protein-design-qc

Rank protein design candidates by computing structural confidence metrics like pLDDT, pTM, and scRMSD alongside interface quality scores such as ipTM and PAE. This QC framework applies validated thresholds to filter low-quality designs and generates a reproducible ranked shortlist for experimental testing.

Composite scoring for designed proteins combines structural confidence (pLDDT, pTM, scRMSD), interface quality (ipTM, PAE, SC), and biophysical checks (instability, GRAVY, pI). These metrics enable tiered selection to prioritize candidates for experimental follow-up across design campaigns.

Yes, you can leverage dependencies including biopython and pyrosetta to compute biophysical and expression checks. The framework evaluates instability, GRAVY, and pI to assess solubility, filtering candidates that fail quality thresholds before experimental follow-up.

The best way to build a reproducible filtering pipeline for protein binder campaigns is applying a composite QC framework. It calibrates structural, interface, and biophysical threshold filters with experimental feedback to systematically weed out low-quality designs and output a ranked shortlist.

Yes, the protein design QC framework supports calibration with experimental feedback for ranking tasks. It adjusts composite scoring thresholds based on structural, interface, and biophysical metrics to refine the tiered selection of candidate designs across campaigns.