Rank binder designs using ipSAE scores from AlphaFold2, AlphaFold3, and Boltz1 predictions.
npx skills add https://github.com/adaptyvbio/protein-design-skills --skill ipsae
Please help me install this Agent Skill. Skill: ipsae Source: https://github.com/adaptyvbio/protein-design-skills/tree/main/skills/ipsae Command: npx skills add https://github.com/adaptyvbio/protein-design-skills --skill ipsae
ipSAE-based ranking provides an objective, scalable method to prioritize protein-protein interaction designs for experimental validation by scoring predicted interfaces.
Run ipsae.py with your predictor outputs to generate scores for ranking designs, e.g., python ipsae.py scores_rank.json design_0.pdb 10 10
High-intent search queries and answers about installing and using this skill.
You can rank protein binder designs by calculating ipSAE scores from AlphaFold2, AlphaFold3, or Boltz1 predictions. This scoring method objectively prioritizes protein-protein interaction candidates to improve experimental hit rates and reduce bench time.
ipSAE scoring is a method that evaluates protein-protein interaction interfaces using predicted aligned error and structural data. It generates chain-pair and residue-level scores to objectively assess and compare the interface quality of predicted protein complexes.
Yes, you can compare ipSAE scores across AlphaFold2, AlphaFold3, and Boltz1 predictions. The skill processes PAE and structure inputs from these predictors to standardize ranking and help select top binder design candidates.
You run the ipsae.py Python script with your predictor outputs, such as running python ipsae.py scores_rank.json design_0.pdb 10 10. This generates chain-pair and residue-level scores for batch ranking large sets of structure prediction outputs.
Yes, you need Python and NumPy installed to calculate ipSAE scores. The script requires these dependencies to process PAE and structure input files and output chain-pair and residue-level scoring data.
ipSAE ranking provides an alternative to ipTM or iPAE by calculating interface quality scores specifically for comparing predictors and prioritizing binder designs. It scales objectively to filter outputs from structure prediction pipelines for experimental testing.
alex-hhScore predicted protein-protein interactions using ipSAE, pDockQ, pDockQ2, and LIS metrics.
junior1pRank protein binder designs using ipSAE_min, LIS, and pDockQ scores.
PharMolixPredict protein and protein-ligand complex structures and affinities using Boltz-2.
zongtingweiScore and rank protein binder designs using ipSAE aligned-error data.
