Oligonucleotides for Deep Mutational Scanning of Pneumocystis DHFR

Francois D. Rouleau published details on oligonucleotides used in a 2026 study of Pneumocystis jirovecii dihydrofolate reductase (PjDHFR). The dataset supports a deep mutational scanning experiment to measure the effects of all single amino-acid substitutions on enzyme function and resistance to antifolate drugs. Experimental results were integrated with biophysical modeling and a random forest model to predict resistance mutations.

Use Cases

• Replicating the deep mutational scanning experiment based on the described oligonucleotide sequences.
• Training interpretable machine learning models for drug resistance prediction based on functional and biophysical features.
• Analyzing the functional-resistance trade-off within an essential fungal protein as described in the study.
• Modeling the effects of mutations on resistance to trimethoprim using the described prediction framework.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• The underlying study employed a deep mutational scanning approach to test all single amino-acid substitutions in the target protein.
• Experimental data was integrated with multiple biophysical modeling approaches and an interpretable machine learning framework.

Limitations

• Dataset is very small at 13.3 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Likely generated as part of a laboratory study involving deep mutational scanning and computational modeling.

Freshness

Last updated 2026-05-27 17:44:22; freshness should be verified.