Ligand channel in pharmacologically stabilized rhodopsin

Mattle, D.; Kuhn, Bernd; Aebi, Johannes D.; Bedoucha, Marc; Kekilli, D.; Grozinger, Nathalie; Alker, André; Rudolph, M.G.; Schmid, Georg; Schertler, Gebhard F. X.; Hennig, Michael; Standfuss, Jörg; Dawson, Roger

doi:10.1073/pnas.1718084115

Cited by 34 publications

(36 citation statements)

References 44 publications

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“…The protocol integrates a set of methods to qualitatively evaluate the impact of detergent and deglycosylation during preparation of the rhodopsin-mini-G o complex. Rhodopsin at inactive state and light-activated state bound with and without the transducin peptide has been crystallized when purified in the detergents octyl glucoside (C8G) 20,21,22 and C9G 23,24 . As the rhodopsin-mini-G o complex purified in C8G and C9G did not yield crystals (data not shown), we then explored a wider range of other detergents using the strategy described (Figure 1).…”

Section: Discussionmentioning

confidence: 99%

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

Pamula

Mühle

Blanc

et al. 2020

JoVE

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The key to determining crystal structures of membrane protein complexes is the quality of the sample prior to crystallization. In particular, the choice of detergent is critical, because it affects both the stability and monodispersity of the complex. We recently determined the crystal structure of an active state of bovine rhodopsin coupled to an engineered G protein, mini-G o , at 3.1 Å resolution. Here, we detail the procedure for optimizing the preparation of the rhodopsin-mini-G o complex. Dark-state rhodopsin was prepared in classical and neopentyl glycol (NPG) detergents, followed by complex formation with mini-G o under light exposure. The stability of the rhodopsin was assessed by ultraviolet-visible (UV-VIS) spectroscopy, which monitors the reconstitution into rhodopsin of the light-sensitive ligand, 9-cis retinal. Automated size-exclusion chromatography (SEC) was used to characterize the monodispersity of rhodopsin and the rhodopsin-mini-G o complex. SDS-polyacrylamide electrophoresis (SDS-PAGE) confirmed the formation of the complex by identifying a 1:1 molar ratio between rhodopsin and mini-G o after staining the gel with Coomassie blue. After cross-validating all this analytical data, we eliminated unsuitable detergents and continued with the best candidate detergent for large-scale preparation and crystallization. An additional problem arose from the heterogeneity of N-glycosylation. Heterologously-expressed rhodopsin was observed on SDS-PAGE to have two different N-glycosylated populations, which would probably have hindered crystallogenesis. Therefore, different deglycosylation enzymes were tested, and endoglycosidase F1 (EndoF1) produced rhodopsin with a single species of N-glycosylation. With this strategic pipeline for characterizing protein quality, preparation of the rhodopsin-mini-G o complex was optimized to deliver the crystal structure. This was only the third crystal structure of a GPCR-G protein signaling complex. This approach can also be generalized for other membrane proteins and their complexes to facilitate sample preparation and structure determination.

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Section: Discussionmentioning

confidence: 99%

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

Pamula

Mühle

Blanc

et al. 2020

JoVE

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“…The predicted interaction sites (residues) of the protein had several overlaps with the true labels, but also with a number of false positives. The example pair ranked around 90% was a ligand binding to rhodopsin (Fig 3d, PDB ID: 6FK7) [37]. The deviation of the predicted interaction sites from true labels in this example was probably due to the scarcity of training data to support these predictions.…”

Section: Performance Evaluation On Pairwise Non-covalent Interaction mentioning

confidence: 96%

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

Wan

Shu

et al. 2019

Preprint

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Computational approaches for inferring the mechanisms of compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, although a number of deep learning based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins through neural attentions, they still lack a systematic evaluation on the interpretability of the identified local features. In addition, in these previous approaches, the exact matchings between interaction sites from compounds and proteins, which are generally important for understanding drug mechanisms of action, still remain unknown. Here, we compiled the first benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs, and used it to systematically evaluate the interpretability of neural attentions in existing prediction models. We developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinity for a given compound-protein pair. MONN uses convolution neural networks on molecular graphs of compounds and primary sequences of proteins to effectively capture the intrinsic features from both inputs, and also takes advantage of the predicted non-covalent interactions to further boost the accuracy of binding affinity prediction. Comprehensive evaluation demonstrated that while the previous neural attention based approaches fail to exhibit satisfactory interpretability results without extra supervision, MONN can successfully predict non-covalent interactions on our benchmark dataset as well as another independent dataset derived from the Protein Data Bank (PDB). Moreover, MONN can outperform other state-of-the-art methods in predicting compound-protein binding affinities. In addition, the pairwise interactions predicted by MONN displayed compatible and accordant patterns in chemical properties, which provided another evidence to support the strong predictive power of MONN. These results suggested that MONN can offer a powerful tool in predicting binding affinities of compound-protein pairs and also provide useful insights into understanding the molecular mechanisms of compound-protein interactions, which thus can greatly advance the drug discovery process. The source code of the MONN model and the dataset creation process can be downloaded from https://github.com/lishuya17/MONN.

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“…Incorporation of QM/MM simulations would broaden the understanding of particular state of rhodopsin photoactivation related diseases. The obtained knowledge can be utilized in drug design that target to stabilize the degradation of rhodopsin [58].…”

Section: Class a Rhodopsin Photoactivity Investigationmentioning

confidence: 99%

Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies

et al. 2020

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G protein-coupled receptors (GPCRs) are major drug targets due to their ability to facilitate signal transduction across cell membranes, a process that is vital for many physiological functions to occur. The development of computational technology provides modern tools that permit accurate studies of the structures and properties of large chemical systems, such as enzymes and GPCRs, at the molecular level. The advent of multiscale molecular modeling permits the implementation of multiple levels of theories on a system of interest, for instance, assigning chemically relevant regions to high quantum mechanics (QM) level of theory while treating the rest of the system using classical force field (molecular mechanics (MM) potential). Multiscale QM/MM molecular modeling have far-reaching applications in the rational design of GPCR drugs/ligands by affording precise ligand binding configurations through the consideration of conformational plasticity. This enables the identification of key binding site residues that could be targeted to manipulate GPCR function. This review will focus on recent applications of multiscale QM/MM molecular simulations in GPCR studies that could boost the efficiency of future structure-based drug design (SBDD) strategies.

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Ligand channel in pharmacologically stabilized rhodopsin

Cited by 34 publications

References 44 publications

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

Strategic Screening and Characterization of the Visual GPCR-mini-G Protein Signaling Complex for Successful Crystallization

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies

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