Home / pku-yuangroup / openai4s · skills/bioskills/bio-chemoinformatics-protac-degraders/SKILL.md · GitHub

bio-protac-degraders skillA

bio-protac-degraders is agent-read markdown (skill) from pku-yuangroup/openai4s: Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows..

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## Version Compatibility

Reference examples tested with: PRosettaC (web service), DeepTernary research code, AlphaFold3, Boltz-1 / Boltz-2, RDKit 2024.09+, OpenMM 8.1+ (for ternary MD).

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# PROTAC and Bivalent Degrader Design

Design bifunctional molecules (PROTACs) that recruit an E3 ubiquitin ligase to a target protein, inducing target ubiquitination and proteasomal degradation. PROTACs differ from traditional drugs: a productive **ternary complex** (target + PROTAC + E3) is required, not just target binding. The modality has produced clinical programs, but their development and regulatory status changes rapidly and must be checked from current sources. PROTAC design balances **target ligand binding**, **E3 ligand binding**, **linker geometry** (length, rigidity, chemistry), **cooperativity**, **dose-dependent ternary-complex formation**, and **cell…
…

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Source

GitHub

pku-yuangroup/openai4s · 586 stars · license MIT · pushed 2026-09-23 · branch main

API

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