Structural and functional characterization of G protein–coupled receptors with deep mutational scanning

By E. Jones, N. Lubock, A. Venkatakrishnan, Jeffrey Wang, Alex M. Tseng, Joseph M. Paggi, Naomi R. Latorraca, Daniel Cancilla, M. Satyadi, Jessica E. Davis and 3 more. 2019

140 citations recorded by the source corpus.

In humans, the 813 G protein-coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state, and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G-protein signal transduction. We tested 7,800 of 7,828 possible single amino acid substitutions to the beta-2 adrenergic receptor (β2AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for β2AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we resolve residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors.

Subfields: Animal Science and Zoology, Cellular and Molecular Neuroscience, Molecular Biology

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In Molecular Biology

Citations in the corpus

Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning (2020, background): “Variational Autoencoders (VAEs) [18, 19] have been shown to be effective at learning complex high-dimensional distributions across a wide range of tasks – from computer vision, to natural language processing [20], to molecules modeling [21], and many others.”