Homology Modeling and Docking Evaluation of Human Muscarinic Acetylcholine Receptors

Thomas, Trayder; Chalmers, David K.; Yuriev, Elizabeth

doi:10.1007/978-1-4939-2858-3_2

Cited by 2 publications

(3 citation statements)

References 75 publications

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“…Over the past few years, there has been increasing interest in characterizing ligand binding pathways for a number of reasons; first, knowledge of the binding pathway is the key to a detailed understanding of ligand binding kinetics, and second, it has become apparent that intermediate states in the pathway of a ligand may themselves be additional binding sites that can be exploited in drug development . Our group is particularly interested in ligand binding to G protein-coupled receptors (GPCRs), − and this study explores ligand binding pathways in the pharmaceutically important dopamine D 2 and D 3 receptors (D 2 R, D 3 R) using molecular dynamics (MD) simulations.…”

Section: Introductionmentioning

confidence: 99%

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D₂ and D₃ Receptors

Thomas

Fang

Yuriev

et al. 2016

J. Chem. Inf. Model.

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The binding of a small molecule ligand to its protein target is most often characterized by binding affinity and is typically viewed as an on/off switch. The more complex reality is that binding involves the ligand passing through a series of intermediate states between the solution phase and the fully bound pose. We have performed a set of 29 unbiased molecular dynamics simulations to model the binding pathways of the dopamine receptor antagonists clozapine and haloperidol binding to the D2 and D3 dopamine receptors. Through these simulations we have captured the binding pathways of clozapine and haloperidol from the extracellular vestibule to the orthosteric binding site and thereby, we also predict the bound pose of each ligand. These are the first long time scale simulations of haloperidol or clozapine binding to dopamine receptors. From these simulations, we have identified several important stages in the binding pathway, including the involvement of Tyr7.35 in a "handover" mechanism that transfers the ligand between the extracellular vestibule and Asp3.32. We have also performed interaction and cluster analyses to determine differences in binding pathways between the D2 and D3 receptors and identified metastable states that may be of use in drug design.

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Section: Introductionmentioning

confidence: 99%

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D₂ and D₃ Receptors

Thomas

Fang

Yuriev

et al. 2016

J. Chem. Inf. Model.

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“…59 Also, LogAUC, an aggregate metric that gives early enrichment more weight, is analogous to AUC but weights the ROC curve by taking the semilog of the x-axis. 60,61 The adjusted LogAUC 0.001 was employed and was simply defined as LogAUC. BEDROC is adapted to the early recognition problem, which can modulate the weight of the top-ranked compounds by using a parameter α.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…EF is a popular early recognition metric that describes the enrichment of known active compounds within the top-scored compounds compared to those of known active compounds throughout the entire database . Also, LogAUC, an aggregate metric that gives early enrichment more weight, is analogous to AUC but weights the ROC curve by taking the semilog of the x -axis. , The adjusted LogAUC 0.001 was employed and was simply defined as LogAUC. BEDROC is adapted to the early recognition problem, which can modulate the weight of the top-ranked compounds by using a parameter α. − Parameter α represents the degree of “early recognition”, which was set to 80.5 in this study.…”

Section: Methodsmentioning

confidence: 99%

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring

Shen

Xiong

et al. 2020

J. Chem. Inf. Model.

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Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving novel hit compounds in drug discovery. However, the accuracy of the current docking scoring function (SF) is usually insufficient. In this study, in order to improve the screening power of SF, a novel approach named EAT-Score was proposed by directly utilizing the energy auxiliary terms (EAT) provided by molecular docking scoring through eXtreme Gradient Boosting (XGBoost). Here, EAT specifically refers to the output of the Molecular Operating Environment (MOE) scoring, including the energy scores of five different classical SFs and the Protein–Ligand Interaction Fingerprint (PLIF) terms. The performance of EAT-Score to discriminate actives from decoys was strictly validated on the DUD-E diverse subset by using different performance metrics. The results showed that EAT-Score performed much better than classical SFs in VS, with its AUC values exhibiting an improvement of around 0.3. Meanwhile, EAT-Score could achieve comparable even better prediction performance compared with other state-of-the-art VS methods, such as some machine learning (ML)-based SFs and classical SFs implemented in docking programs, in terms of AUC, LogAUC, or BEDROC. Furthermore, the EAT-Score model can capture important binding pattern information from protein–ligand complexes by Shapley additive explanations (SHAP) analysis, which may be very helpful in interpreting the ligand binding mechanism for a certain target and thereby guiding drug design.

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Homology Modeling and Docking Evaluation of Human Muscarinic Acetylcholine Receptors

Cited by 2 publications

References 75 publications

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D₂ and D₃ Receptors

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D₂ and D₃ Receptors

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring

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Homology Modeling and Docking Evaluation of Human Muscarinic Acetylcholine Receptors

Cited by 2 publications

References 75 publications

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D2 and D3 Receptors

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D2 and D3 Receptors

Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring

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Product

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Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D₂ and D₃ Receptors

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D₂ and D₃ Receptors