Literature DB >> 20361237

Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach.

Akos Tarcsay1, Róbert Kiss, György M Keseru.   

Abstract

A novel structure-based approach for site of metabolism prediction has been developed. This knowledge-based method consists of three steps: (1) generation of possible metabolites, (2) docking the predicted metabolites to the CYP binding site and (3) selection of the most probable metabolites based on their complementarity to the binding site. As a proof of concept we evaluated our method by using MetabolExpert for metabolite generation and Glide for docking into the binding site of the CYP2C9 crystal structure. Our method could identify the correct metabolite among the three best-ranked compounds in 69% of the cases. The predictive power of our knowledge-based method was compared to that achieved by substrate docking and two alternative literature approaches.

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Year:  2010        PMID: 20361237     DOI: 10.1007/s10822-010-9347-3

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  33 in total

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Journal:  Nat Rev Drug Discov       Date:  2003-03       Impact factor: 84.694

2.  Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9.

Authors:  Robert P Sheridan; Kenneth R Korzekwa; Rhonda A Torres; Matthew J Walker
Journal:  J Med Chem       Date:  2007-06-19       Impact factor: 7.446

3.  CYP2C9 structure-metabolism relationships: substrates, inhibitors, and metabolites.

Authors:  Marie M Ahlström; Marianne Ridderström; Ismael Zamora
Journal:  J Med Chem       Date:  2007-10-04       Impact factor: 7.446

4.  Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions.

Authors:  Diansong Zhou; Lovisa Afzelius; Scott W Grimm; Tommy B Andersson; Randy J Zauhar; Ismael Zamora
Journal:  Drug Metab Dispos       Date:  2006-03-15       Impact factor: 3.922

5.  Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 ligands.

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Journal:  J Chem Inf Model       Date:  2009-01       Impact factor: 4.956

6.  A refined 3-dimensional QSAR of cytochrome P450 2C9: computational predictions of drug interactions.

Authors:  S Rao; R Aoyama; M Schrag; W F Trager; A Rettie; J P Jones
Journal:  J Med Chem       Date:  2000-07-27       Impact factor: 7.446

7.  The 2.6-A crystal structure of Pseudomonas putida cytochrome P-450.

Authors:  T L Poulos; B C Finzel; I C Gunsalus; G C Wagner; J Kraut
Journal:  J Biol Chem       Date:  1985-12-25       Impact factor: 5.157

8.  Predicting drug metabolism: a site of metabolism prediction tool applied to the cytochrome P450 2C9.

Authors:  Ismael Zamora; Lovisa Afzelius; Gabriele Cruciani
Journal:  J Med Chem       Date:  2003-06-05       Impact factor: 7.446

9.  Prediction of metabolism by cytochrome P450 2C9: alignment and docking studies of a validated database of substrates.

Authors:  Matthew J Sykes; Ross A McKinnon; John O Miners
Journal:  J Med Chem       Date:  2008-02-01       Impact factor: 7.446

Review 10.  Drug-drug interactions for UDP-glucuronosyltransferase substrates: a pharmacokinetic explanation for typically observed low exposure (AUCi/AUC) ratios.

Authors:  J Andrew Williams; Ruth Hyland; Barry C Jones; Dennis A Smith; Susan Hurst; Theunis C Goosen; Vincent Peterkin; Jeffrey R Koup; Simon E Ball
Journal:  Drug Metab Dispos       Date:  2004-08-10       Impact factor: 3.922

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  9 in total

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Review 2.  Predicting drug metabolism: experiment and/or computation?

Authors:  Johannes Kirchmair; Andreas H Göller; Dieter Lang; Jens Kunze; Bernard Testa; Ian D Wilson; Robert C Glen; Gisbert Schneider
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3.  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.

Authors:  Matthew L Danielson; Prashant V Desai; Michael A Mohutsky; Steven A Wrighton; Markus A Lill
Journal:  Eur J Med Chem       Date:  2011-06-23       Impact factor: 6.514

4.  RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes.

Authors:  Jed Zaretzki; Patrik Rydberg; Charles Bergeron; Kristin P Bennett; Lars Olsen; Curt M Breneman
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5.  Impact of Established and Emerging Software Tools on the Metabolite Identification Landscape.

Authors:  Anne Marie E Smith; Kiril Lanevskij; Andrius Sazonovas; Jesse Harris
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Review 6.  Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Authors:  Johannes Kirchmair; Mark J Williamson; Jonathan D Tyzack; Lu Tan; Peter J Bond; Andreas Bender; Robert C Glen
Journal:  J Chem Inf Model       Date:  2012-02-17       Impact factor: 4.956

Review 7.  Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes.

Authors:  Hannu Raunio; Mira Kuusisto; Risto O Juvonen; Olli T Pentikäinen
Journal:  Front Pharmacol       Date:  2015-06-12       Impact factor: 5.810

8.  RD-Metabolizer: an integrated and reaction types extensive approach to predict metabolic sites and metabolites of drug-like molecules.

Authors:  Jiajia Meng; Shiliang Li; Xiaofeng Liu; Mingyue Zheng; Honglin Li
Journal:  Chem Cent J       Date:  2017-07-18       Impact factor: 4.215

9.  Structure-Based Site of Metabolism (SOM) Prediction of Ligand for CYP3A4 Enzyme: Comparison of Glide XP and Induced Fit Docking (IFD).

Authors:  Deepak K Lokwani; Aniket P Sarkate; Kshipra S Karnik; Anna Pratima G Nikalje; Julio A Seijas
Journal:  Molecules       Date:  2020-04-01       Impact factor: 4.411

  9 in total

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