Pneumocystis jirovecii DHFR Mutational Scanning Data for Antifolate Resistance Modeling

Francois D. Rouleau published a dataset on figshare in 2026 detailing PCR cycles used in a study of Pneumocystis jirovecii, an opportunistic fungal pathogen. The research involved deep mutational scanning of the dihydrofolate reductase (DHFR) protein expressed in yeast to measure the effects of amino-acid substitutions on function and resistance to antifolate drugs. The dataset, 16.7 KB in size, supports a machine-learning framework for predicting resistance mutations.

Use Cases

• Training random forest models to classify drug resistance mutations based on experimental functional measurements.
• Predicting resistance to trimethoprim by extrapolating from a model trained on methotrexate resistance data.
• Analyzing the functional-resistance trade-off within the PjDHFR protein using deep mutational scanning results.
• Integrating biophysical features like distance to ligand and protein stability into resistance prediction models.

Strengths

• Dataset is licensed under CC-BY-4.0, permitting broad reuse with attribution.
• The underlying study integrates experimental deep mutational scanning with multiple biophysical modeling approaches.
• The research framework is designed to be interpretable, highlighting key predictors like functional trade-off and ligand distance.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 16.7 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Data likely originates from a laboratory study involving deep mutational scanning and machine learning analysis.

Freshness

Last updated 2026-05-27 17:44:24; freshness should be verified.