DcuSEnvZ: High-Throughput Screening Data for 1,173 Bacterial Sensor Kinase Mutants

1,173 mutations in chimeric DcuS/EnvZ sensor histidine kinases were screened for signaling outputs in response to fumarate and aspartate. The 883.4 MB dataset includes input and output files for Python and R analysis scripts from the PlesaLab GitHub repository. Lucas Lippert deposited the data on figshare under a CC-BY-4.0 license, with raw sequencing files available in the NIH SRA.

Use Cases

• Identify key residues for ligand selectivity based on quantified signaling outputs for aspartate and fumarate.
• Train machine learning models to predict signaling phenotypes from sequence-function relationships in sensory domains.
• Expedite the design of biosensors for novel ligands by analyzing the effects of mutations in transmembrane and cytoplasmic domains.

Strengths

• Quantifies signaling outputs for 1,173 distinct mutations, providing a substantial mutational library.
• Includes data for two ligand conditions (fumarate and aspartate), enabling selectivity analysis.
• Raw sequencing data is available under BioProject PRJNA1468850, supporting reproducibility.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific modeling tasks.

Provenance

Source

PlesaLab, likely associated with an academic institution.

Collection Method

High-throughput screening of a mutagenized library using a synthetic two-component signaling system with a standardized fluorescence readout.

Freshness

Last updated 2026-05-21 21:22:55.