protein-qc

Protein design quality-control can be automated by applying standardized thresholds to evaluate design viability, filtering by structural confidence, interface quality, and sequence liabilities. This Skill processes your designs CSV to prune unsuitable candidates and reduce experimental failure risk.

Standard protein design filtering thresholds include pLDDT > 0.85, ipTM > 0.50, and scRMSD < 2.0. These hard thresholds evaluate structural confidence and interface quality to ensure design viability before advancing candidates.

You can compute a composite score for protein designs by combining multiple metrics such as pLDDT, ipTM, PAE, SC, and esm2_pll into a single ranking score. This composite scoring produces prioritized design sets for experimental testing.

Yes, automated quality-control checks include risk assessment with severity scoring to identify actionable sequence liabilities across binding, expression, and structural assessments. This guides targeted redesign efforts for problematic candidates.

The best way to rank protein designs is tiered filtering followed by composite scoring. By applying hard thresholds for structural confidence and interface quality, then combining multiple metrics, you output top candidates optimized for experimental testing.

Yes, the protein QC pipeline accepts standard design CSV uploads to evaluate viability. It applies standardized thresholds across structural, binding, and expression assessments to support tiered filtering in high-throughput campaigns.