ipsae
About
The ipsae skill ranks protein binder designs using the ipSAE scoring function, which outperforms ipTM and iPAE for identifying successful binders. Use it to filter and rank outputs from tools like RFdiffusion or to compare predictions from AF2/AF3. It's designed for prioritizing designs for experimental testing and predicting binding success rates.
Quick Install
Claude Code
Recommendednpx skills add NeverSight/skills_feed -a claude-code/plugin add https://github.com/NeverSight/skills_feedgit clone https://github.com/NeverSight/skills_feed.git ~/.claude/skills/ipsaeCopy and paste this command in Claude Code to install this skill
GitHub Repository
Related Skills
protein-qc
OtherThe `protein-qc` skill provides research-backed quality control metrics and filtering thresholds for evaluating protein designs. It helps developers check sequence liabilities, compute biophysical and interface properties, and create multi-stage filtering pipelines. Use it for pre-screening designs based on pLDDT, ipTM, PAE, and other benchmarks before experimental testing.
binding-characterization
OtherThis skill provides expert guidance for planning, troubleshooting, and interpreting Surface Plasmon Resonance (SPR) and Bio-Layer Interferometry (BLI) binding experiments. It helps developers choose between SPR and BLI platforms using a detailed decision matrix based on factors like sensitivity, throughput, and sample requirements. Use it when designing kinetics studies or analyzing binding data artifacts.
binding-characterization
OtherThis skill provides guidance for planning and troubleshooting SPR and BLI binding kinetics experiments. It helps developers choose between platforms, interpret data artifacts, and resolve poor binding signals. Key features include a decision matrix comparing SPR/BLI and troubleshooting for common experimental issues.
binding-characterization
OtherThis skill provides expert guidance for planning, troubleshooting, and interpreting Surface Plasmon Resonance (SPR) and Bio-Layer Interferometry (BLI) binding kinetics experiments. It helps developers choose between SPR and BLI platforms using a detailed decision matrix and offers solutions for common issues like poor signal or data artifacts. Use it when designing or validating binding assays to ensure robust experimental results.
