetris R., Dapkunas J., Sazonovas A., Japertas P. Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition. J. Comput. Aided Mol. Des. 2010, 24:891-906.
29. Mishra N., Agarwal S., Raghava G. Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule. BMC Pharmacol. 2010, 10:8.
30. Terfloth L., Bienfait B., Gasteiger J. Ligand-based models for the isoform specificity of cytochrome P450 3A4, 2D6, and 2C9 substrates. J. Chem. Inf. Model. 2007, 47:1688-1701.
31. Michielan L., Terfloth L., Gasteiger J., Moro S. Comparison of multilabel and single-label classification applied to the prediction of the isoform specificity of cytochrome P450 substrates. J. Chem. Inf. Model. 2009, 49:2588-2605.
32. Cherkasov A., Muratov E.N., Fourches D., Varnek A., Baskin I.I., Cronin M., Dearden J., Gramatica P., Martin Y.C., Todeschini R., Consonni V., Kuz'min V.E., Cramer R., Benigni R., Yang C., Rathman J., Terfloth L., Gasteiger J., Richard A., Tropsha A. QSAR modeling: where have you been? Where are you going to?. J. Med. Chem. 2014, 57:4977-5010.
33. Chen Y.C. Beware of docking!. Trends Pharmacol. Sci. 2015, 36:78-95.
34. Jones G., Willett P., Glen R.C., Leach A.R., Taylor R. Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 1997, 267:727-748.
35. Vasanthanathan P., Hritz J., Taboureau O., Olsen L., Jørgensen F.S., Vermeulen N.P., Oostenbrink C. Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 ligands. J. Chem. Inf. Model. 2009, 49:43-52.
36. Vasanthanathan P., Lastdrager J., Oostenbrink C., Commandeur J.N.M., Vermeulen N.P.E., Jørgensen F.S., Olsen L. Identification of CYP1A2 ligands by structure-based and ligand-based virtual screening. Med. Chem. Commun. 2011, 2:853-859.
37. Vasanthanathan P., Olsen L., Jørgensen F.S., Vermeulen N.P., Oostenbrink C. Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations. Drug Metab. Dispos. 2010, 38:1347-1354.
38. Stjernschantz E., Oostenbrink C. Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys. J. 2010, 98:2682-2691.
39. Åqvist J., Medina C., Samuelsson J.E. A new method for predicting binding affinity in computer-aided drug design. Protein Eng. 1994, 7:385-391.
40. Peric-Hassler L., Stjernschantz E., Oostenbrink C., Geerke D.P. CYP 2D6 binding affinity predictions using multiple ligand and protein conformations. Int. J. Mol. Sci. 2013, 14:24514-24530.
41. Bren U., Oostenbrink C. Cytochrome P450 3A4 inhibition by ketoconazole: tackling the problem of ligand cooperativity using molecular dynamics simulations and free-energy calculations. J. Chem. Inf. Model. 2012, 52:1573-1582.
42. Yin H., Anders M.W., Korzekwa K.R., Higgins L., Thummel K.E., Kharasch E.D., Jones J.P. Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes. Proc. Natl. Acad. Sci. U. S. A. 1995, 92:11076-11080.
43. Higgins L., Korzekwa K.R., Rao S., Shou M., Jones J.P. An assessment of the reaction energetics for cytochrome P450-mediated reactions. Arch. Biochem. Biophys. 2001, 385:220-230.
44. Meunier B., de Visser S.P., Shaik S. Mechanism of oxidation reactions catalyzed by cytochrome p450 enzymes. Chem. Rev. 2004, 104:3947-3980.
45. Shaik S., Kumar D., de Visser S.P., Altun A., Thiel W. Theoretical perspective on the structure and mechanism of cytochrome P450 enzymes. Chem. Rev. 2005, 105:2279-2328.
46. Rydberg P., Lonsdale R., Harvey J.N., Mulholland A.J., Olsen L. Trends in predicted chemoselectivity of cytochrome P450 oxidation: B3LYP barrier heights for epoxidation and hydroxylation reactions. J. Mol. Graph. Model. 2014, 52:30-35.
47. Bathelt C.M., Ridder L., Mulholland A.J., Harvey J.N. Mechanism and structure-reactivity relationships for aromatic hydroxylation by cytochrome P450. Org. Biomol. Chem. 2004, 2:2998-3005.
48. Rydberg P., Ryde U., Olsen L. Prediction of activation energies for aromatic oxidation by cytochrome P450. J. Phys. Chem. A 2008, 112:13058-13065.
49. Olsen L., Rydberg P., Rod T.H., Ryde U. Prediction of activation energies for hydrogen abstraction by cytochrome p450. J. Med. Chem. 2006, 49:6489-6499.
50. Afzelius L., Arnby C.H., Broo A., Carlsson L., Isaksson C., Jurva U., Kjellander B., Kolmodin K., Nilsson K., Raubacher F., Weidolf L. State-of-the-art tools for computational site of metabolism predictions: comparative analysis, mechanistical insights, and future applications. Drug Metab. Rev. 2007, 39:61-86.
51. Hsiao Y.W., Petersson C., Svensson M.A., Norinder U. A pragmatic approach using first-principle methods to address site of metabolism with implications for reactive metabolite formation. J. Chem. Inf. Model. 2012, 52:686-695.
52. Mayeno A.N., Robinson J.L., Reisfeld B. Rapid estimation of activation enthalpies for cytochrome-P450-mediated hydroxylations. J. Comput. Chem. 2011, 32:639-657.
53. Rydberg P., Gloriam D.E., Zaretzki J., Breneman C., Olsen L. SMARTCyp: a 2D method for prediction of cytochrome P450-mediated drug metabolism. ACS Med. Chem. Lett. 2010, 1:96-100.
54. Ekroos M., Sjogren T. Structural basis for ligand promiscuity in cytochrome P450 3A4. Proc. Natl. Acad. Sci. U. S. A. 2006, 103:13682-13687.
55. Rydberg P., Rostkowski M., Gloriam D.E., Olsen L. The contribution of atom accessibility to site of metabolism models for cytochromes P450. Mol. Pharm. 2013, 10:1216-1223.
56. de Groot M.J., Ekins S. Pharmacophore modeling of cytochromes P450. Adv. Drug Deliv. Rev. 2002, 54:367-383.
57. Rowland P., Blaney F.E., Smyth M.G., Jones J.J., Leydon V.R., Oxbrow A.K., Lewis C.J., Tennant M.G., Modi S., Eggleston D.S., Chenery R.J., Bridges A.M. Crystal structure of human cytochrome P450 2D6. J. Biol. Chem. 2006, 281:7614-7622.
58. Wang A., Savas U., Hsu M.H., Stout C.D., Johnson E.F. Crystal structure of human cytochrome P450 2D6 with prinomastat bound. J. Biol. Chem. 2012, 287:10834-10843.
59. Wester M.R., Yano J.K., Schoch G.A., Yang C., Griffin K.J., Stout C.D., Johnson E.F. The structure of human cytochrome P450 2C9 complexed with flurbiprofen at 2.0-a resolution. J. Biol. Chem. 2004, 279:35630-35637.
60. Rydberg P., Olsen L. Predicting drug metabolism by cytochrome P450 2C9: comparison with the 2D6 and 3A4 isoforms. ChemMedChem 2012, 7:1202-1209.
61. Liu R., Liu J., Tawa G., Wallqvist A. 2D SMARTCyp reactivity-based site of metabolism prediction for major drug-metabolizing cytochrome P450 enzymes. J. Chem. Inf. Model. 2012, 52:1698-1712.
62. Zaretzki J., Bergeron C., Rydberg P., Huang T.W., Bennett K.P., Breneman C.M. RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4. J. Chem. Inf. Model. 2011, 51:1667-1689.
63. Zaretzki J., Rydberg P., Bergeron C., Bennett K.P., Olsen L., Breneman C.M. RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes. J. Chem. Inf. Model. 2012, 52:1637-1659.
64. Zaretzki J., Matlock M., Swamidass S.J. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks. J. Chem. Inf. Model. 2013, 53:3373-3383.
65. Stardrop, Optibrium Ltd., Cambridge, United Kingdom See. http://www.optibrium.com/stardrop/.
66. Schrödinger P450 Site of Metabolism Module 2012, Schrödinger, LLC, New York, (See https://http://www.schrodinger.com).
67. Hennemann M., Friedl A., Lobell M., Keldenich J., Hillisch A., Clark T., Goeller A.H. CypScore: quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory. ChemMedChem 2009, 4:657-669.
68. Boyer S., Arnby C.H., Carlsson L., Smith J., Stein V., Glen R.C. Reaction site mapping of xenobiotic biotransformations. J. Chem. Inf. Model. 2007, 47:583-590.
69. Dapkunas J., Sazonovasay A., Japertas P. Probabilistic prediction of the human CYP3A4 and CYP2D6 metabolism sites. Chem. Biodivers. 2009, 6:2101-2106.
70. Tyzack J.D., Mussa H.Y., Williamson M.J., Kirchmair J., Glen R.C. Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers. J. Cheminform. 2014, 6:29.
71. Rudik A.V., Dmitriev A.V., Lagunin A.A., Filimonov D.A., Poroikov V.V. Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm. J. Chem. Inf. Model. 2014, 54:498-507.
72. Meteor, Lhasa Ltd., Leeds, United Kingdom See. http://www.lhasalimited.org/.
73. Testa B., Balmat A.L., Long A. Predicting drug metabolism: concepts and challenges. Pure Appl. Chem. 2004, 76:907-914.
74. Testa B., Balmat A.L., Long A., Judson P. Predicting drug metabolism - an evaluation of the expert system METEOR. Chem. Biodivers. 2005, 2:872-885.
75. Marchant C.A., Briggs K.A., Long A. In silico tools for sharing data and knowledge on toxicity and metabolism: Derek for Windows, Meteor, and Vitic. Toxicol. Mech. Methods 2008, 18:177-187.
76. Cruciani G., Carosati E., De Boeck B., Ethirajulu K., Mackie C., Howe T., Vianello R. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J. Med. Chem. 2005, 48:6970-6979.
77. Ferrara P., Gohlke H., Price D.J., Klebe G., Brooks C.L. Assessing scoring functions for protein-ligand interactions. J. Med. Chem. 2004, 47:3032-3047.
78. Warren G.L., Andrews C.W., Capelli A.M., Clarke B., LaLonde J., Lambert M.H., Lindvall M., Nevins N., Semus S.F., Senger S., Tedesco G., Wall I.D., Woolven J.M., Peishoff C.E., Head M.S. A critical assessment of docking programs and scoring functions. J. Med. Chem. 2006, 49:5912-5931.
79. de Graaf C., Oostenbrink C., Keizers P.H., van der Wijst T., Jongejan A., Vermeulen N.P. Catalytic site prediction and virtual screening of cytochrome P450 2D6 substrates by consideration of water and rescoring in automated docking. J. Med. Chem. 2006, 49:2417-2430.
80. de Graaf C., Pospisil P., Pos W., Folkers G., Vermeulen N.P. Binding mode prediction of cytochrome p450 and thymidine kinase protein-ligand complexes by consideration of water and rescoring in automated docking. J. Med. Chem. 2005, 48:2308-2318.
81. Li J., Schneebeli S.T., Bylund J., Farid R., Friesner R.A. IDSite: an accurate approach to predict P450-mediated drug metabolism. J. Chem. Theory Comput. 2011, 7:3829-3845.
82. Guengerich F.P. A malleable catalyst dominates the metabolism of drugs. Proc. Natl. Acad. Sci. U. S. A. 2006, 103:13565-13566.
83. Oostenbrink C., de Ruiter A., Hritz J., Vermeulen N. Malleability and versatility of cytochrome P450 active sites studied by molecular simulations. Curr. Drug Metab. 2012, 13:190-196.
84. Sansen S., Yano J.K., Reynald R.L., Schoch G.A., Griffin K.J., Stout C.D., Johnson E.F. Adaptations for the oxidation of polycyclic aromatic hydrocarbons exhibited by the structure of human P450 1A2. J. Biol. Chem. 2007, 282:14348-14355.
85. Verdonk M.L., Chessari G., Cole J.C., Hartshorn M.J., Murray C.W., Nissink J.W., Taylor R.D., Taylor R. Modeling water molecules in protein-ligand docking using GOLD. J. Med. Chem. 2005, 48:6504-6515.
86. de Graaf C., Oostenbrink C., Keizers P.H., van Vugt-Lussenburg B.M., van Waterschoot R.A., Tschirret-Guth R.A., Commandeur J.N., Vermeulen N.P. Molecular modeling-guided site-directed mutagenesis of cytochrome P450 2D6. Curr. Drug Metab. 2007, 8:59-77.
87. Hritz J., de Ruiter A., Oostenbrink C. Impact of plasticity and flexibility on docking results for cytochrome P450 2D6: a combined approach of molecular dynamics and ligand docking. J. Med. Chem. 2008, 51:7469-7477.
88. Oostenbrink C. Structure-based methods for predicting the sites and products of metabolism. Drug Metabolism Prediction 2014, 243-263. Wiley, Weinheim. J. Kirchmair (Ed.).
89. Santos R., Hritz J., Oostenbrink C. Role of water in molecular docking simulations of cytochrome P450 2D6. J. Chem. Inf. Model. 2010, 50:146-154.
90. Teixeira V.H., Ribeiro V., Martel P.J. Analysis of binding modes of ligands to multiple conformations of CYP3A4. Biochim. Biophys. Acta 2010, 1804:2036-2045.
91. Hayes C., Ansbro D., Kontoyianni M. Elucidating substrate promiscuity in the human cytochrome 3A4. J. Chem. Inf. Model. 2014, 54:857-869.
92. de Beer S.B., van Bergen L.A., Keijzer K., Rea V., Venkataraman H., Guerra C.F., Bickelhaupt F.M., Vermeulen N.P., Commandeur J.N., Geerke D.P. The role of protein plasticity in computational rationalization studies on regioselectivity in testosterone hydroxylation by cytochrome P450 BM3 mutants. Curr. Drug Metab. 2012, 13:155-166.
93. Danielson M.L., Desai P.V., Mohutsky M.A., Wrighton S.A., Lill M.A. Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: the utility of incorporating protein flexibility via an ensemble of structures. Eur. J. Med. Chem. 2011, 46:3953-3963.
94. Moors S.L., Vos A.M., Cummings M.D., Van Vlijmen H., Ceulemans A. Structure-based site of metabolism prediction for cytochrome P450 2D6. J. Med. Chem. 2011, 54:6098-6105.
95. Jung J., Kim N.D., Kim S.Y., Choi I., Cho K.H., Oh W.S., Kim D.N., No K.T. Regioselectivity prediction of CYP1A2-mediated phase I metabolism. J. Chem. Inf. Model. 2008, 48:1074-1080.
96. Tyzack J.D., Williamson M.J., Torella R., Glen R.C. Prediction of cytochrome P450 xenobiotic metabolism: tethered docking and reactivity derived from ligand molecular orbital analysis. J. Chem. Inf. Model. 2013, 53:1294-1305.
97. Oh W.S., Kim D.N., Jung J., Cho K.H., No K.T. New combined model for the prediction of regioselectivity in cytochrome P450/3A4 mediated metabolism. J. Chem. Inf. Model. 2008, 48:591-601.
98. Rydberg P., Vasanthanathan P., Oostenbrink C., Olsen L. Fast prediction of cytochrome P450 mediated drug metabolism. ChemMedChem 2009, 4:2070-2079.
99. Campagna-Slater V., Pottel J., Therrien E., Cantin L.D., Moitessier N. Development of a computational tool to rival experts in the prediction of sites of metabolism of xenobiotics by p450s. J. Chem. Inf. Model. 2012, 52:2471-2483.
100. Rydberg P., Hansen S.M., Kongsted J., Norrby P.-O., Olsen L., Ryde U. Transition-state docking of flunitrazepam and progesterone in cytochrome P450. J. Chem. Theory Comput. 2008, 4:673-681.
101. Rydberg P., Olsen L., Norrby P.-O., Ryde U. General transition-state force field for cytochrome p450 hydroxylation. J. Chem. Theory Comput. 2007, 3:1765-1773.
102. Nielsen L.M., Linnet K., Olsen L., Rydberg P. Prediction of cytochrome p450 mediated metabolism of designer drugs. Curr. Top. Med. Chem. 2014, 14:1365-1373.
103. Zamora I., Afzelius L., Cruciani G. Predicting drug metabolism: a site of metabolism prediction tool applied to the cytochrome P450 2C9. J. Med. Chem. 2003, 46:2313-2324.
104. Huang T.W., Zaretzki J., Bergeron C., Bennett K.P., Breneman C.M. DR-predictor: incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism. J. Chem. Inf. Model. 2013, 53:3352-3366.
105. Cooper H.L., Groves J.T. Molecular probes of the mechanism of cytochrome P450. Oxygen traps a substrate radical intermediate. Arch. Biochem. Biophys. 2011, 507:111-118.
106. Sündermann A., Oostenbrink C. Molecular dynamics simulations give insight into the conformational change, complex formation, and electron transfer pathway for cytochrome P450 reductase. Protein Sci. 2013, 22:1183-1195.
107. Tarcsay A., Kiss R., Keseru G.M. Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach. J. Comput. Aided Mol. Des. 2010, 24:399-408.
108. Zaretzki J., Bergeron C., Huang T.W., Rydberg P., Swamidass S.J., Breneman C.M. RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules. Bioinformatics 2013, 29:497-498.
109. van der Kamp M.W., Mulholland A.J. Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry 2013, 52:2708-2728.
110. Lonsdale R., Mulholland A.J. QM/MM modelling of drug-metabolizing enzymes. Curr. Top. Med. Chem. 2014, 14:1339-1347.
111. Kwiecien R.A., Le Questel J.Y., Lebreton J., Delaforge M., Andre F., Pihan E., Roussel A., Fournial A., Paneth P., Robins R.J. Cytochrome P450-catalyzed degradation of nicotine: fundamental parameters determining hydroxylation by cytochrome P450 2A6 at the 5'-carbon or the n-methyl carbon. J. Phys. Chem. B 2012, 116:7827-7840.
112. Li D., Huang X., Lin J., Zhan C.G. Catalytic mechanism of cytochrome P450 for N-methylhydroxylation of nicotine: reaction pathways and regioselectivity of the enzymatic nicotine oxidation. Dalton Trans. 2013, 42:3812-3820.
113. Lonsdale R., Houghton K.T., Zurek J., Bathelt C.M., Foloppe N., de Groot M.J., Harvey J.N., Mulholland A.J. Quantum mechanics/molecular mechanics modeling of regioselectivity of drug metabolism in cytochrome P450 2C9. J. Am. Chem. Soc. 2013, 135:8001-8015.
114. Olah J., Mulholland A.J., Harvey J.N. Understanding the determinants of selectivity in drug metabolism through modeling of dextromethorphan oxidation by cytochrome P450. Proc. Natl. Acad. Sci. U. S. A. 2011, 108:6050-6055.
115. Schyman P., Lai W., Chen H., Wang Y., Shaik S. The directive of the protein: how does cytochrome P450 select the mechanism of dopamine formation?. J. Am. Chem. Soc. 2011, 133:7977-7984.
116. Hartman J.H., Cothren S.D., Park S.H., Yun C.H., Darsey J.A., Miller G.P. Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code. Bioorg. Med. Chem. 2013, 21:3749-3759.
117. de Graaf C., Oostenbrink C., Keizers P.H., van Vugt-Lussenburg B.M., Commandeur J.N., Vermeulen N.P. Free energies of binding of R- and S-propranolol to wild-type and F483A mutant cytochrome P450 2D6 from molecular dynamics simulations. Eur. Biophys. J. 2007, 36:589-599.
118. Nagy G., Oostenbrink C. Rationalization of stereospecific binding of propranolol to cytochrome P450 2D6 by free energy calculations. Eur. Biophys. J. 2012, 41:1065-1076.
119. Lai B., Nagy G., Garate J.A., Oostenbrink C. Entropic and enthalpic contributions to stereospecific ligand binding from enhanced sampling methods. J. Chem. Inf. Model. 2014, 54:151-158.
120. de Beer S.B., Venkataraman H., Geerke D.P., Oostenbrink C., Vermeulen N.P. Free energy calculations give insight into the stereoselective hydroxylation of alpha-ionones by engineered cytochrome P450 BM3 mutants. J. Chem. Inf. Model. 2012, 52:2139-2148.
121. Kirchmair J., Williamson M.J., Afzal A.M., Tyzack J.D., Choy A.P., Howlett A., Rydberg P., Glen R.C. FAst MEtabolizer (FAME): a rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes. J. Chem. Inf. Model. 2013, 53:2896-2907.