Literature DB >> 26202430

Development of 3D-QSAR Model for Acetylcholinesterase Inhibitors Using a Combination of Fingerprint, Molecular Docking, and Structure-Based Pharmacophore Approaches.

Sehan Lee1, Mace G Barron2.   

Abstract

Acetylcholinesterase (AChE), a serine hydrolase vital for regulating the neurotransmitter acetylcholine in animals, has been used as a target for drugs and pesticides. With the increasing availability of AChE crystal structures, with or without ligands bound, structure-based approaches have been successfully applied to AChE inhibitors (AChEIs). The major limitation of these approaches has been the small applicability domain due to the lack of structural diversity in the training set. In this study, we developed a 3 dimensional quantitative structure-activity relationship (3D-QSAR) for inhibitory activity of 89 reversible and irreversible AChEIs including drugs and insecticides. A 3D-fingerprint descriptor encoding protein-ligand interactions was developed using molecular docking and structure-based pharmacophore to rationalize the structural requirements responsible for the activity of these compounds. The obtained 3D-QSAR model exhibited high correlation value (R(2) = 0.93) and low mean absolute error (MAE = 0.32 log units) for the training set (n = 63). The model was predictive across a range of structures as shown by the leave-one-out cross-validated correlation coefficient (Q(2) = 0.89) and external validation results (n = 26, R(2) = 0.89, and MAE = 0.38 log units). The model revealed that the compounds with high inhibition potency had proper conformation in the active site gorge and interacted with key amino acid residues, in particular Trp84 and Phe330 at the catalytic anionic site, Trp279 at the peripheral anionic site, and Gly118, Gly119, and Ala201 at the oxyanion hole. The resulting universal 3D-QSAR model provides insight into the multiple molecular interactions determining AChEI potency that may guide future chemical design and regulation of toxic AChEIs. Published by Oxford University Press on behalf of the Society of Toxicology 2015. This work is written by US Government employees and is in the public domain in the US.

Entities:  

Keywords:  3D-QSAR; 3D-fingerprint; AChE; molecular docking; structure-based pharmacophore

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Year:  2015        PMID: 26202430     DOI: 10.1093/toxsci/kfv160

Source DB:  PubMed          Journal:  Toxicol Sci        ISSN: 1096-0929            Impact factor:   4.849


  8 in total

1.  A mechanism-based 3D-QSAR approach for classification and prediction of acetylcholinesterase inhibitory potency of organophosphate and carbamate analogs.

Authors:  Sehan Lee; Mace G Barron
Journal:  J Comput Aided Mol Des       Date:  2016-04-07       Impact factor: 3.686

2.  2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors.

Authors:  Bing Niu; Manman Zhao; Qiang Su; Mengying Zhang; Wei Lv; Qin Chen; Fuxue Chen; Dechang Chu; Dongshu Du; Yuhui Zhang
Journal:  Mol Divers       Date:  2017-03-09       Impact factor: 2.943

3.  In Silico Site-Directed Mutagenesis Informs Species-Specific Predictions of Chemical Susceptibility Derived From the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Tool.

Authors:  Jon A Doering; Sehan Lee; Kurt Kristiansen; Linn Evenseth; Mace G Barron; Ingebrigt Sylte; Carlie A LaLone
Journal:  Toxicol Sci       Date:  2018-11-01       Impact factor: 4.849

4.  Simultaneous Inhibitory Effects of All-Trans Astaxanthin on Acetylcholinesterase and Oxidative Stress.

Authors:  Xin Wang; Tao Zhang; Xiaochen Chen; Yating Xu; Zhipeng Li; Yuanfan Yang; Xiping Du; Zedong Jiang; Hui Ni
Journal:  Mar Drugs       Date:  2022-03-31       Impact factor: 6.085

5.  Structure-Based Understanding of Binding Affinity and Mode of Estrogen Receptor α Agonists and Antagonists.

Authors:  Sehan Lee; Mace G Barron
Journal:  PLoS One       Date:  2017-01-06       Impact factor: 3.240

6.  2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors.

Authors:  Manman Zhao; Lin Wang; Linfeng Zheng; Mengying Zhang; Chun Qiu; Yuhui Zhang; Dongshu Du; Bing Niu
Journal:  Biomed Res Int       Date:  2017-05-29       Impact factor: 3.411

7.  Potential Acetylcholinesterase Inhibitor Acting on the Pesticide Resistant and Susceptible Cotton Pests.

Authors:  Seethalakshmi Sakthivel; Habeeb Shaik Mohideen; Chandrasekar Raman; Saharuddin Bin Mohamad
Journal:  ACS Omega       Date:  2022-06-07

8.  3D-QSAR study of steroidal and azaheterocyclic human aromatase inhibitors using quantitative profile of protein-ligand interactions.

Authors:  Sehan Lee; Mace G Barron
Journal:  J Cheminform       Date:  2018-01-18       Impact factor: 5.514

  8 in total

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