[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M75KDVXZJD9B00AXF0VTQ2C0":3,"skill-same:01M75KDVXZJD9B00AXF0VTQ2C0":46},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":6,"ownerDescription":30,"updatedAt":31,"install":32,"resources":45},"01M75KDVXZJD9B00AXF0VTQ2C0","protac-degraders",null,"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 with constraints), cooperativity (alpha), DC50 \u002F 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.","pradyumnasagar\u002Fopen-research-skills","pradyumnasagar","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F10559871?v=4","01M75K78MYCCVKRTYGT8KKC380","Pradyumna Jayaram","01M75K78NTNE41F2PE1XBVFHAS",8,"MIT","https:\u002F\u002Fgithub.com\u002Fpradyumnasagar\u002Fopen-research-skills\u002Fblob\u002Fmain\u002Fskills\u002Fchemoinformatics\u002Fprotac-degraders\u002FSKILL.md",[],[],"2026-11-12 13:55:43",false,"---\n\nname: protac-degraders\ndescription: \"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 with constraints), cooperativity (alpha), DC50 \u002F 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.\"\nlicense: MIT\n---\n\u003C!-- metadata:\n  category: chemoinformatics\n  oversight: low\n  version: 1.0.0\n  author: Pradyumna Jayaram\n  tags: [chemoinformatics, research]\n  difficulty: intermediate\n-->\n\n## Version Compatibility\n\nReference examples tested with: PRosettaC (web service), DeepTernary 1.0+, AlphaFold3 (constraints-enabled), Boltz-1 \u002F Boltz-2, RDKit 2024.09+, OpenMM 8.1+ (for ternary MD).\n\nBefore using code patterns, verify installed versions match. If versions differ:\n- Python: `pip show \u003Cpackage>` then `help(module.function)` to check signatures\n\nIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.\n\n# PROTAC and Bivalent Degrader Design\n\n## Hard rules\n\n1. **No fabricated citations.** Every cited work must resolve to a verifiable\n2. **No claim without provenance.** Every quantitative or factual claim\n3. **No silent failure.** Every script invocation, API call, or tool use must declare its exit status and what to do on non-zero. A skill that silently swallows errors is a violation.\n\n## When to use\n\nLoad this skill when the user asks a question that matches its declared\ntrigger conditions (see the frontmatter `description` for the most common\nones). Do not load it for questions outside its scope — defer to the\nappropriate ORS skill instead.\n\n## When NOT to use\n\nDo not load this skill if the question is in a sibling skill's domain\n(see `## Cross-references`), if the user explicitly asks for a different\ntool, or if the task is outside the skill's declared category.\n\n## E3 Ligase Choice\n\n| E3 ligase | Ligand series | Best at | Limitations |\n|-----------|---------------|---------|-------------|\n| VHL | VL-269 | Surface-exposed targets | Tissue-restricted expression |\n| CRBN (cereblon) | thalidomide, pomalidomide | Broad tissue expression | Off-target neosubstrates (IKZF1, SALL4) |\n| IAP (XIAP, cIAP1) | SMAC mimetics (LCL161) | Apoptotic \u002F IAP targets | Limited target scope |\n| MDM2 | nutlin \u002F idasanutlin | TP53 pathway | Limited target diversity |\n| KEAP1 | DDB1-DCAF15-Keap1 | NRF2 pathway | Specialized use |\n| RNF114 | EN450 | Newer; under exploration | Limited tooling |\n\n**Decision:** For first-generation PROTAC, **CRBN (cereblon)** is the most-developed. **VHL** is second-most-developed (more selective; tissue-restricted).\n\n## Linker Design Principles\n\n| Property | Range | Effect |\n|----------|-------|--------|\n| Linker length | 8-30 atoms | Critical; geometry-dependent |\n| Linker rigidity | Flexible (PEG) vs rigid (piperazine) | Higher rigidity reduces entropy penalty |\n| Linker chemistry | PEG, alkyl, piperazine, triazole, ether, amide | PEG common; rigid for tighter binding |\n| Click chemistry compatibility | Triazole compatible | Easy synthesis |\n| MW range | PROTAC 800-1500 Da | Lipinski-violating but accepted |\n| Polar atoms | 1-5 per linker | Permeability vs solubility balance |\n\"\n**Critical:** \"Goldilocks linker length\" is target-specific. Too short = ternary clash; too long = ternary entropy too high. Typically 12-20 atoms for surface-exposed targets.\n\n## Decision Tree by Scenario\n\n| Goal | E3 \u002F linker | Tools |\n|------|-------------|-------|\n| First-generation PROTAC, surface-exposed target | CRBN + PEG linker (10-15 atoms) | PRosettaC for ternary prediction |\n| Selective degrader (avoid off-target) | VHL + rigid linker | PRosettaC + cellular validation |\n| BTK \u002F IAP family targets | IAP-based PROTAC | Standard pipelines |\n| Novel target, no cryptic | Multiple E3 \u002F linker variants | Combinatorial design + PRosettaC |\n| Molecular glue (non-PROTAC) | CRBN-based | Distinct mechanism |\n\n## Ternary Complex Prediction Tools\n\n| Tool | Approach | Strength | Fails when |\n|------|----------|----------|------------|\n| PRosettaC | Rosetta-based protein-protein docking | Industry standard | Slow; needs crystal structures |\n| DeepTernary | ML-based | Fast; no crystal required | Limited accuracy for new targets |\n| AlphaFold3 (with constraints) | Foundation model | Excellent when co-crystal of binary available | API-restricted |\n| Boltz-1 \u002F Boltz-2 | Foundation model | Open source; multimer support | Larger than AF3 in some benchmarks |\n| MOE | Commercial | GUI + scriptable | License cost |\n\n```python\n# Conceptual PRosettaC workflow\n# prc -target target.pdb -e3 crbn.pdb -linker-protac protac.smi \\\n#     -output ternary.pdb\n```\n\n## Cooperativity (Alpha)\n\nCooperativity measures whether the PROTAC binds target+E3 more tightly than the sum of binary bindings:\n\n```\nalpha = Kd(binary) \u002F Kd(ternary) * 1\n```\n\n| alpha | Interpretation | Action |\n|-------|----------------|--------|\n| > 10 | Strong cooperativity | Excellent candidate |\n| 1-10 | Moderate | Iterative optimization |\n| \u003C 1 | Negative (anticooperative) | Re-design |\n\nCooperativity > 5 is desirable for cellular activity. Negative cooperativity suggests linker too long or attachment geometry wrong.\n\n## DC50 \u002F Dmax Characterization\n\n- **DC50**: concentration for 50% target degradation\n- **Dmax**: maximum degradation achievable (0-100%)\n- **Hook effect**: PROTAC activity drops at high concentration (binary complexes dominate over ternary)\n\n```python\n# Cellular degradation assay (Western blot or reporter)\n# Measure DC50 (e.g. 10 nM) and Dmax (e.g. 90% at 1 uM)\n# If Dmax plateaus \u003C 80% at high conc, check hook effect\n```\n\n## PROTAC Databases\n\n| Database | URL | Compounds |\n|----------|-----|-----------|\n| PROTAC-DB | http:\u002F\u002Fprotacdb.weizmann.ac.il\u002F | ~5000 published PROTACs |\n| EU-OPENSCREEN | Various | Curated |\n\n## Per-Tool Failure Modes\n\n### Hook effect at clinical dose\n\n**Trigger:** Bell-shaped degradation curve in cellular assay.\n\n**Mechanism:** At high PROTAC concentration, binary complexes dominate over ternary; productive degradation only happens at moderate concentration.\n\n**Symptom:** Dmax falls at high PROTAC; therapeutic window narrow.\n\n**Fix:** Reduce dose; or re-design linker to favor ternary.\n\n### Negative cooperativity\n\n**Trigger:** PROTAC binds target with similar Kd to parent ligand, but ternary unstable.\n\n**Mechanism:** Linker geometry puts target and E3 in unfavorable orientation.\n\n**Symptom:** Cellular degradation absent despite binary binding.\n\n**Fix:** PRosettaC-driven linker re-design; consider rigid linker.\n\n### Cellular permeability poor\n\n**Trigger:** PROTAC active in lysate but not in cells.\n\n**Mechanism:** MW > 1000 Da often violates cellular permeability rules.\n\n**Symptom:** Cellular DC50 >> biochemical Kd.\n\n**Fix:** Reduce MW; switch to rigid linker (smaller); use \"PROTAC-to-drug\" libraries optimized for permeability.\n\n## References\n\n- Békés et al. -- PROTAC review.\n- Gadd et al. -- BRD4 degraders.\n- Bondeson et al. -- CRBN-based PROTACs.\n\n## Related Skills\n\n- chemoinformatics\u002Fvirtual-screening - Target ligand design\n- chemoinformatics\u002Fretrosynthesis - Synthesizability\n- chemoinformatics\u002Fgenerative-design - Generative linker design\n- chemoinformatics\u002Fcovalent-design - Covalent E3 ligands\n\n## Cross-references\n\nOther skills in this category:\n\n- admet-prediction\n- conformer-generation\n- covalent-design\n- docking-rescoring\n- free-energy-calculations\n- generative-design\n- molecular-descriptors\n- molecular-io\n- molecular-standardization\n- pharmacophore-modeling\n- pose-validation\n- qsar-modeling\n- retrosynthesis\n- scaffold-analysis\n- shape-similarity\n- similarity-searching\n- substructure-search\n- virtual-screening\n\n## Changelog\n\n- **1.1.0** (migration) — Bulk-migrated to v0.4.0 schema: canonical\n  metadata block, base Hard rules, Cross-references. Body content\n  unchanged; author should review and fill in any domain-specific\n  extensions to the Hard rules.\n- **1.0.0** — Initial release.\n\n","\u003C!-- metadata:\n  category: chemoinformatics\n  oversight: low\n  version: 1.0.0\n  author: Pradyumna Jayaram\n  tags: [chemoinformatics, research]\n  difficulty: intermediate\n-->\n\n## Version Compatibility\n\nReference examples tested with: PRosettaC (web service), DeepTernary 1.0+, AlphaFold3 (constraints-enabled), Boltz-1 \u002F Boltz-2, RDKit 2024.09+, OpenMM 8.1+ (for ternary MD).\n\nBefore using code patterns, verify installed versions match. If versions differ:\n- Python: `pip show \u003Cpackage>` then `help(module.function)` to check signatures\n\nIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.\n\n# PROTAC and Bivalent Degrader Design\n\n## Hard rules\n\n1. **No fabricated citations.** Every cited work must resolve to a verifiable\n2. **No claim without provenance.** Every quantitative or factual claim\n3. **No silent failure.** Every script invocation, API call, or tool use must declare its exit status and what to do on non-zero. A skill that silently swallows errors is a violation.\n\n## When to use\n\nLoad this skill when the user asks a question that matches its declared\ntrigger conditions (see the frontmatter `description` for the most common\nones). Do not load it for questions outside its scope — defer to the\nappropriate ORS skill instead.\n\n## When NOT to use\n\nDo not load this skill if the question is in a sibling skill's domain\n(see `## Cross-references`), if the user explicitly asks for a different\ntool, or if the task is outside the skill's declared category.\n\n## E3 Ligase Choice\n\n| E3 ligase | Ligand series | Best at | Limitations |\n|-----------|---------------|---------|-------------|\n| VHL | VL-269 | Surface-exposed targets | Tissue-restricted expression |\n| CRBN (cereblon) | thalidomide, pomalidomide | Broad tissue expression | Off-target neosubstrates (IKZF1, SALL4) |\n| IAP (XIAP, cIAP1) | SMAC mimetics (LCL161) | Apoptotic \u002F IAP targets | Limited target scope |\n| MDM2 | nutlin \u002F idasanutlin | TP53 pathway | Limited target diversity |\n| KEAP1 | DDB1-DCAF15-Keap1 | NRF2 pathway | Specialized use |\n| RNF114 | EN450 | Newer; under exploration | Limited tooling |\n\n**Decision:** For first-generation PROTAC, **CRBN (cereblon)** is the most-developed. **VHL** is second-most-developed (more selective; tissue-restricted).\n\n## Linker Design Principles\n\n| Property | Range | Effect |\n|----------|-------|--------|\n| Linker length | 8-30 atoms | Critical; geometry-dependent |\n| Linker rigidity | Flexible (PEG) vs rigid (piperazine) | Higher rigidity reduces entropy penalty |\n| Linker chemistry | PEG, alkyl, piperazine, triazole, ether, amide | PEG common; rigid for tighter binding |\n| Click chemistry compatibility | Triazole compatible | Easy synthesis |\n| MW range | PROTAC 800-1500 Da | Lipinski-violating but accepted |\n| Polar atoms | 1-5 per linker | Permeability vs solubility balance |\n\"\n**Critical:** \"Goldilocks linker length\" is target-specific. Too short = ternary clash; too long = ternary entropy too high. Typically 12-20 atoms for surface-exposed targets.\n\n## Decision Tree by Scenario\n\n| Goal | E3 \u002F linker | Tools |\n|------|-------------|-------|\n| First-generation PROTAC, surface-exposed target | CRBN + PEG linker (10-15 atoms) | PRosettaC for ternary prediction |\n| Selective degrader (avoid off-target) | VHL + rigid linker | PRosettaC + cellular validation |\n| BTK \u002F IAP family targets | IAP-based PROTAC | Standard pipelines |\n| Novel target, no cryptic | Multiple E3 \u002F linker variants | Combinatorial design + PRosettaC |\n| Molecular glue (non-PROTAC) | CRBN-based | Distinct mechanism |\n\n## Ternary Complex Prediction Tools\n\n| Tool | Approach | Strength | Fails when |\n|------|----------|----------|------------|\n| PRosettaC | Rosetta-based protein-protein docking | Industry standard | Slow; needs crystal structures |\n| DeepTernary | ML-based | Fast; no crystal required | Limited accuracy for new targets |\n| AlphaFold3 (with constraints) | Foundation model | Excellent when co-crystal of binary available | API-restricted |\n| Boltz-1 \u002F Boltz-2 | Foundation model | Open source; multimer support | Larger than AF3 in some benchmarks |\n| MOE | Commercial | GUI + scriptable | License cost |\n\n```python\n# Conceptual PRosettaC workflow\n# prc -target target.pdb -e3 crbn.pdb -linker-protac protac.smi \\\n#     -output ternary.pdb\n```\n\n## Cooperativity (Alpha)\n\nCooperativity measures whether the PROTAC binds target+E3 more tightly than the sum of binary bindings:\n\n```\nalpha = Kd(binary) \u002F Kd(ternary) * 1\n```\n\n| alpha | Interpretation | Action |\n|-------|----------------|--------|\n| > 10 | Strong cooperativity | Excellent candidate |\n| 1-10 | Moderate | Iterative optimization |\n| \u003C 1 | Negative (anticooperative) | Re-design |\n\nCooperativity > 5 is desirable for cellular activity. Negative cooperativity suggests linker too long or attachment geometry wrong.\n\n## DC50 \u002F Dmax Characterization\n\n- **DC50**: concentration for 50% target degradation\n- **Dmax**: maximum degradation achievable (0-100%)\n- **Hook effect**: PROTAC activity drops at high concentration (binary complexes dominate over ternary)\n\n```python\n# Cellular degradation assay (Western blot or reporter)\n# Measure DC50 (e.g. 10 nM) and Dmax (e.g. 90% at 1 uM)\n# If Dmax plateaus \u003C 80% at high conc, check hook effect\n```\n\n## PROTAC Databases\n\n| Database | URL | Compounds |\n|----------|-----|-----------|\n| PROTAC-DB | http:\u002F\u002Fprotacdb.weizmann.ac.il\u002F | ~5000 published PROTACs |\n| EU-OPENSCREEN | Various | Curated |\n\n## Per-Tool Failure Modes\n\n### Hook effect at clinical dose\n\n**Trigger:** Bell-shaped degradation curve in cellular assay.\n\n**Mechanism:** At high PROTAC concentration, binary complexes dominate over ternary; productive degradation only happens at moderate concentration.\n\n**Symptom:** Dmax falls at high PROTAC; therapeutic window narrow.\n\n**Fix:** Reduce dose; or re-design linker to favor ternary.\n\n### Negative cooperativity\n\n**Trigger:** PROTAC binds target with similar Kd to parent ligand, but ternary unstable.\n\n**Mechanism:** Linker geometry puts target and E3 in unfavorable orientation.\n\n**Symptom:** Cellular degradation absent despite binary binding.\n\n**Fix:** PRosettaC-driven linker re-design; consider rigid linker.\n\n### Cellular permeability poor\n\n**Trigger:** PROTAC active in lysate but not in cells.\n\n**Mechanism:** MW > 1000 Da often violates cellular permeability rules.\n\n**Symptom:** Cellular DC50 >> biochemical Kd.\n\n**Fix:** Reduce MW; switch to rigid linker (smaller); use \"PROTAC-to-drug\" libraries optimized for permeability.\n\n## References\n\n- Békés et al. -- PROTAC review.\n- Gadd et al. -- BRD4 degraders.\n- Bondeson et al. -- CRBN-based PROTACs.\n\n## Related Skills\n\n- chemoinformatics\u002Fvirtual-screening - Target ligand design\n- chemoinformatics\u002Fretrosynthesis - Synthesizability\n- chemoinformatics\u002Fgenerative-design - Generative linker design\n- chemoinformatics\u002Fcovalent-design - Covalent E3 ligands\n\n## Cross-references\n\nOther skills in this category:\n\n- admet-prediction\n- conformer-generation\n- covalent-design\n- docking-rescoring\n- free-energy-calculations\n- generative-design\n- molecular-descriptors\n- molecular-io\n- molecular-standardization\n- pharmacophore-modeling\n- pose-validation\n- qsar-modeling\n- retrosynthesis\n- scaffold-analysis\n- shape-similarity\n- similarity-searching\n- substructure-search\n- virtual-screening\n\n## Changelog\n\n- **1.1.0** (migration) — Bulk-migrated to v0.4.0 schema: canonical\n  metadata block, base Hard rules, Cross-references. Body content\n  unchanged; author should review and fill in any domain-specific\n  extensions to the Hard rules.\n- **1.0.0** — Initial release.\n\n",{"name":5,"license":15,"description":7},"main","9c0b41e2036cc5588c699f06e658ea6dc3b9685d","skills\u002Fchemoinformatics\u002Fprotac-degraders",2,"https:\u002F\u002Fgithub.com\u002Fpradyumnasagar\u002Fopen-research-skills","https:\u002F\u002Fgithub.com\u002Fpradyumnasagar","PhD Scholar,\r\nManipal School Of Life Sciences, Manipal\r\n #OpenSourceDeveloper #LifeSciences #ComputationalBiology","2026-10-30 01:30:52",{"source":8,"skillName":5,"commands":33,"prompt":43,"zipAvailable":44},[34,37,40],{"tool":35,"command":36},"npx","npx skills add pradyumnasagar\u002Fopen-research-skills --skill protac-degraders",{"tool":38,"command":39},"bunx","bunx skills add pradyumnasagar\u002Fopen-research-skills --skill protac-degraders",{"tool":41,"command":42},"pnpm","pnpm dlx skills add pradyumnasagar\u002Fopen-research-skills --skill protac-degraders","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fpradyumnasagar\u002Fopen-research-skills\u002Fblob\u002Fmain\u002Fskills\u002Fchemoinformatics\u002Fprotac-degraders\u002FSKILL.md",true,[],[47,54,60,67,74,81],{"skillNo":48,"name":49,"nameZh":6,"description":50,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":51,"categories":52,"keywords":53,"publishedAt":19},"01M75KTS5K9QNX8FQBTJW2GQSH","good-skill","A well-formed test fixture for the validation 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meet.","https:\u002F\u002Fgithub.com\u002Fpradyumnasagar\u002Fopen-research-skills\u002Fblob\u002Fmain\u002Fskills\u002Fthesis-writing\u002Fthesis-structure\u002FSKILL.md",[],[],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M75KTE3WKW46FXFHQ579H8YP","thesis-methods-reproducibility","Use when writing or checking the Methods, figures, data, and code of a bioinformatics \u002F microbiology \u002F life-science PhD thesis — the FAIR data, versioned code, report-generation, figure-and-table discipline, and the reproducibility expectations that examiners probe at viva.","https:\u002F\u002Fgithub.com\u002Fpradyumnasagar\u002Fopen-research-skills\u002Fblob\u002Fmain\u002Fskills\u002Fthesis-writing\u002Fthesis-methods-reproducibility\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":19},"01M75KTA033ZQCJY3DC8P5VQET","thesis-defense-viva","Use when preparing for the viva voce \u002F oral thesis defense — building the examiner's question set, rehearsing the presentation, and the pre-defense polish (slides, summary, the 'what if I'm stuck' responses) for a life-science or bioinformatics PhD.","https:\u002F\u002Fgithub.com\u002Fpradyumnasagar\u002Fopen-research-skills\u002Fblob\u002Fmain\u002Fskills\u002Fthesis-writing\u002Fthesis-defense-viva\u002FSKILL.md",[],[]]