Virtual screening of GPCRs: An in silico chemogenomics approach

Jacob, Laurent; Hoffmann, Brice; Stoven, Véronique; Vert, Jean-Philippe

doi:10.1186/1471-2105-9-363

Cited by 97 publications

(112 citation statements)

References 73 publications

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“…The results reported here are comparable to those from other similar works [19][20][21][22] which showed that GPCR modeling [23][24][25][26][27][28][29][30][31] in the absence of a crystal structure can be a valid replacement [32][33][34][35][36][37][38][39] for structural and functional exploration of GPCR receptors, and for the discovery [21,[40][41][42][43], VS [44][45][46][47][48][49][50][51][52] and optimisation [23,53] of their ligands.…”

Section: Introductionsupporting

confidence: 90%

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

Àlex¹,

Heifetz²,

Mazanetz³

et al. 2013

Med chem

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G-Protein Coupled Receptors (GPCRs) have enormous physiological and biomedical importance, being the primary target of a large number of modern drugs. The availability of structural information of the binding site of the targeted GPCR plays a key role in rationalization, efficiency and cost-effectiveness of the drug discovery process. However, obtaining structural information on GPCRs using X-ray crystallography or NMR requires a large investment of time and is technically very challenging. This situation significantly limits the ability of these methods to have an impact in drug discovery for GPCR targets in the short term and hence there is an urgent need for other effective and cost-efficient alternatives. We present here a practical approach that integrates GPCR modelling with fragment based screening to provide structural insights on the H3 and H4 histamine receptor binding sites. This approach creates a cost-efficient new avenue for structure-based drug design (SBDD) against GPCR targets. We report here a success of using this protocol for the discovery of selective and dual H3 and H4 antagonists. Our fragment screen yielded 44 H3, 21 H4 selective and 20 dual fragment hits. These fragments were used to construct high-quality H3 and H4 models followed by binding site exploration and structure based virtual screening (VS). Overall, 172 compounds were purchased for testing based on the virtual screening results. Of the 74 compounds predicted to have dual activity, 33 had activity against one or other of the two receptors (44%), of which 17 had activity against both. Of the 19 compounds predicted to be H3 selective, 13 were active against H3 (68%) and 10 of these also had selectivity over H4. Of the 79 compounds predicted to be H4 selective, 36 were active against H4 (45%) and 2 of these also had selectivity over H3.

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Section: Introductionsupporting

confidence: 90%

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

Àlex¹,

Heifetz²,

Mazanetz³

et al. 2013

Med chem

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“…The goal is to be able to use the known protein-chemical interactions for a given protein family to help predict which chemicals the proteins of another protein family will interact with, for which no interaction information is known. We obtained a data set from Jacob et al [22] which includes all chemicals and their G protein-coupled receptor (GPCR) targets, built from an exhaustive search of the GPCR ligand database GLIDA [27]. The data set contains 80 GPCR proteins across 5 protein families, 2687 compounds, and a total of 4051 protein-chemical interactions.…”

Section: Protein-chemical Interaction (Data Sets 13 -24)mentioning

confidence: 99%

Large margin transductive transfer learning

Quanz

Huan

2009

Proceedings of the 18th ACM Conference on Information and Knowledge Management

105

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Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributions is relaxed. We specifically address the problem of transductive transfer learning in which we have access to labeled training data and unlabeled testing data potentially drawn from different, yet related distributions, and the goal is to leverage the labeled training data to learn a classifier to correctly predict data from the testing distribution. We have derived efficient algorithms for transductive transfer learning based on a novel viewpoint and the Support Vector Machine (SVM) paradigm, of a large margin hyperplane classifier in a feature space. We show that our method can out-perform some recent state-of-the-art approaches for transfer learning on several data sets, with the added benefits of model and data separation and the potential to leverage existing work on support vector machines.

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“…CPI-specific kernel methods based on the kernel trick were recently formulated by Jacob, Vert, and co-workers. [33,34] The result of their work that is important to discussion here is that they showed for CPIs (c 1 , p 1 ) and (c 2 , p 2 ),…”

Section: Cpi Similarity Metrics and Examplesmentioning

confidence: 88%

CompoundProtein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions

Brown¹,

Niijima²,

Okuno³

2013

Molecular Informatics

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With advancements in high-throughput technologies and open availability of bioassay data, computational methods to generate models, that zoom out from a single protein with a focused ligand set to a larger and more comprehensive description of compound-protein interactions and furthermore demonstrate subsequent translational validity in prospective experiments, are of prime importance. In this article, we discuss some of the new benefits and challenges of the emerging computational chemogenomics paradigm, particularly with respect to compound-protein interaction. Examples of experimentally validated computational predictions and recent trends in molecular feature extraction are presented. In addition, analyses of cross-family interactions are considered. We also discuss the expected role of computational chemogenomics in contributing to increasingly expansive network-level modeling and screening projects.

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Virtual screening of GPCRs: An in silico chemogenomics approach

Cited by 97 publications

References 73 publications

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

Large margin transductive transfer learning

CompoundProtein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions

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