Project description:G protein-coupled receptors (GPCRs) mediate a variety of signaling pathways and are among the most common pharmacological targets. While advances in structural biochemistry have provided deep functional insights into dozens of key receptors, many of the 800+ human GPCRs remain understudied. In the following, we introduce a versatile “deep receptor scanning” platform that can be used to experimentally characterize 767 human GPCRs and 174 known GPCR splice variants in parallel. We quantitatively characterize the relative abundance of receptor transcripts, their translational efficiency, and the plasma membrane expression of each receptor in the context of a recombinant pool of HEK293T cells expressing individual GPCRs. We then employ machine learning to identify specific structural features that modulate GPCR expression. This experimental platform and informatic approach are compatible with a variety of assays and can be used to efficiently explore the biochemical and pharmacological properties of the GPCRome.
Project description: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.
Project description:Despite the importance of Aβ aggregation in Alzheimer’s disease etiology, our understanding of the sequence determinants of aggregation is sparse and largely derived from in vitro studies. For example, in vitro proline and alanine scanning mutagenesis of Aβ40 proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant Aβ42. Thus, to better understand the molecular determinants of Aβ42 aggregation in a cell-based system, we combined a yeast DHFR aggregation assay with deep mutational scanning. We measured the effect of 791 of the 798 possible single amino acid substitutions on the aggregation propensity of Aβ42. We found that ~75% of substitutions, largely to hydrophobic residues, maintained or increased aggregation. We identified 11 positions at which substitutions, particularly to hydrophilic and charged amino acids, disrupted Aβ aggregation. These critical positions were similar but not identical to critical positions identified in previous Aβ mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different Aβ aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived Aβ aggregates.
Project description:Deep mutational scanning is a powerful method for exploring the mutational fitness landscape of proteins. Its adaptation to anti-CRISPR proteins, which are natural CRISPR-Cas inhibitors and key players in the co-evolution of microbes and phages, facilitates their characterization and optimization. Here, we developed a robust anti-CRISPR deep mutational scanning pipeline in Escherichia coli that combines synthetic gene circuits based on CRISPR interference with flow cytometry coupled sequencing and mathematical modeling. Using this pipeline, we characterized comprehensive single point mutation libraries for AcrIIA4 and AcrIIA5, two potent inhibitors of CRISPR-Cas9. The resulting mutational fitness landscapes revealed considerable mutational tolerance for both Acrs, suggesting an intrinsic redundancy with respect to Cas9 inhibitory features, and – for AcrIIA5 – indicated mutations that boost Cas9 inhibition. Subsequent in vitro characterization suggested that the observed differences in inhibitory potency between mutant inhibitors were mostly due to changes in binding affinity rather than protein expression levels. Finally, to demonstrate that our pipeline can inform Acrs-based genome editing applications, we employed a selected subset of mutant inhibitors to increase CRISPR-Cas9 target specificity by modulating Cas9 activity. Taken together, our work establishes deep mutational scanning as a powerful method for anti-CRISPR protein characterization and optimization.
Project description:Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based In silico variant prediction tools have limited utility for engineering plant proteins as they were unable to reproduce our experimental results. We conclude that DMS can produce datasets which, when combined with the right predictive computational frameworks, can direct plant engineering efforts through derivative phenotype selection and evolutionary insights.
Project description:Deep mutational scanning of the interaction of JUN's leucine zipper domain and other human bZIPs in different experimental conditions
Project description:The influenza A virus nuclear export protein (NEP) is a multifunctional protein that is essential for the viral life cycle and has very high sequence conservation. However, since the open reading frame of NEP largely overlaps with that of another influenza viral protein, non-structural protein 1, it is difficult to infer the functional constraints of NEP based on sequence conservation analysis. In addition, the N-terminal of NEP is structurally disordered, which further complicates the understanding of its function. Here, we systematically measure the replication fitness effects of >1,800 mutations of NEP. Our results show that the N-terminal domain has high mutational tolerance. Additional experiments show that N-terminal domain mutations affect viral transcription and replication dynamics, host cellular responses, and mammalian adaptation of avian influenza virus. Overall, our study not only advances the functional understanding of NEP but also provides insights into its evolutionary constraints.