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Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1.
Molecules. 2019 Jul 29; 24(15)M

Abstract

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.

Authors+Show Affiliations

INSERM U1133, CNRS UMR 8251, Unit of functional and adaptive biology, Université de Paris, Paris 75013, France.Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark.Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark.Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Copenhagen University Hospital, 4000 Roskilde, Denmark. Department of Science and Environment, Roskilde University, 4000 Roskilde, Denmark.Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen N, Denmark.INSERM U1133, CNRS UMR 8251, Unit of functional and adaptive biology, Université de Paris, Paris 75013, France. Olivier.taboureau@univ-paris-diderot.fr.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31362390

Citation

Briand, Eliane, et al. "Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents With Potential Inhibitory Effect On Human CES1." Molecules (Basel, Switzerland), vol. 24, no. 15, 2019.
Briand E, Thomsen R, Linnet K, et al. Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1. Molecules. 2019;24(15).
Briand, E., Thomsen, R., Linnet, K., Rasmussen, H. B., Brunak, S., & Taboureau, O. (2019). Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1. Molecules (Basel, Switzerland), 24(15). https://doi.org/10.3390/molecules24152747
Briand E, et al. Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents With Potential Inhibitory Effect On Human CES1. Molecules. 2019 Jul 29;24(15) PubMed PMID: 31362390.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1. AU - Briand,Eliane, AU - Thomsen,Ragnar, AU - Linnet,Kristian, AU - Rasmussen,Henrik Berg, AU - Brunak,Søren, AU - Taboureau,Olivier, Y1 - 2019/07/29/ PY - 2019/06/10/received PY - 2019/07/11/revised PY - 2019/07/24/accepted PY - 2019/8/1/entrez PY - 2019/8/1/pubmed PY - 2019/12/31/medline KW - CES1 inhibitors KW - adverse drug reactions KW - carboxylesterase 1 KW - docking KW - ensemble docking KW - machine learning KW - metabolism JF - Molecules (Basel, Switzerland) JO - Molecules VL - 24 IS - 15 N2 - 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. SN - 1420-3049 UR - https://www.unboundmedicine.com/medline/citation/31362390/Combined_Ensemble_Docking_and_Machine_Learning_in_Identification_of_Therapeutic_Agents_with_Potential_Inhibitory_Effect_on_Human_CES1 L2 - http://www.mdpi.com/resolver?pii=molecules24152747 DB - PRIME DP - Unbound Medicine ER -