Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1
The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactio...
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doaj-7affc93356584c2db179bea78e0154662020-11-24T21:21:38ZengMDPI AGMolecules1420-30492019-07-012415274710.3390/molecules24152747molecules24152747Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1Eliane Briand0Ragnar Thomsen1Kristian Linnet2Henrik Berg Rasmussen3Søren Brunak4Olivier Taboureau5INSERM U1133, CNRS UMR 8251, Unit of functional and adaptive biology, Université de Paris, Paris 75013, FranceDepartment of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, DenmarkDepartment of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, DenmarkInstitute of Biological Psychiatry, Mental Health Centre Sct. Hans, Copenhagen University Hospital, 4000 Roskilde, DenmarkNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, DenmarkINSERM U1133, CNRS UMR 8251, Unit of functional and adaptive biology, Université de Paris, Paris 75013, FranceThe human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.https://www.mdpi.com/1420-3049/24/15/2747carboxylesterase 1dockingensemble dockingmachine learningCES1 inhibitorsadverse drug reactionsmetabolism |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Eliane Briand Ragnar Thomsen Kristian Linnet Henrik Berg Rasmussen Søren Brunak Olivier Taboureau |
spellingShingle |
Eliane Briand Ragnar Thomsen Kristian Linnet Henrik Berg Rasmussen Søren Brunak Olivier Taboureau Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1 Molecules carboxylesterase 1 docking ensemble docking machine learning CES1 inhibitors adverse drug reactions metabolism |
author_facet |
Eliane Briand Ragnar Thomsen Kristian Linnet Henrik Berg Rasmussen Søren Brunak Olivier Taboureau |
author_sort |
Eliane Briand |
title |
Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1 |
title_short |
Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1 |
title_full |
Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1 |
title_fullStr |
Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1 |
title_full_unstemmed |
Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1 |
title_sort |
combined ensemble docking and machine learning in identification of therapeutic agents with potential inhibitory effect on human ces1 |
publisher |
MDPI AG |
series |
Molecules |
issn |
1420-3049 |
publishDate |
2019-07-01 |
description |
The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure. |
topic |
carboxylesterase 1 docking ensemble docking machine learning CES1 inhibitors adverse drug reactions metabolism |
url |
https://www.mdpi.com/1420-3049/24/15/2747 |
work_keys_str_mv |
AT elianebriand combinedensembledockingandmachinelearninginidentificationoftherapeuticagentswithpotentialinhibitoryeffectonhumances1 AT ragnarthomsen combinedensembledockingandmachinelearninginidentificationoftherapeuticagentswithpotentialinhibitoryeffectonhumances1 AT kristianlinnet combinedensembledockingandmachinelearninginidentificationoftherapeuticagentswithpotentialinhibitoryeffectonhumances1 AT henrikbergrasmussen combinedensembledockingandmachinelearninginidentificationoftherapeuticagentswithpotentialinhibitoryeffectonhumances1 AT sørenbrunak combinedensembledockingandmachinelearninginidentificationoftherapeuticagentswithpotentialinhibitoryeffectonhumances1 AT oliviertaboureau combinedensembledockingandmachinelearninginidentificationoftherapeuticagentswithpotentialinhibitoryeffectonhumances1 |
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1725998821347950592 |