Strategies for the Structural Determination of G Protein-coupled Receptors: From an Example of Histamine H<sub>1</sub> Receptor

Abstract Motivation Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities; this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key substructural features which determine ligand bioactivities are needed to address this issue. Results A new method, SED, was proposed to predict ligand bioactivities and to recognize key substructures associated with GPCRs through the coupling of screening for Lasso of long extended-connectivity fingerprints (ECFPs) with deep neural network training. The SED pipeline contains three successive steps: (i) representation of long ECFPs for ligand molecules, (ii) feature selection by screening for Lasso of ECFPs and (iii) bioactivity prediction through a deep neural network regression model. The method was examined on a set of 16 representative GPCRs that cover most subfamilies of human GPCRs, where each has 300–5000 ligand associations. The results show that SED achieves excellent performance in modelling ligand bioactivities, especially for those in the GPCR datasets without sufficient ligand associations, where SED improved the baseline predictors by 12% in correlation coefficient (r2) and 19% in root mean square error. Detail data analyses suggest that the major advantage of SED lies on its ability to detect substructures from long ECFPs which significantly improves the predictive performance. Availability and implementation The source code and datasets of SED are freely available at https://zhanglab.ccmb.med.umich.edu/SED/. Supplementary information Supplementary data are available at Bioinformatics online.

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Toward G protein-coupled receptor structure-based drug design using X-ray lasers

IUCrJ ◽

10.1107/s2052252519013137 ◽

2019 ◽

Vol 6 (6) ◽

pp. 1106-1119 ◽

Cited By ~ 11

Author(s):

Andrii Ishchenko ◽

Benjamin Stauch ◽

Gye Won Han ◽

Alexander Batyuk ◽

Anna Shiriaeva ◽

...

Keyword(s):

Drug Design ◽

Crystal Structures ◽

G Protein ◽

Rapid Determination ◽

Binding Pocket ◽

G Protein Coupled Receptors ◽

Structure Based Drug Design ◽

G Protein Coupled ◽

Serial Femtosecond Crystallography

Rational structure-based drug design (SBDD) relies on the availability of a large number of co-crystal structures to map the ligand-binding pocket of the target protein and use this information for lead-compound optimization via an iterative process. While SBDD has proven successful for many drug-discovery projects, its application to G protein-coupled receptors (GPCRs) has been limited owing to extreme difficulties with their crystallization. Here, a method is presented for the rapid determination of multiple co-crystal structures for a target GPCR in complex with various ligands, taking advantage of the serial femtosecond crystallography approach, which obviates the need for large crystals and requires only submilligram quantities of purified protein. The method was applied to the human β2-adrenergic receptor, resulting in eight room-temperature co-crystal structures with six different ligands, including previously unreported structures with carvedilol and propranolol. The generality of the proposed method was tested with three other receptors. This approach has the potential to enable SBDD for GPCRs and other difficult-to-crystallize membrane proteins.

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