Escherichia coli strain:Stellar (Clontech)
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ABSTRACT:
PROVIDER: PRJNA196551 | ENA |
REPOSITORIES: ENA
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Project description:Specific interactions between proteins and DNA are essential to many biological processes. Yet it remains unclear how the diversification in DNA-binding specificity was brought about, and what were the mutational paths that led to changes in specificity. Using a pair of evolutionarily related DNA-binding proteins, each with a different DNA preference (ParB and Noc: both having roles in bacterial chromosome maintenance), we show that specificity is encoded by a set of four residues at the protein-DNA interface. Combining X-ray crystallography and deep mutational scanning of the interface, we suggest that permissive mutations must be introduced before specificity-switching mutations to reprogram specificity, and that mutational paths to a new specificity do not necessarily involve dual-specificity intermediates. Overall, our results provide insight into the possible evolutionary history of ParB and Noc, and in a broader context, might be useful in understanding the evolution of other classes of DNA-binding proteins.
2019-08-05 | GSE129285 | GEO
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.
2024-03-01 | GSE254101 | GEO
Project description:The ubiquitin-proteasome system plays critical roles in biology by regulating protein degradation. Despite their importance, precise recognition specificity is known for few of the 600 E3s. Here we establish a two-pronged strategy for identifying and mapping critical residues of internal degrons on a genome scale in HEK-293T cells. We employ Global Protein Stability profiling combined with machine learning to identify 15,800 peptides likely to contain sequence-dependent degrons. We combine this with scanning mutagenesis to define critical residues for over 5,000 predicted degrons. Focusing on Cullin-RING ligase degrons, we generated mutational fingerprints for 219 degrons and developed DegronID, a computational algorithm enabling the clustering of degron peptides with similar motifs. CRISPR analysis enabled the discovery of E3-degron pairs of which we uncover 16 pairs that revealed extensive degron variability and structural determinants. We provide the visualization of this data on the public DegronID Data Browser as a resource for future exploration.
2023-09-21 | GSE240610 | GEO
Project description:More than half of disease-causing missense variants are thought to lead to protein degradation, but the molecular mechanism of how these variants are recognized by the cell remains enigmatic. To approach this issue we have applied deep mutational scanning experiments to test the degradation of thousands of missense protein variants in large multiplexed experiments in cultured human cells. As a model protein we selected the ubiquitin-protein ligase Parkin, where known missense variants result in an autosomal recessive early onset Parkinsonism. The resulting mutational map comprises 9219 out of the 9300 (>99%) possible single-amino-acid substitution and nonsense Parkin variants. With a few notable exceptions, the majority of the destabilizing mutations are located within the structured domains of the protein, while the flexible linker regions are more tolerant to mutations. The cellular abundance data correlate with Parkin structural stability, evolutionary conservation, and separates known disease-linked variants from benign variants. Systematic mapping of degradation signals (degrons) shows that inherent primary degrons in Parkin largely overlap with regions that are highly sensitive to mutations. We identify a degron region proximal to the ACT element, which is enhanced by substitutions to hydrophobic residues. The vast majority of unstable Parkin variants are degraded through the ubiquitin-proteasome system and are stabilized at lowered temperatures. In conclusion, in addition to providing a diagnostic tool for rare genetic disorders, deep mutational scanning technologies have the potential to reveal both protein specific and general information on the specificity of the protein quality control network and the ubiquitin-proteasome system.
2024-01-31 | GSE254618 | GEO
Project description:The HIV-1 genome gains access to the inside of a cell via the mechanism of the viral spike protein Env, which undergoes a series of major conformational rearrangements after binding target receptors that ultimately drive virus-cell membrane fusion. Env is expressed as a heterogenous ensemble of conformations, which can inappropriately misdirect the host immune response towards the production of non-protective, strain-specific antibodies. Potent, broadly neutralizing antibodies (bnAbs) frequently recognize a ‘closed’ Env conformation, and therefore Env has undergone significant engineering to stabilize the closed state for vaccine incorporation. Previously, we used deep mutational scanning of Env from a prototypical tier 1 clade B strain (BaL) to characterize the sequence-activity landscape for binding to PG16, a bnAb that preferentially binds the closed state. Mutations were identified that increased expression of closed Env and reduced conformational heterogeneity, but these mutations were only partially transferable to Env sequences from other strains. To generate an expanded set of mutations that may be broadly applicable to diverse HIV-1 strains, we present here the deep mutational scanning of Env from the tier 2 clade C strain DU422 for interactions with CD4 and PG16. Residues across the trimerization domain and trimer interface have low mutational tolerance for maintaining PG16 recognition. New mutations are identified that enhance presentation of the closed Env conformation, and these are applied to Env sequences spanning multiple clades and tiers.
2019-02-01 | GSE117328 | GEO
Project description:Targeted protein degradation is a novel pharmacology established by drugs that recruit target proteins to E3 ubiquitin ligases. Based on the structure of the degrader and the target, different E3 interfaces are critically involved, thus forming defined "functional hotspots". Understanding disruptive mutations in functional hotspots informs on the architecture of the assembly, and highlights residues susceptible to acquire resistance phenotypes. Here, deep mutational scanning revealed hotspots that are conserved or specific for chemically distinct degraders and targets and validated hotspots mutated in patients that relapse from degrader treatment.
2022-09-09 | GSE198278 | GEO
Project description:Genomic analyses often involve scanning for potential transcription-factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein’s binding specificity by representing sequence motifs, including the gaps and dependencies between binding-site residues, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For 9 TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices learned by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases. In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
2012-12-13 | GSE42864 | GEO