Dataset Information

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The ?2 Adrenergic Receptor as a Case Study.

ABSTRACT: How accurate do structures of the ?2 adrenergic receptor (?2AR) need to be to effectively serve as platforms for docking-based virtual screening campaigns? To answer this research question, here, we targeted through controlled virtual screening experiments 23 homology models of the ?2AR endowed with different levels of structural accuracy. Subsequently, we studied the correlation between virtual screening performance and structural accuracy of the targeted models. Moreover, we studied the correlation between virtual screening performance and template/target receptor sequence identity. Our study demonstrates that docking-based virtual screening campaigns targeting homology models of the ?2AR, in the majority of the cases, yielded results that exceeded random expectations in terms of area under the receiver operating characteristic curve (ROC AUC). Moreover, with the most effective scoring method, over one-third and one-quarter of the models yielded results that exceeded random expectation also in terms of enrichment factors (EF1, EF5, and EF10) and BEDROC (? = 160.9), respectively. Not surprisingly, we found a detectable linear correlation between virtual screening performance and structural accuracy of the ligand-binding cavity. We also found a detectable linear correlation between virtual screening performance and structural accuracy of the second extracellular loop (EL2). Finally, our data indicate that, although there is no detectable linear correlation between virtual screening performance and template/?2AR sequence identity, models built on the basis of templates that show high sequence identity with the ?2AR, especially within the ligand-biding cavity, performed consistently well. Conversely, models with lower sequence identity displayed performance levels that ranged from very good to random, with no apparent correlation with the sequence identity itself.

SUBMITTER: Costanzi S

PROVIDER: S-EPMC6800073 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study.

Costanzi Stefano S Cohen Austin A Danfora Abigail A Dolatmoradi Marjan M

Journal of chemical information and modeling 20190701 7

How accurate do structures of the β2 adrenergic receptor (β2AR) need to be to effectively serve as platforms for docking-based virtual screening campaigns? To answer this research question, here, we targeted through controlled virtual screening experiments 23 homology models of the β2AR endowed with different levels of structural accuracy. Subsequently, we studied the correlation between virtual screening performance and structural accuracy of the targeted models ...[more]

PMID: 31257873

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Project description:The transient receptor potential vanilloid type 1 (TRPV1) is a heat-activated cation channel protein, which contributes to inflammation, acute and persistent pain. Antagonists of human TRPV1 (hTRPV1) represent a novel therapeutic approach for the treatment of pain. Developing various antagonists of hTRPV1, however, has been hindered by the unavailability of a 3D structure of hTRPV1. Recently, the 3D structures of rat TRPV1 (rTRPV1) in the presence and absence of ligand have been reported as determined by cryo-EM. rTRPV1 shares 85.7% sequence identity with hTRPV1. In the present work, we constructed and reported the 3D homology tetramer model of hTRPV1 based on the cryo-EM structures of rTRPV1. Molecular dynamics (MD) simulations, energy minimizations, and prescreen were applied to select and validate the best model of hTRPV1. The predicted binding pocket of hTRPV1 consists of two adjacent monomers subunits, which were congruent with the experimental rTRPV1 data and the cyro-EM structures of rTRPV1. The detailed interactions between hTRPV1 and its antagonists or agonists were characterized by molecular docking, which helped us to identify the important residues. Conformational changes of hTRPV1 upon antagonist/agonist binding were also explored by MD simulation. The different movements of compounds led to the different conformational changes of monomers in hTRPV1, indicating that TRPV1 works in a concerted way, resembling some other channel proteins such as aquaporins. We observed that the selective filter was open when hTRPV1 bound with an agonist during MD simulation. For the lower gate of hTRPV1, we observed large similarities between hTRPV1 bound with antagonist and with agonist. A five-point pharmacophore model based on several antagonists was established, and the structural model was used to screen in silico for new antagonists for hTRPV1. By using the 3D TRPV1 structural model above, the pilot in silico screening has begun to yield promising hits with activity as hTRPV1 antagonists, several of which showed substantial potency.

Project description:BackgroundThe virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model.ResultsWe evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening.ConclusionThe proposed applicability domain formulations for kernel-based QSAR models can successfully identify compounds for which no reliable predictions can be expected from the model. The resulting reduction of the search space and the elimination of some of the active compounds should not be considered as a drawback, because the results indicate that, in most cases, these omitted ligands would not be found by the model anyway.

Dataset Information

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The ?2 Adrenergic Receptor as a Case Study.

Publications

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β<sub>2</sub> Adrenergic Receptor as a Case Study.

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