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Kędzierski J, Jäger MC, Naeem S, Odermatt A, Smieško M. In silico and in vitro assessment of drugs potentially causing adverse effects by inhibiting CYP17A1. Toxicol Appl Pharmacol 2024; 486:116945. [PMID: 38688424 DOI: 10.1016/j.taap.2024.116945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/11/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024]
Abstract
Cytochrome P450 enzymes (CYPs) play a crucial role in the metabolism and synthesis of various compound classes. While drug-metabolizing CYP enzymes are frequently investigated as anti-targets, the inhibition of CYP enzymes involved in adrenal steroidogenesis is not well studied. The steroidogenic enzyme CYP17A1 is a dual-function enzyme catalyzing hydroxylase and lyase reactions relevant for the biosynthesis of adrenal glucocorticoids and androgens. Inhibition of CYP17A1-hydroxylase leads to pseudohyperaldosteronism with subsequent excessive mineralocorticoid receptor activation, hypertension and hypokalemia. In contrast, specific inhibition of the lyase function might be beneficial for the treatment of prostate cancer by decreasing adrenal androgen levels. This study combined in silico and in vitro methods to identify drugs inhibiting CYP17A1. The most potent CYP17A1 inhibitors identified are serdemetan, mocetinostat, nolatrexed, liarozole, and talarozole. While some of these drugs are currently under investigation for the treatment of various cancers, their potential for the treatment of prostate cancer is yet to be explored. The DrugBank database was screened for CYP17A1 inhibitors, to increase the awareness for the risk of drug-induced pseudohyperaldosteronism and to highlight drugs so far unknown for their potential to cause side effects resulting from CYP17A1 inhibition.
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Affiliation(s)
- Jacek Kędzierski
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
| | - Marie-Christin Jäger
- Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
| | - Sadaf Naeem
- Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Department of Biochemistry, University of Karachi, KU, Circular Road, Karachi, Pakistan
| | - Alex Odermatt
- Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
| | - Martin Smieško
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, Basel 4056, Switzerland; Swiss Centre for Human Applied Toxicology, University of Basel, Missionsstrasse 64, Basel 4055, Switzerland.
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2
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Long TZ, Jiang DJ, Shi SH, Deng YC, Wang WX, Cao DS. Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence. J Chem Inf Model 2024; 64:3222-3236. [PMID: 38498003 DOI: 10.1021/acs.jcim.4c00159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.
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Affiliation(s)
- Teng-Zhi Long
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - De-Jun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Shao-Hua Shi
- Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
| | - You-Chao Deng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Wen-Xuan Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China
- Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon, Hong Kong SAR 999077, P. R. China
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P. R. China
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3
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Ashworth MA, Bombino E, de Jong RM, Wijma HJ, Janssen DB, McLean KJ, Munro AW. Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin. ACS Catal 2022; 12:15028-15044. [PMID: 36570080 PMCID: PMC9764288 DOI: 10.1021/acscatal.2c03974] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/22/2022] [Indexed: 11/29/2022]
Abstract
CYP105AS1 is a cytochrome P450 from Amycolatopsis orientalis that catalyzes monooxygenation of compactin to 6-epi-pravastatin. For fermentative production of the cholesterol-lowering drug pravastatin, the stereoselectivity of the enzyme needs to be inverted, which has been partially achieved by error-prone PCR mutagenesis and screening. In the current study, we report further optimization of the stereoselectivity by a computationally aided approach. Using the CoupledMoves protocol of Rosetta, a virtual library of mutants was designed to bind compactin in a pro-pravastatin orientation. By examining the frequency of occurrence of beneficial substitutions and rational inspection of their interactions, a small set of eight mutants was predicted to show the desired selectivity and these variants were tested experimentally. The best CYP105AS1 variant gave >99% stereoselective hydroxylation of compactin to pravastatin, with complete elimination of the unwanted 6-epi-pravastatin diastereomer. The enzyme-substrate complexes were also examined by ultrashort molecular dynamics simulations of 50 × 100 ps and 5 × 22 ns, which revealed that the frequency of occurrence of near-attack conformations agreed with the experimentally observed stereoselectivity. These results show that a combination of computational methods and rational inspection could improve CYP105AS1 stereoselectivity beyond what was obtained by directed evolution. Moreover, the work lays out a general in silico framework for specificity engineering of enzymes of known structure.
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Affiliation(s)
- Mark A. Ashworth
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Elvira Bombino
- Department
of Biochemistry, Groningen Biomolecular Sciences and Biotechnology
Institute, University of Groningen, Nijenborgh 4, Groningen 9747 AG, Netherlands
| | - René M. de Jong
- DSM
Food & Beverage, Alexander Fleminglaan 1, 2613 AX Delft, the Netherlands
| | - Hein J. Wijma
- Department
of Biochemistry, Groningen Biomolecular Sciences and Biotechnology
Institute, University of Groningen, Nijenborgh 4, Groningen 9747 AG, Netherlands
| | - Dick B. Janssen
- Department
of Biochemistry, Groningen Biomolecular Sciences and Biotechnology
Institute, University of Groningen, Nijenborgh 4, Groningen 9747 AG, Netherlands,
| | - Kirsty J. McLean
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Manchester M1 7DN, United Kingdom,Department
of Biological and Geographical Sciences, School of Applied Sciences, University of Huddersfield, Huddersfield HD1 3DH, United Kingdom
| | - Andrew W. Munro
- Manchester
Institute of Biotechnology, School of Chemistry, The University of Manchester, Manchester M1 7DN, United Kingdom
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4
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In Silico Prediction of the Metabolic Resistance of Vitamin D Analogs against CYP3A4 Metabolizing Enzyme. Int J Mol Sci 2022; 23:ijms23147845. [PMID: 35887195 PMCID: PMC9322940 DOI: 10.3390/ijms23147845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/11/2022] [Accepted: 07/14/2022] [Indexed: 12/02/2022] Open
Abstract
The microsomal cytochrome P450 3A4 (CYP3A4) and mitochondrial cytochrome P450 24A1 (CYP24A1) hydroxylating enzymes both metabolize vitamin D and its analogs. The three-dimensional (3D) structure of the full-length native human CYP3A4 has been solved, but the respective structure of the main vitamin D hydroxylating CYP24A1 enzyme is unknown. The structures of recombinant CYP24A1 enzymes have been solved; however, from studies of the vitamin D receptor, the use of a truncated protein for docking studies of ligands led to incorrect results. As the structure of the native CYP3A4 protein is known, we performed rigid docking supported by molecular dynamic simulation using CYP3A4 to predict the metabolic conversion of analogs of 1,25-dihydroxyvitamin D2 (1,25D2). This is highly important to the design of novel vitamin D-based drug candidates of reasonable metabolic stability as CYP3A4 metabolizes ca. 50% of the drug substances. The use of the 3D structure data of human CYP3A4 has allowed us to explain the substantial differences in the metabolic conversion of the side-chain geometric analogs of 1,25D2. The calculated free enthalpy of the binding of an analog of 1,25D2 to CYP3A4 agreed with the experimentally observed conversion of the analog by CYP24A1. The metabolic conversion of an analog of 1,25D2 to the main vitamin D hydroxylating enzyme CYP24A1, of unknown 3D structure, can be explained by the binding strength of the analog to the known 3D structure of the CYP3A4 enzyme.
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Labarre A, Stille JK, Patrascu MB, Martins A, Pottel J, Moitessier N. Docking Ligands into Flexible and Solvated Macromolecules. 8. Forming New Bonds─Challenges and Opportunities. J Chem Inf Model 2022; 62:1061-1077. [PMID: 35133156 DOI: 10.1021/acs.jcim.1c00701] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Over the years, structure-based design programs and specifically docking small molecules to proteins have become prominent in drug discovery. However, many of these computational tools have been developed to primarily dock enzyme inhibitors (and ligands to other protein classes) relying heavily on hydrogen bonds and electrostatic and hydrophobic interactions. In reality, many drug targets either feature metal ions, can be targeted covalently, or are simply not even proteins (e.g., nucleic acids). Herein, we describe several new features that we have implemented into Fitted to broaden its applicability to a wide range of covalent enzyme inhibitors and to metalloenzymes, where metal coordination is essential for drug binding. This updated version of our docking program was tested for its ability to predict the correct binding mode of drug-sized molecules in a large variety of proteins. We also report new datasets that were essential to demonstrate areas of success and those where additional efforts are required. This resource could be used by other program developers to assess their own software.
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Affiliation(s)
- Anne Labarre
- Department of Chemistry, McGill University, 801 Sherbrooke St W, Montreal H3A 0B8, Quebec, Canada
| | - Julia K Stille
- Department of Chemistry, McGill University, 801 Sherbrooke St W, Montreal H3A 0B8, Quebec, Canada
| | - Mihai Burai Patrascu
- Department of Chemistry, McGill University, 801 Sherbrooke St W, Montreal H3A 0B8, Quebec, Canada
| | - Andrew Martins
- Department of Chemistry, McGill University, 801 Sherbrooke St W, Montreal H3A 0B8, Quebec, Canada
| | - Joshua Pottel
- Molecular Forecaster Inc., 7171, rue Frederick-Banting, Montreal H4S 1Z9, Quebec, Canada
| | - Nicolas Moitessier
- Department of Chemistry, McGill University, 801 Sherbrooke St W, Montreal H3A 0B8, Quebec, Canada.,Molecular Forecaster Inc., 7171, rue Frederick-Banting, Montreal H4S 1Z9, Quebec, Canada
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6
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Ma D, Zhang L, Yin Y, Gao Y, Wang Q. Spectroscopic studies of the interaction between phosphorus heterocycles and cytochrome P450. J Pharm Anal 2022; 11:757-763. [PMID: 35028181 PMCID: PMC8740452 DOI: 10.1016/j.jpha.2020.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 12/11/2020] [Accepted: 12/17/2020] [Indexed: 11/17/2022] Open
Abstract
P450 fatty acid decarboxylase OleT from Staphylococcus aureus (OleTSA) is a novel cytochrome P450 enzyme that catalyzes the oxidative decarboxylation of fatty acids to yield primarily terminal alkenes and CO2 or minor α- and β-hydroxylated fatty acids as side-products. In this work, the interactions between a series of cycloalkyl phosphorus heterocycles (CPHs) and OleTSA were investigated in detail by fluorescence titration experiment, ultraviolet-visible (UV-vis) and 31P NMR spectroscopies. Fluorescence titration experiment results clearly showed that a dynamic quenching occurred when CPH-6, a representative CPHs, interacted with OleTSA with a binding constant value of 15.2 × 104 M-1 at 293 K. The thermodynamic parameters (ΔH, ΔS and ΔG) showed that the hydrogen bond and van der Waals force played major roles in the interaction between OleTSA and CPHs. The UV-vis and 31P NMR studies indicated the penetration of CPH-6 into the interior environment of OleTSA, which greatly affects the enzymatic activity of OleTSA. Therefore, our study revealed an effective way to use phosphorus heterocyclic compounds to modulate the activity of cytochrome P450 enzymes.
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Affiliation(s)
- Dumei Ma
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, Fujian, China.,Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC, 29208, USA
| | - Libo Zhang
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC, 29208, USA
| | - Yingwu Yin
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, Fujian, China
| | - Yuxing Gao
- Department of Chemistry and Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, Fujian, China
| | - Qian Wang
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC, 29208, USA
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7
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Tinivella A, Pinzi L, Rastelli G. Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models. J Cheminform 2021; 13:18. [PMID: 33676550 PMCID: PMC7937250 DOI: 10.1186/s13321-021-00499-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 02/22/2021] [Indexed: 11/23/2022] Open
Abstract
The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.
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Affiliation(s)
- Annachiara Tinivella
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125, Modena, Italy.
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8
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Rifai EA, van Dijk M, Geerke DP. Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations. Front Mol Biosci 2020; 7:114. [PMID: 32626725 PMCID: PMC7311763 DOI: 10.3389/fmolb.2020.00114] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 05/14/2020] [Indexed: 11/13/2022] Open
Abstract
The linear interaction energy (LIE) approach is an end-point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔG bind . This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of ΔG bind for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on ΔG bind computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and -dynamic based ΔG bind calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).
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Affiliation(s)
- Eko Aditya Rifai
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Marc van Dijk
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Daan P Geerke
- AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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9
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Ji L. Synergy between Experiments and Computations: A Green Channel for Revealing Metabolic Mechanism of Xenobiotics in Chemical Toxicology. Chem Res Toxicol 2020; 33:1539-1550. [DOI: 10.1021/acs.chemrestox.9b00448] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Li Ji
- College of Environmental and Resource Sciences, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China
- Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
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10
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Fessner ND. P450 Monooxygenases Enable Rapid Late-Stage Diversification of Natural Products via C-H Bond Activation. ChemCatChem 2019; 11:2226-2242. [PMID: 31423290 PMCID: PMC6686969 DOI: 10.1002/cctc.201801829] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/07/2019] [Indexed: 01/07/2023]
Abstract
The biological potency of natural products has been exploited for decades. Their inherent structural complexity and natural diversity might hold the key to efficiently address the urgent need for the development of novel pharmaceuticals. At the same time, it is that very complexity, which impedes necessary chemical modifications such as structural diversification, to improve the effectiveness of the drug. For this purpose, Cytochrome P450 enzymes, which possess unique abilities to activate inert sp3-hybridised C-H bonds in a late-stage fashion, offer an attractive synthetic tool. In this review the potential of cytochrome P450 enzymes in chemoenzymatic lead diversification is illustrated discussing studies reporting late-stage functionalisations of natural products and other high-value compounds. These enzymes were proven to extend the synthetic toolbox significantly by adding to the flexibility and efficacy of synthetic strategies of natural product chemists, and scientists of other related disciplines.
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Affiliation(s)
- Nico D. Fessner
- Institute of Molecular BiotechnologyGraz University of Technology, NAWI GrazPetersgasse 148010GrazAustria
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11
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Four Major Channels Detected in the Cytochrome P450 3A4: A Step toward Understanding Its Multispecificity. Int J Mol Sci 2019; 20:ijms20040987. [PMID: 30823507 PMCID: PMC6412807 DOI: 10.3390/ijms20040987] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 02/12/2019] [Accepted: 02/20/2019] [Indexed: 12/27/2022] Open
Abstract
We computed the network of channels of the 3A4 isoform of the cytochrome P450 (CYP) on the basis of 16 crystal structures extracted from the Protein Data Bank (PDB). The calculations were performed with version 2 of the CCCPP software that we developed for this research project. We identified the minimal cost paths (MCPs) output by CCCPP as probable ways to access to the buried active site. The algorithm of calculation of the MCPs is presented in this paper, with its original method of visualization of the channels. We found that these MCPs constitute four major channels in CYP3A4. Among the many channels proposed by Cojocaru et al. in 2007, we found that only four of them open in 3A4. We provide a refined description of these channels together with associated quantitative data.
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12
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Kazmi SR, Jun R, Yu MS, Jung C, Na D. In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput Biol Med 2019; 106:54-64. [PMID: 30682640 DOI: 10.1016/j.compbiomed.2019.01.008] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 01/14/2019] [Accepted: 01/14/2019] [Indexed: 01/08/2023]
Abstract
The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.
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Affiliation(s)
- Sayada Reemsha Kazmi
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Ren Jun
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Myeong-Sang Yu
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Chanjin Jung
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Dokyun Na
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea.
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13
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Luirink RA, Dekker SJ, Capoferri L, Janssen LF, Kuiper CL, Ari ME, Vermeulen NP, Vos JC, Commandeur JN, Geerke DP. A combined computational and experimental study on selective flucloxacillin hydroxylation by cytochrome P450 BM3 variants. J Inorg Biochem 2018; 184:115-122. [DOI: 10.1016/j.jinorgbio.2018.04.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 03/23/2018] [Accepted: 04/18/2018] [Indexed: 12/20/2022]
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14
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van Dijk M, ter Laak AM, Wichard JD, Capoferri L, Vermeulen NPE, Geerke DP. Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors. J Chem Inf Model 2017; 57:2294-2308. [PMID: 28776988 PMCID: PMC5615371 DOI: 10.1021/acs.jcim.7b00222] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Indexed: 11/30/2022]
Abstract
Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol-1), with good cross-validation statistics.
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Affiliation(s)
- Marc van Dijk
- AIMMS
Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical
Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | | | - Jörg D. Wichard
- Bayer AG, Pharmaceuticals Division, Müllerstrasse
178, D-13353 Berlin, Germany
| | - Luigi Capoferri
- AIMMS
Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical
Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Nico P. E. Vermeulen
- AIMMS
Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical
Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Daan P. Geerke
- AIMMS
Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical
Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
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15
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Ghadari R. The role of human CYP2C8 in the metabolizing of montelukast-like compounds: a computational study. RESEARCH ON CHEMICAL INTERMEDIATES 2017. [DOI: 10.1007/s11164-017-2911-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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16
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Li J, Du H, Wu Z, Su H, Liu G, Tang Y, Li W. Interactions of omeprazole-based analogues with cytochrome P450 2C19: a computational study. MOLECULAR BIOSYSTEMS 2017; 12:1913-21. [PMID: 27098535 DOI: 10.1039/c6mb00139d] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Cytochrome P450 2C19 (CYP2C19) is one of 57 drug metabolizing enzymes in humans and is responsible for the metabolism of ∼7-10% of drugs in clinical use. Recently omeprazole-based analogues were reported to be the potent inhibitors of CYP2C19 and have the potential to be used as the tool compounds for studying the substrate selectivity of CYP2C19. However, the binding modes of these compounds with CYP2C19 remain to be elucidated. In this study, a combination of molecular docking, molecular dynamics (MD), and MM/GBSA calculations was employed to systematically investigate the interactions between these compounds and CYP2C19. The binding modes of these analogues were analyzed in detail. The results indicated that the inclusion of explicit active site water molecules could improve binding energy prediction when the water molecules formed a hydrogen bonding network between the ligand and protein. We also found that the effect of active site water molecules on binding free energy prediction was dependent on the ligand binding modes. Our results unravel the interactions of these omeprazole-based analogues with CYP2C19 and might be helpful for the future design of potent CYP2C19 inhibitors with improved metabolic properties.
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Affiliation(s)
- Junhao Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
| | - Hanwen Du
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
| | - Haixia Su
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
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17
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Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System. Int J Mol Sci 2016; 17:ijms17101686. [PMID: 27735849 PMCID: PMC5085718 DOI: 10.3390/ijms17101686] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 09/22/2016] [Accepted: 09/30/2016] [Indexed: 02/04/2023] Open
Abstract
During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy.
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18
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Kesharwani SS, Nandekar PP, Pragyan P, Rathod V, Sangamwar AT. Characterization of differences in substrate specificity among CYP1A1, CYP1A2 and CYP1B1: an integrated approach employing molecular docking and molecular dynamics simulations. J Mol Recognit 2016; 29:370-90. [PMID: 26916064 DOI: 10.1002/jmr.2537] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Revised: 12/16/2015] [Accepted: 01/08/2016] [Indexed: 01/05/2023]
Abstract
Recent trends in new drug discovery of anticancer drugs have made oncologists more aware of the fact that the new drug discovery must target the developing mechanism of tumorigenesis to improve the therapeutic efficacy of antineoplastic drugs. The drugs designed are expected to have high affinity towards the novel targets selectively. Current research highlights overexpression of CYP450s, particularly cytochrome P450 1A1 (CYP1A1), in tumour cells, representing a novel target for anticancer therapy. However, the CYP1 family is identified as posing significant problems in selectivity of anticancer molecules towards CYP1A1. Three members have been identified in the human CYP1 family: CYP1A1, CYP1A2 and CYP1B1. Although sequences of the three isoform have high sequence identity, they have distinct substrate specificities. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics, design novel antitumour compounds that could be specifically metabolized by only CYP1A1 to mediate their antitumour activity and elucidate the reasons for differences in substrate specificity profile among the three proteins. In the present study, we employed a combination of computational methodologies: molecular docking and molecular dynamics simulations. We utilized eight substrates for elucidating the difference in substrate specificity of the three isoforms. Lastly, we conclude that the substrate specificity of a particular substrate depends upon the type of the active site residues, the dynamic motions in the protein structure upon ligand binding and the physico-chemical characteristics of a particular ligand. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Siddharth S Kesharwani
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Prajwal P Nandekar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Preeti Pragyan
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Vijay Rathod
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
| | - Abhay T Sangamwar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar-, 160062 Punjab, India
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19
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Perryman AL, Stratton TP, Ekins S, Freundlich JS. Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data. Pharm Res 2016; 33:433-49. [PMID: 26415647 PMCID: PMC4712113 DOI: 10.1007/s11095-015-1800-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 09/22/2015] [Indexed: 02/07/2023]
Abstract
PURPOSE Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability. METHODS Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism). RESULTS "Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h. CONCLUSIONS Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.
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Affiliation(s)
- Alexander L Perryman
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Thomas P Stratton
- Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave., Newark, New Jersey, 07103, USA
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, USA
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, USA
| | - Joel S Freundlich
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, New Jersey, 07103, USA.
- Department of Pharmacology & Physiology, Rutgers University-New Jersey Medical School, Medical Sciences Building, I-503, 185 South Orange Ave., Newark, New Jersey, 07103, USA.
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20
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Li J, Cai J, Su H, Du H, Zhang J, Ding S, Liu G, Tang Y, Li W. Effects of protein flexibility and active site water molecules on the prediction of sites of metabolism for cytochrome P450 2C19 substrates. MOLECULAR BIOSYSTEMS 2016; 12:868-78. [DOI: 10.1039/c5mb00784d] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development.
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Affiliation(s)
- Junhao Li
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Jinya Cai
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Haixia Su
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Hanwen Du
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Juan Zhang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Shihui Ding
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
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21
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Capoferri L, Verkade-Vreeker MCA, Buitenhuis D, Commandeur JNM, Pastor M, Vermeulen NPE, Geerke DP. Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation. PLoS One 2015; 10:e0142232. [PMID: 26551865 PMCID: PMC4638363 DOI: 10.1371/journal.pone.0142232] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 10/18/2015] [Indexed: 11/22/2022] Open
Abstract
Prediction of human Cytochrome P450 (CYP) binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD) simulations and Linear Interaction Energy (LIE) theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE) of 4.1 kJ mol-1 and a standard error in prediction (SDEP) in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units).
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Affiliation(s)
- Luigi Capoferri
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, VU University, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Marlies C. A. Verkade-Vreeker
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, VU University, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Danny Buitenhuis
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, VU University, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Jan N. M. Commandeur
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, VU University, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader, 88, E-08003 Barcelona, Spain
| | - Nico P. E. Vermeulen
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, VU University, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Daan P. Geerke
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, VU University, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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22
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Zisaki A, Miskovic L, Hatzimanikatis V. Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches. Curr Pharm Des 2015; 21:806-22. [PMID: 25341854 PMCID: PMC4435036 DOI: 10.2174/1381612820666141024151119] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 10/09/2014] [Indexed: 02/07/2023]
Abstract
Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements. Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present. Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches. This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.
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Affiliation(s)
| | | | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology (LCSB), Ecole Polytechnique Federale de Lausanne, EPFL/SB/ISIC/LCSB, CH H4 624/ Station 6/ CH-1015 Lausanne/ Switzerland.
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23
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Kesharwani SS, Nandekar PP, Pragyan P, Sangamwar AT. Comparative proteomics among cytochrome p450 family 1 for differential substrate specificity. Protein J 2015; 33:536-48. [PMID: 25331835 DOI: 10.1007/s10930-014-9586-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Apart from playing key roles in drug metabolism and adverse drug-drug interactions, CYPs are potential drug targets to treat a variety of diseases. The intervention of over expression of P450 1A1 (CYP1A1) in tumor cells is identified as a novel strategy for anticancer therapy. We investigated three isoforms of CYP1 family (CYP1A1, CYP1A2, and CYP1B1) for their substrate specificity. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics. This can help in design of new antitumor molecule specifically metabolized by CYP1A1 to mediate their antitumor activity. In the present study, we carried out the comparative protein structure analysis of the three isoforms. Sequence alignment, root mean square deviation (RMSD) analysis, B-factor analysis was performed to give a better understanding of the macromolecular features involved in substrate specificity and to understand the interplay between protein dynamics and functions which will have important implications on rational design of anticancer drugs. We identified the differences in amino acid residues among the three isoforms of CYP1 family, which may account for differential substrate specificity. Six putative substrate recognition sequences are characterized along with the regions they form in the protein structure. Further the RMSD and B-factor analysis provides the information about the identified residues having the maximum RMSD and B-factor deviations.
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Affiliation(s)
- Siddharth S Kesharwani
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab, 160062, India
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24
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Dai ZR, Ai CZ, Ge GB, He YQ, Wu JJ, Wang JY, Man HZ, Jia Y, Yang L. A Mechanism-Based Model for the Prediction of the Metabolic Sites of Steroids Mediated by Cytochrome P450 3A4. Int J Mol Sci 2015; 16:14677-94. [PMID: 26133240 PMCID: PMC4519866 DOI: 10.3390/ijms160714677] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Revised: 05/26/2015] [Accepted: 05/27/2015] [Indexed: 11/16/2022] Open
Abstract
Early prediction of xenobiotic metabolism is essential for drug discovery and development. As the most important human drug-metabolizing enzyme, cytochrome P450 3A4 has a large active cavity and metabolizes a broad spectrum of substrates. The poor substrate specificity of CYP3A4 makes it a huge challenge to predict the metabolic site(s) on its substrates. This study aimed to develop a mechanism-based prediction model based on two key parameters, including the binding conformation and the reaction activity of ligands, which could reveal the process of real metabolic reaction(s) and the site(s) of modification. The newly established model was applied to predict the metabolic site(s) of steroids; a class of CYP3A4-preferred substrates. 38 steroids and 12 non-steroids were randomly divided into training and test sets. Two major metabolic reactions, including aliphatic hydroxylation and N-dealkylation, were involved in this study. At least one of the top three predicted metabolic sites was validated by the experimental data. The overall accuracy for the training and test were 82.14% and 86.36%, respectively. In summary, a mechanism-based prediction model was established for the first time, which could be used to predict the metabolic site(s) of CYP3A4 on steroids with high predictive accuracy.
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Affiliation(s)
- Zi-Ru Dai
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
- Graduate School of Chinese Academy of Sciences, Beijing 100049, China.
| | - Chun-Zhi Ai
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Guang-Bo Ge
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Yu-Qi He
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Jing-Jing Wu
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Jia-Yue Wang
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Hui-Zi Man
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Yan Jia
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
- Graduate School of Chinese Academy of Sciences, Beijing 100049, China.
| | - Ling Yang
- Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
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25
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Olsen L, Oostenbrink C, Jørgensen FS. Prediction of cytochrome P450 mediated metabolism. Adv Drug Deliv Rev 2015; 86:61-71. [PMID: 25958010 DOI: 10.1016/j.addr.2015.04.020] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 03/30/2015] [Accepted: 04/27/2015] [Indexed: 10/23/2022]
Abstract
Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure-based methods that consider how the substrate is oriented in the active site or/and how reactive the part of the substrate that is accessible to the heme group is. We will review key aspects for various approaches that are available to predict binding and site of metabolism (SOM), what outcome can be expected from the predictions, and how they could potentially be improved.
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26
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Raunio H, Kuusisto M, Juvonen RO, Pentikäinen OT. Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes. Front Pharmacol 2015; 6:123. [PMID: 26124721 PMCID: PMC4464169 DOI: 10.3389/fphar.2015.00123] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 05/29/2015] [Indexed: 01/01/2023] Open
Abstract
The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.
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Affiliation(s)
- Hannu Raunio
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland Kuopio, Finland
| | - Mira Kuusisto
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland Kuopio, Finland ; Computational Bioscience Laboratory, Department of Biological and Environmental Science, Nanoscience Center, University of Jyväskylä Jyväskylä, Finland
| | - Risto O Juvonen
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland Kuopio, Finland
| | - Olli T Pentikäinen
- Computational Bioscience Laboratory, Department of Biological and Environmental Science, Nanoscience Center, University of Jyväskylä Jyväskylä, Finland
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27
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Bren U, Fuchs JE, Oostenbrink C. Cooperative binding of aflatoxin B1 by cytochrome P450 3A4: a computational study. Chem Res Toxicol 2014; 27:2136-47. [PMID: 25398138 DOI: 10.1021/tx5004062] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Aflatoxin B1 (AFB1)-the most potent natural carcinogen known to men-is metabolized by cytochrome P450 3A4 (CYP3A4), either to the genotoxic AFB1 exo-8,9-epoxide or to the detoxified 3α-hydroxy AFB1. The activation of the procarcinogen proceeds in a highly cooperative fashion, which differs from common allosteric regulation in the sense that it can be attributed to simultaneous occupancy of a single large and malleable active site by multiple ligand molecules. Unfortunately, unlike in the case of ketoconazole, there is currently no experimental structure available for the doubly ligated CYP3A4-AFB1 complex. Therefore, we employed a sequential molecular docking protocol to create various possible doubly ligated complexes and subsequently performed molecular dynamics simulations and free-energy calculations to check for their consistency with the available experimental data on regio- and stereoselectivity of both AFB1 oxidations as well as with available kinetic data. Only the system in which the first AFB1 molecule was bound in a face-on C8-C9 epoxidation mode and the second AFB1 molecule was bound in a side-on 3α-hydroxylation mode-a result of an unconstrained molecular docking protocol-has successfully fulfilled all the imposed criteria and is therefore proposed as the most likely structure of the doubly ligated complex of CYP3A4 with AFB1. The empirical Linear Interaction Energy method revealed that shape complementarity through nonpolar dispersion interactions between the two bound AFB1 molecules is the main source of the experimentally observed positive homotropic cooperativity. The reported study represents a nice example of how state-of-the-art molecular modeling techniques can be used to study complicated macromolecular complexes, whose structures have not yet been experimentally determined, and to validate these against the available experimental data. The proposed structure will facilitate future studies on the rational design of successful AFB1 modulators or on human subpopulations characterized by specific CYP3A4 polymorphisms that are especially sensitive to AFB1.
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Affiliation(s)
- Urban Bren
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences , Muthgasse 18, AT-1190 Vienna, Austria
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28
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Oostenbrink C. Structure‐Based Methods for Predicting the Sites and Products of Metabolism. ACTA ACUST UNITED AC 2014. [DOI: 10.1002/9783527673261.ch10] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Wijma HJ, Marrink SJ, Janssen DB. Computationally efficient and accurate enantioselectivity modeling by clusters of molecular dynamics simulations. J Chem Inf Model 2014; 54:2079-92. [PMID: 24916632 DOI: 10.1021/ci500126x] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Computational approaches could decrease the need for the laborious high-throughput experimental screening that is often required to improve enzymes by mutagenesis. Here, we report that using multiple short molecular dynamics (MD) simulations makes it possible to accurately model enantioselectivity for large numbers of enzyme-substrate combinations at low computational costs. We chose four different haloalkane dehalogenases as model systems because of the availability of a large set of experimental data on the enantioselective conversion of 45 different substrates. To model the enantioselectivity, we quantified the frequency of occurrence of catalytically productive conformations (near attack conformations) for pairs of enantiomers during MD simulations. We found that the angle of nucleophilic attack that leads to carbon-halogen bond cleavage was a critical variable that limited the occurrence of productive conformations; enantiomers for which this angle reached values close to 180° were preferentially converted. A cluster of 20-40 very short (10 ps) MD simulations allowed adequate conformational sampling and resulted in much better agreement to experimental enantioselectivities than single long MD simulations (22 ns), while the computational costs were 50-100 fold lower. With single long MD simulations, the dynamics of enzyme-substrate complexes remained confined to a conformational subspace that rarely changed significantly, whereas with multiple short MD simulations a larger diversity of conformations of enzyme-substrate complexes was observed.
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Affiliation(s)
- Hein J Wijma
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen , Nijenborgh 4, 9747 AG Groningen, The Netherlands
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Cruciani G, Baroni M, Benedetti P, Goracci L, Fortuna CG. Exposition and reactivity optimization to predict sites of metabolism in chemicals. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e155-65. [PMID: 24050245 DOI: 10.1016/j.ddtec.2012.11.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Chemical modifications of drugs induced by phase I biotransformations significantly affect their pharmacokinetic properties. Because the metabolites produced can themselves have a pharmacological effect and an intrinsic toxicity, medicinal chemists need to accurately predict the sites of metabolism (SoM) of drugs as early as possible. However, site of metabolism prediction is rarely accompanied by a prediction of the relative abundance of the various metabolites. Such a prediction would be a great help in the study of drug– drug interactions and in the process of reducing the toxicity of potential drug candidates. The aim of this paper is to present recent developments in the prediction of xenobiotic metabolism and to use concrete examples to explain the computational mechanism employed.
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31
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Towards automated binding affinity prediction using an iterative linear interaction energy approach. Int J Mol Sci 2014; 15:798-816. [PMID: 24413750 PMCID: PMC3907839 DOI: 10.3390/ijms15010798] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Revised: 12/17/2013] [Accepted: 12/23/2013] [Indexed: 02/03/2023] Open
Abstract
Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.
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Sousa MC, Braga RC, Cintra BA, de Oliveira V, Andrade CH. In silico metabolism studies of dietary flavonoids by CYP1A2 and CYP2C9. Food Res Int 2013. [DOI: 10.1016/j.foodres.2012.09.027] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Widmann M, Pleiss J, Samland AK. Computational tools for rational protein engineering of aldolases. Comput Struct Biotechnol J 2012; 2:e201209016. [PMID: 24688657 PMCID: PMC3962226 DOI: 10.5936/csbj.201209016] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Revised: 10/31/2012] [Accepted: 11/07/2012] [Indexed: 11/22/2022] Open
Abstract
In this mini-review we describe the different strategies for rational protein engineering and summarize the computational tools available. Computational tools can either be used to design focused libraries, to predict sequence-function relationships or for structure-based molecular modelling. This also includes de novo design of enzymes. Examples for protein engineering of aldolases and transaldolases are given in the second part of the mini-review.
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Affiliation(s)
- Michael Widmann
- Institute of Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Jürgen Pleiss
- Institute of Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
| | - Anne K Samland
- Institute of Microbiology, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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Berne S, Podobnik B, Zupanec N, Novak M, Kraševec N, Turk S, Korošec B, Lah L, Šuligoj E, Stojan J, Gobec S, Komel R. Virtual Screening Yields Inhibitors of Novel Antifungal Drug Target, Benzoate 4-Monooxygenase. J Chem Inf Model 2012; 52:3053-63. [DOI: 10.1021/ci3004418] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Sabina Berne
- Faculty of Medicine, Institute
of Biochemistry, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia
| | - Barbara Podobnik
- Lek Pharmaceuticals d.d., Verovškova 57, SI-1000 Ljubljana, Slovenia
| | - Neja Zupanec
- Laboratory for Molecular Biology
and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Metka Novak
- Laboratory for Molecular Biology
and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Nada Kraševec
- Laboratory for Molecular Biology
and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Samo Turk
- Faculty of Pharmacy, Chair of
Pharmaceutical Chemistry, University of Ljubljana, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Branka Korošec
- Laboratory for Molecular Biology
and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Ljerka Lah
- Laboratory for Molecular Biology
and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Erika Šuligoj
- Laboratory for Molecular Biology
and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Jure Stojan
- Faculty of Medicine, Institute
of Biochemistry, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia
| | - Stanislav Gobec
- Faculty of Pharmacy, Chair of
Pharmaceutical Chemistry, University of Ljubljana, Aškerčeva cesta 7, SI-1000 Ljubljana, Slovenia
| | - Radovan Komel
- Faculty of Medicine, Institute
of Biochemistry, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia
- Laboratory for Molecular Biology
and Nanobiotechnology, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
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Campagna-Slater V, Pottel J, Therrien E, Cantin LD, 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; 52:2471-83. [PMID: 22916680 DOI: 10.1021/ci3003073] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The metabolism of xenobiotics--and more specifically drugs--in the liver is a critical process controlling their half-life. Although there exist experimental methods, which measure the metabolic stability of xenobiotics and identify their metabolites, developing higher throughput predictive methods is an avenue of research. It is expected that predicting the chemical nature of the metabolites would be an asset for designing safer drugs and/or drugs with modulated half-lives. We have developed IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States), a computational tool combining docking to metabolic enzymes, transition state modeling, and rule-based substrate reactivity prediction to predict the site of metabolism (SoM) of xenobiotics. Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates and comparison to experts' predictions demonstrates its accuracy and significance. IMPACTS identified an experimentally observed SoM in the top 2 predicted sites for 77% of the substrates, while the accuracy of biotransformation experts' prediction was 65%. Application of IMPACTS to external sets and comparison of its accuracy to those of eleven other methods further validated the method implemented in IMPACTS.
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Affiliation(s)
- Valérie Campagna-Slater
- Department of Chemistry, McGill University, 801 Sherbrooke St W, Montreal, QC H3A 0B8, Canada
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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; 52:1573-82. [PMID: 22587011 DOI: 10.1021/ci300118x] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cytochrome P450 3A4 (CYP3A4) metabolizes more than 50% of clinically used drugs and is often involved in adverse drug-drug interactions. It displays atypical binding and kinetic behavior toward a number of ligands characterized by a sigmoidal shape of the corresponding titration curves, which is indicative of a positive homotropic cooperativity. This requires a participation of at least two ligand molecules, whereby the binding of the first ligand molecule increases the affinity of CYP3A4 for the binding of the second ligand molecule. In the current study, a combination of molecular dynamics simulations and free-energy calculations was applied to elucidate the physicochemical origin of the observed positive homotropic cooperativity in ketoconazole binding to CYP3A4. The binding of the first ketoconazole molecule was established to increase the affinity for the binding of the second ketoconazole molecule by 5 kJ mol(-1), which explains and quantifies the experimentally observed cooperative behavior of CYP3A4. Shape complementarity through nonpolar van der Waals interactions was identified as the main driving force of this binding, which seems to be in line with the promiscuous nature of CYP3A4. Moreover, the calculated binding free energies were found to be in good agreement with the values predicted from a simple 2-ligand binding kinetic model as well as to successfully reproduce the experimental titration curve. This confirms the general applicability of rapid free-energy methods to study challenging biomolecular systems like cytochromes P450, which are characterized by a large flexibility and malleability of their active sites.
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Affiliation(s)
- Urban Bren
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, AT-1190 Vienna, Austria
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Kirchmair J, Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, Glen RC. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012; 52:617-48. [PMID: 22339582 PMCID: PMC3317594 DOI: 10.1021/ci200542m] [Citation(s) in RCA: 187] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
![]()
Metabolism of xenobiotics remains a central challenge
for the discovery
and development of drugs, cosmetics, nutritional supplements, and
agrochemicals. Metabolic transformations are frequently related to
the incidence of toxic effects that may result from the emergence
of reactive species, the systemic accumulation of metabolites, or
by induction of metabolic pathways. Experimental investigation of
the metabolism of small organic molecules is particularly resource
demanding; hence, computational methods are of considerable interest
to complement experimental approaches. This review provides a broad
overview of structure- and ligand-based computational methods for
the prediction of xenobiotic metabolism. Current computational approaches
to address xenobiotic metabolism are discussed from three major perspectives:
(i) prediction of sites of metabolism (SOMs), (ii) elucidation of
potential metabolites and their chemical structures, and (iii) prediction
of direct and indirect effects of xenobiotics on metabolizing enzymes,
where the focus is on the cytochrome P450 (CYP) superfamily of enzymes,
the cardinal xenobiotics metabolizing enzymes. For each of these domains,
a variety of approaches and their applications are systematically
reviewed, including expert systems, data mining approaches, quantitative
structure–activity relationships (QSARs), and machine learning-based
methods, pharmacophore-based algorithms, shape-focused techniques,
molecular interaction fields (MIFs), reactivity-focused techniques,
protein–ligand docking, molecular dynamics (MD) simulations,
and combinations of methods. Predictive metabolism is a developing
area, and there is still enormous potential for improvement. However,
it is clear that the combination of rapidly increasing amounts of
available ligand- and structure-related experimental data (in particular,
quantitative data) with novel and diverse simulation and modeling
approaches is accelerating the development of effective tools for
prediction of in vivo metabolism, which is reflected by the diverse
and comprehensive data sources and methods for metabolism prediction
reviewed here. This review attempts to survey the range and scope
of computational methods applied to metabolism prediction and also
to compare and contrast their applicability and performance.
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Affiliation(s)
- Johannes Kirchmair
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom
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38
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Accessing, using, and creating chemical property databases for computational toxicology modeling. Methods Mol Biol 2012; 929:221-41. [PMID: 23007432 DOI: 10.1007/978-1-62703-050-2_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Toxicity data is expensive to generate, is increasingly seen as precompetitive, and is frequently used for the generation of computational models in a discipline known as computational toxicology. Repositories of chemical property data are valuable for supporting computational toxicologists by providing access to data regarding potential toxicity issues with compounds as well as for the purpose of building structure-toxicity relationships and associated prediction models. These relationships use mathematical, statistical, and modeling computational approaches and can be used to understand the mechanisms by which chemicals cause harm and, ultimately, enable prediction of adverse effects of these chemicals to human health and/or the environment. Such approaches are of value as they offer an opportunity to prioritize chemicals for testing. An increasing amount of data used by computational toxicologists is being published into the public domain and, in parallel, there is a greater availability of Open Source software for the generation of computational models. This chapter provides an overview of the types of data and software available and how these may be used to produce predictive toxicology models for the community.
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39
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Braga RC, Alves VM, Fraga CAM, Barreiro EJ, de Oliveira V, Andrade CH. Combination of docking, molecular dynamics and quantum mechanical calculations for metabolism prediction of 3,4-methylenedioxybenzoyl-2-thienylhydrazone. J Mol Model 2011; 18:2065-78. [DOI: 10.1007/s00894-011-1219-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Accepted: 08/09/2011] [Indexed: 11/29/2022]
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40
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Moors SLC, Vos AM, Cummings MD, Van Vlijmen H, Ceulemans A. Structure-Based Site of Metabolism Prediction for Cytochrome P450 2D6. J Med Chem 2011; 54:6098-105. [DOI: 10.1021/jm2006468] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Samuel L. C. Moors
- Department of Chemistry, Katholieke Universiteit Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium
| | - Ann M. Vos
- Tibotec BVBA, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Maxwell D. Cummings
- Johnson & Johnson Pharmaceutical Research & Development, Welsh and McKean Roads, Springhouse, Pennsylvania 19477, United States
| | | | - Arnout Ceulemans
- Department of Chemistry, Katholieke Universiteit Leuven, Celestijnenlaan 200F, 3001 Heverlee, Belgium
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Abstract
In silico toxicology in its broadest sense means “anything that we can do with a computer in toxicology.” Many different types of in silico methods have been developed to characterize and predict toxic outcomes in humans and environment. The term non-testing methods denote grouping approaches, structure–activity relationship, and expert systems. These methods are already used for regulatory purposes and it is anticipated that their role will be much more prominent in the near future. This Perspective will delineate the basic principles of non-testing methods and evaluate their role in current and future risk assessment of chemical compounds.
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Affiliation(s)
- Hannu Raunio
- Faculty of Health Sciences, University of Eastern Finland Kuopio, Finland
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42
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Danielson ML, Desai PV, Mohutsky MA, Wrighton SA, Lill MA. 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; 46:3953-63. [PMID: 21703735 DOI: 10.1016/j.ejmech.2011.05.067] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2011] [Revised: 05/24/2011] [Accepted: 05/26/2011] [Indexed: 10/18/2022]
Abstract
Cytochrome P450 enzymes are responsible for metabolizing many endogenous and xenobiotic molecules encountered by the human body. It has been estimated that 75% of all drugs are metabolized by cytochrome P450 enzymes. Thus, predicting a compound's potential sites of metabolism (SOM) is highly advantageous early in the drug development process. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity-accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand-binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. The results reported here indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation (ensemble: 96%, static: 85%) but only leads to a slight improvement (49% vs. 44%) in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. Using ensemble docking, the reactivity-accessibility model accurately predicts SOM in the top-1 ranked position for 49% of the ligand library and considering the top-3 predicted sites increases the prediction success rate to approximately 70% of the ligand library. Further classifying the substrate library according to K(m) values leads to an improvement in SOM prediction for substrates with low K(m) values (57% at top-1). While the current predictive power of the reactivity-accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein-ligand interactions and guide structural modifications of the ligand to increase its metabolic stability.
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Affiliation(s)
- Matthew L Danielson
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, 575 Stadium Mall Drive, West Lafayette, IN 47907, USA
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43
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Andrade CH, Freitas LMD, Oliveira VD. Twenty-six years of HIV science: an overview of anti-HIV drugs metabolism. BRAZ J PHARM SCI 2011. [DOI: 10.1590/s1984-82502011000200003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
From the identification of HIV as the agent causing AIDS, to the development of effective antiretroviral drugs, the scientific achievements in HIV research over the past twenty-six years have been formidable. Currently, there are twenty-five anti-HIV compounds which have been formally approved for clinical use in the treatment of AIDS. These compounds fall into six categories: nucleoside reverse transcriptase inhibitors (NRTIs), nucleotide reverse transcriptase inhibitors (NtRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), cell entry inhibitors or fusion inhibitors (FIs), co-receptor inhibitors (CRIs), and integrase inhibitors (INIs). Metabolism by the host organism is one of the most important determinants of the pharmacokinetic profile of a drug. Formation of active or toxic metabolites will also have an impact on the pharmacological and toxicological outcomes. Therefore, it is widely recognized that metabolism studies of a new chemical entity need to be addressed early in the drug discovery process. This paper describes an overview of the metabolism of currently available anti-HIV drugs.
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44
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Stoll F, Göller AH, Hillisch A. Utility of protein structures in overcoming ADMET-related issues of drug-like compounds. Drug Discov Today 2011; 16:530-8. [PMID: 21554979 DOI: 10.1016/j.drudis.2011.04.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2010] [Revised: 03/01/2011] [Accepted: 04/08/2011] [Indexed: 01/28/2023]
Abstract
The number of solved X-ray structures of proteins relevant for ADMET processes of drug molecules has increased remarkably over recent years. In principle, this development offers the possibility to complement the quantitative structure-property relationship (QSPR)-dominated repertoire of in silico ADMET methods with protein-structure-based approaches. However, the complex nature and the weak nonspecific ligand-binding properties of ADMET proteins take structural biology methods and current docking programs to the limit. In this review we discuss the utility of protein-structure-based design and docking approaches aimed at overcoming issues related to plasma protein binding, active transport via P-glycoprotein, hERG channel mediated cardiotoxicity and cytochrome P450 inhibition, metabolism and induction.
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Affiliation(s)
- Friederike Stoll
- Bayer HealthCare AG, Global Drug Discovery, Medicinal Chemistry, Wuppertal, Germany.
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45
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Flexibility of human cytochrome P450 enzymes: Molecular dynamics and spectroscopy reveal important function-related variations. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2011; 1814:58-68. [DOI: 10.1016/j.bbapap.2010.07.017] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2010] [Revised: 07/11/2010] [Accepted: 07/14/2010] [Indexed: 11/18/2022]
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46
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Vasanthanathan P, Lastdrager J, Oostenbrink C, Commandeur JNM, Vermeulen NPE, Jørgensen FS, Olsen L. Identification of CYP1A2 ligands by structure-based and ligand-based virtual screening. MEDCHEMCOMM 2011. [DOI: 10.1039/c1md00087j] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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47
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Stjernschantz E, Oostenbrink C. Improved ligand-protein binding affinity predictions using multiple binding modes. Biophys J 2010; 98:2682-91. [PMID: 20513413 DOI: 10.1016/j.bpj.2010.02.034] [Citation(s) in RCA: 95] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2009] [Revised: 01/29/2010] [Accepted: 02/17/2010] [Indexed: 11/16/2022] Open
Abstract
Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.
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Affiliation(s)
- Eva Stjernschantz
- Leiden/Amsterdam Center for Drug Research, Division of Molecular Toxicology, Vrije Universiteit, Amsterdam, The Netherlands
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48
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Wu G, Vashishtha SC, Erve JCL. Characterization of Glutathione Conjugates of Duloxetine by Mass Spectrometry and Evaluation of in Silico Approaches to Rationalize the Site of Conjugation for Thiophene Containing Drugs. Chem Res Toxicol 2010; 23:1393-404. [DOI: 10.1021/tx100141d] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Guosheng Wu
- Vitae Pharmaceuticals, 502 West Office Center Drive, Fort Washington, Pennsylvania 19034, and Pharmacokinetics Dynamics and Metabolism, Pfizer, 500 Arcola Road, Collegeville, Pennsylvania 19426
| | - Sarvesh C. Vashishtha
- Vitae Pharmaceuticals, 502 West Office Center Drive, Fort Washington, Pennsylvania 19034, and Pharmacokinetics Dynamics and Metabolism, Pfizer, 500 Arcola Road, Collegeville, Pennsylvania 19426
| | - John C. L. Erve
- Vitae Pharmaceuticals, 502 West Office Center Drive, Fort Washington, Pennsylvania 19034, and Pharmacokinetics Dynamics and Metabolism, Pfizer, 500 Arcola Road, Collegeville, Pennsylvania 19426
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49
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Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0. J Comput Aided Mol Des 2010; 24:771-87. [DOI: 10.1007/s10822-010-9373-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2010] [Accepted: 06/28/2010] [Indexed: 11/26/2022]
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Rydberg P, Gloriam DE, 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. [PMID: 24936230 DOI: 10.1021/ml100016x] [Citation(s) in RCA: 192] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2010] [Accepted: 03/04/2010] [Indexed: 11/29/2022] Open
Abstract
SMARTCyp is an in silico method that predicts the sites of cytochrome P450-mediated metabolism of druglike molecules. The method is foremost a reactivity model, and as such, it shows a preference for predicting sites that are metabolized by the cytochrome P450 3A4 isoform. SMARTCyp predicts the site of metabolism directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures. This is a major advantage, because it makes SMARTCyp very fast. Other advantages are that experimental data are not a prerequisite to create the model, and it can easily be integrated with other methods to create models for other cytochrome P450 isoforms. Benchmarking tests on a database of 394 3A4 substrates show that SMARTCyp successfully identifies at least one metabolic site in the top two ranked positions 76% of the time. SMARTCyp is available for download at http://www.farma.ku.dk/p450.
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Affiliation(s)
- Patrik Rydberg
- Biostructural Research, Department of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark
| | - David E. Gloriam
- Biostructural Research, Department of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark
| | - Jed Zaretzki
- Center for Biotechnology and Interdisciplinary Studies, Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - Curt Breneman
- Center for Biotechnology and Interdisciplinary Studies, Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - Lars Olsen
- Biostructural Research, Department of Medicinal Chemistry, Faculty of Pharmaceutical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark
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