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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] [What about the content of this article? (0)] [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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2
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Capoferri L, van Dijk M, Rustenburg AS, Wassenaar TA, Kooi DP, Rifai EA, Vermeulen NPE, Geerke DP. eTOX ALLIES: an automated pipeLine for linear interaction energy-based simulations. J Cheminform 2017; 9:58. [PMID: 29159598 PMCID: PMC5696310 DOI: 10.1186/s13321-017-0243-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 11/01/2017] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Computational methods to predict binding affinities of small ligands toward relevant biological (off-)targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up the drug discovery process. However, use of ligand-based approaches can lead to erroneous predictions when structural and dynamic features of the target substantially affect ligand binding. Free energy methods for affinity computation can include steric and electrostatic protein-ligand interactions, solvent effects, and thermal fluctuations, but often they are computationally demanding and require a high level of supervision. As a result their application is typically limited to the screening of small sets of compounds by experts in molecular modeling. RESULTS We have developed eTOX ALLIES, an open source framework that allows the automated prediction of ligand-binding free energies requiring the ligand structure as only input. eTOX ALLIES is based on the linear interaction energy approach, an efficient end-point free energy method derived from Free Energy Perturbation theory. Upon submission of a ligand or dataset of compounds, the tool performs the multiple steps required for binding free-energy prediction (docking, ligand topology creation, molecular dynamics simulations, data analysis), making use of external open source software where necessary. Moreover, functionalities are also available to enable and assist the creation and calibration of new models. In addition, a web graphical user interface has been developed to allow use of free-energy based models to users that are not an expert in molecular modeling. CONCLUSIONS Because of the user-friendliness, efficiency and free-software licensing, eTOX ALLIES represents a novel extension of the toolbox for computational chemists, pharmaceutical scientists and toxicologists, who are interested in fast affinity predictions of small molecules toward biological (off-)targets for which protein flexibility, solvent and binding site interactions directly affect the strength of ligand-protein binding.
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Affiliation(s)
- Luigi Capoferri
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - 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
| | - Ariën S. Rustenburg
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
- Present Address: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065 USA
| | - Tsjerk A. Wassenaar
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
- Present Address: Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, 9747 AG Groningen, The Netherlands
| | - Derk P. Kooi
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Eko A. Rifai
- 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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3
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Russo S, Callegari D, Incerti M, Pala D, Giorgio C, Brunetti J, Bracci L, Vicini P, Barocelli E, Capoferri L, Rivara S, Tognolini M, Mor M, Lodola A. Exploiting Free-Energy Minima to Design Novel EphA2 Protein-Protein Antagonists: From Simulation to Experiment and Return. Chemistry 2016; 22:8048-52. [DOI: 10.1002/chem.201600993] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Indexed: 01/09/2023]
Affiliation(s)
- Simonetta Russo
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Donatella Callegari
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Matteo Incerti
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Daniele Pala
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Carmine Giorgio
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Jlenia Brunetti
- Dipartimento di Biotecnologie Mediche; Università degli Studi di Siena; Via Fiorentina 1 53100 Siena Italy
| | - Luisa Bracci
- Dipartimento di Biotecnologie Mediche; Università degli Studi di Siena; Via Fiorentina 1 53100 Siena Italy
| | - Paola Vicini
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Elisabetta Barocelli
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Luigi Capoferri
- Department of Chemistry and Pharmaceutical Sciences; VU University; De Boelelaan 1083 1081 HV Amsterdam the Netherlands
| | - Silvia Rivara
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Massimiliano Tognolini
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Marco Mor
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
| | - Alessio Lodola
- Dipartimento di Farmacia; Università degli Studi di Parma; Viale delle Scienze 27A 43124 Parma Italy
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5
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Capoferri L, Leth R, ter Haar E, Mohanty AK, Grootenhuis PDJ, Vottero E, Commandeur JNM, Vermeulen NPE, Jørgensen FS, Olsen L, Geerke DP. Insights into regioselective metabolism of mefenamic acid by cytochrome P450 BM3 mutants through crystallography, docking, molecular dynamics, and free energy calculations. Proteins 2016; 84:383-96. [PMID: 26757175 DOI: 10.1002/prot.24985] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 12/21/2015] [Accepted: 01/05/2016] [Indexed: 12/21/2022]
Abstract
Cytochrome P450 BM3 (CYP102A1) mutant M11 is able to metabolize a wide range of drugs and drug-like compounds. Among these, M11 was recently found to be able to catalyze formation of human metabolites of mefenamic acid and other nonsteroidal anti-inflammatory drugs (NSAIDs). Interestingly, single active-site mutations such as V87I were reported to invert regioselectivity in NSAID hydroxylation. In this work, we combine crystallography and molecular simulation to study the effect of single mutations on binding and regioselective metabolism of mefenamic acid by M11 mutants. The heme domain of the protein mutant M11 was expressed, purified, and crystallized, and its X-ray structure was used as template for modeling. A multistep approach was used that combines molecular docking, molecular dynamics (MD) simulation, and binding free-energy calculations to address protein flexibility. In this way, preferred binding modes that are consistent with oxidation at the experimentally observed sites of metabolism (SOMs) were identified. Whereas docking could not be used to retrospectively predict experimental trends in regioselectivity, we were able to rank binding modes in line with the preferred SOMs of mefenamic acid by M11 and its mutants by including protein flexibility and dynamics in free-energy computation. In addition, we could obtain structural insights into the change in regioselectivity of mefenamic acid hydroxylation due to single active-site mutations. Our findings confirm that use of MD and binding free-energy calculation is useful for studying biocatalysis in those cases in which enzyme binding is a critical event in determining the selective metabolism of a substrate.
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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
| | - Rasmus Leth
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark
| | - Ernst ter Haar
- Vertex Pharmaceuticals Incorporated, 50 Northern Avenue, Boston, MA, 02210
| | - Arun K Mohanty
- Vertex Pharmaceuticals Incorporated, 50 Northern Avenue, Boston, MA, 02210
| | | | - Eduardo Vottero
- 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
| | - 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
| | - Flemming Steen Jørgensen
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark
| | - Lars Olsen
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark
| | - 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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6
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Vosmeer CR, Kooi DP, Capoferri L, Terpstra MM, Vermeulen NPE, Geerke DP. Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions. J Mol Model 2016; 22:31. [PMID: 26757914 PMCID: PMC4710667 DOI: 10.1007/s00894-015-2883-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Accepted: 11/29/2015] [Indexed: 11/24/2022]
Abstract
Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.
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Affiliation(s)
- C Ruben Vosmeer
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081, HV, Amsterdam, The Netherlands
| | - Derk P Kooi
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081, HV, Amsterdam, The Netherlands
| | - Luigi Capoferri
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081, HV, Amsterdam, The Netherlands
| | - Margreet M Terpstra
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081, HV, Amsterdam, The Netherlands
| | - Nico P E Vermeulen
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081, HV, Amsterdam, The Netherlands
| | - Daan P Geerke
- AIMMS Division of Molecular Toxicology, Department of Chemistry and Pharmaceutical Sciences, Faculty of Sciences, VU University Amsterdam, De Boelelaan 1083, 1081, HV, Amsterdam, The Netherlands.
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7
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Sanz F, Carrió P, López O, Capoferri L, Kooi DP, Vermeulen NPE, Geerke DP, Montanari F, Ecker GF, Schwab CH, Kleinöder T, Magdziarz T, Pastor M. Integrative Modeling Strategies for Predicting Drug Toxicities at the eTOX Project. Mol Inform 2015; 34:477-84. [DOI: 10.1002/minf.201400193] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Accepted: 04/01/2015] [Indexed: 11/11/2022]
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9
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Capoferri L, Lodola A, Rivara S, Mor M. Quantum mechanics/molecular mechanics modeling of covalent addition between EGFR-cysteine 797 and N-(4-anilinoquinazolin-6-yl) acrylamide. J Chem Inf Model 2015; 55:589-99. [PMID: 25658136 DOI: 10.1021/ci500720e] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Irreversible epidermal growth factor receptor (EGFR) inhibitors can circumvent resistance to first-generation ATP-competitive inhibitors in the treatment of nonsmall-cell lung cancer. They covalently bind a noncatalytic cysteine (Cys797) at the surface of EGFR active site by an acrylamide warhead. Herein, we used a hybrid quantum mechanics/molecular mechanics (QM/MM) potential in combination with umbrella sampling in the path-collective variable space to investigate the mechanism of alkylation of Cys797 by the prototypical covalent inhibitor N-(4-anilinoquinazolin-6-yl) acrylamide. Calculations show that Cys797 reacts with the acrylamide group of the inhibitor through a direct addition mechanism, with Asp800 acting as a general base/general acid in distinct steps of the reaction. The obtained reaction free energy is negative (ΔA = -12 kcal/mol) consistent with the spontaneous and irreversible alkylation of Cys797 by N-(4-anilinoquinazolin-6-yl) acrylamide. Our calculations identify desolvation of Cys797 thiolate anion as a key step of the alkylation process, indicating that changes in the intrinsic reactivity of the acrylamide would have only a minor impact on the inhibitor potency.
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Affiliation(s)
- Luigi Capoferri
- Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze 27/A, I-43124, Parma, Italy
| | - Alessio Lodola
- Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze 27/A, I-43124, Parma, Italy
| | - Silvia Rivara
- Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze 27/A, I-43124, Parma, Italy
| | - Marco Mor
- Dipartimento di Farmacia, Università degli Studi di Parma, Viale delle Scienze 27/A, I-43124, Parma, Italy
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10
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Lodola A, Capoferri L, Rivara S, Tarzia G, Piomelli D, Mulholland A, Mor M. Quantum mechanics/molecular mechanics modeling of fatty acid amide hydrolase reactivation distinguishes substrate from irreversible covalent inhibitors. J Med Chem 2013; 56:2500-12. [PMID: 23425199 DOI: 10.1021/jm301867x] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Carbamate and urea derivatives are important classes of fatty acid amide hydrolase (FAAH) inhibitors that carbamoylate the active-site nucleophile Ser241. In the present work, the reactivation mechanism of carbamoylated FAAH is investigated by means of a quantum mechanics/molecular mechanics (QM/MM) approach. The potential energy surfaces for decarbamoylation of FAAH covalent adducts, derived from the O-aryl carbamate URB597 and from the N-piperazinylurea JNJ1661610, were calculated and compared to that for deacylation of FAAH acylated by the substrate oleamide. Calculations show that a carbamic group bound to Ser241 prevents efficient stabilization of transition states of hydrolysis, leading to large increments in the activation barrier. Moreover, the energy barrier for the piperazine carboxylate was significantly lower than that for the cyclohexyl carbamate derived from URB597. This is consistent with experimental data showing slowly reversible FAAH inhibition for the N-piperazinylurea inhibitor and irreversible inhibition for URB597.
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Affiliation(s)
- Alessio Lodola
- Dipartimento di Farmacia, Università degli Studi di Parma, I-43124 Parma, Italy
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11
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Lodola A, Branduardi D, De Vivo M, Capoferri L, Mor M, Piomelli D, Cavalli A. A catalytic mechanism for cysteine N-terminal nucleophile hydrolases, as revealed by free energy simulations. PLoS One 2012; 7:e32397. [PMID: 22389698 PMCID: PMC3289653 DOI: 10.1371/journal.pone.0032397] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2011] [Accepted: 01/29/2012] [Indexed: 12/22/2022] Open
Abstract
The N-terminal nucleophile (Ntn) hydrolases are a superfamily of enzymes specialized in the hydrolytic cleavage of amide bonds. Even though several members of this family are emerging as innovative drug targets for cancer, inflammation, and pain, the processes through which they catalyze amide hydrolysis remains poorly understood. In particular, the catalytic reactions of cysteine Ntn-hydrolases have never been investigated from a mechanistic point of view. In the present study, we used free energy simulations in the quantum mechanics/molecular mechanics framework to determine the reaction mechanism of amide hydrolysis catalyzed by the prototypical cysteine Ntn-hydrolase, conjugated bile acid hydrolase (CBAH). The computational analyses, which were confirmed in water and using different CBAH mutants, revealed the existence of a chair-like transition state, which might be one of the specific features of the catalytic cycle of Ntn-hydrolases. Our results offer new insights on Ntn-mediated hydrolysis and suggest possible strategies for the creation of therapeutically useful inhibitors.
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Affiliation(s)
- Alessio Lodola
- Pharmaceutical Department, University of Parma, Parma, Italy
| | - Davide Branduardi
- Drug Discovery and Development, Italian Institute of Technology, Genova, Italy
| | - Marco De Vivo
- Drug Discovery and Development, Italian Institute of Technology, Genova, Italy
| | - Luigi Capoferri
- Pharmaceutical Department, University of Parma, Parma, Italy
| | - Marco Mor
- Pharmaceutical Department, University of Parma, Parma, Italy
| | - Daniele Piomelli
- Drug Discovery and Development, Italian Institute of Technology, Genova, Italy
- Department of Pharmacology, University of California Irvine, Irvine, California, United States of America
| | - Andrea Cavalli
- Drug Discovery and Development, Italian Institute of Technology, Genova, Italy
- Department of Pharmaceutical Sciences, University of Bologna, Bologna, Italy
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12
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Capoferri L, Mor M, Sirirak J, Chudyk E, Mulholland AJ, Lodola A. Application of a SCC-DFTB QM/MM approach to the investigation of the catalytic mechanism of fatty acid amide hydrolase. J Mol Model 2011; 17:2375-83. [PMID: 21365225 DOI: 10.1007/s00894-011-0981-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2010] [Accepted: 01/21/2011] [Indexed: 12/20/2022]
Abstract
Self-consistent charge density functional tight binding (SCC-DFTB) is a promising method for hybrid quantum mechanics/molecular mechanics (QM/MM) simulations of enzyme-catalyzed reactions. The acylation reaction of fatty acid amide hydrolase (FAAH), a promising drug target, was investigated by applying a SCC-DFTB/CHARMM27 scheme. Calculated potential energy barriers resulted in reasonable agreement with experiments for oleamide (OA) and oleoylmethyl ester (OME) substrates, outperforming previous calculations performed at the PM3/CHARMM22 level. Furthermore, the experimental preference of FAAH in hydrolyzing OA faster than OME was adequately reproduced by calculations. All these findings indicate that the SCC-DFTB/CHARMM27 approach can be successfully applied to mechanistic investigations of FAAH-catalyzed reactions.
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Affiliation(s)
- Luigi Capoferri
- Dipartimento Farmaceutico, Università degli Studi di Parma, viale G. P. Usberti 27/A Campus Universitario, 43124, Parma, Italy
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Lodola A, Capoferri L, Rivara S, Chudyk E, Sirirak J, Dyguda-Kazimierowicz E, Andrzej Sokalski W, Mileni M, Tarzia G, Piomelli D, Mor M, Mulholland AJ. Understanding the role of carbamate reactivity in fatty acid amide hydrolase inhibition by QM/MM mechanistic modelling. Chem Commun (Camb) 2011; 47:2517-9. [DOI: 10.1039/c0cc04937a] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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