mims-harvard/tooluniverse-protein-modification-analysis
skills/tooluniverse-protein-modification-analysis/SKILL.md
Post-translational modification (PTM) analysis — phosphorylation, ubiquitination, acetylation, glycosylation, methylation. Uses iPTMnet (sites + enzymes), ProtVar (functional consequences), UniProt (baseline), STRING, ELM (linear motifs), MassIVE/ProteomeXchange (experimental). Use for PTM site annotation, kinase-substrate identification, and PTM-disease associations.
$ install --global
skillsauthnpx skillsauth add mims-harvard/tooluniverse tooluniverse-protein-modification-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- tooluniverse-protein-modification-analysis
- description:
- Post-translational modification (PTM) analysis — phosphorylation, ubiquitination, acetylation, glycosylation, methylation. Uses iPTMnet (sites + enzymes), ProtVar (functional consequences), UniProt (baseline), STRING, ELM (linear motifs), MassIVE/ProteomeXchange (experimental). Use for PTM site annotation, kinase-substrate identification, and PTM-disease associations.
- disable-model-invocation:
- true
Protein Post-Translational Modification Analysis
Comprehensive PTM analysis using iPTMnet (primary), ProtVar (functional context), UniProt (baseline), STRING (interactions), ELM (linear motifs), and MassIVE/ProteomeXchange (experimental data).
LOOK UP DON'T GUESS
- PTM sites/enzymes:
iPTMnet_get_ptm_sites - Functional consequence:
ProtVar_get_function+iPTMnet_get_ptm_ppi - Proteoforms:
iPTMnet_get_proteoforms - Linear motifs:
ELM_get_instances
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Domain Reasoning
PTMs are context-dependent: same phosphorylation site can activate or inhibit depending on kinase and effectors. Always check: which enzyme, what functional consequence, in what cell context.
KEY PRINCIPLES
- Disambiguation first -- resolve to UniProt accession before iPTMnet calls
- iPTMnet is SOAP-style -- every call requires
operationparameter - Evidence-graded -- distinguish experimental (T1) from predicted (T4)
- English-first queries
Workflow
Phase 0: Protein Disambiguation → UniProt accession
Phase 1: PTM Site Inventory → iPTMnet_get_ptm_sites
Phase 2: Proteoform Analysis → iPTMnet_get_proteoforms
Phase 3: PTM-Dependent Interactions → iPTMnet_get_ptm_ppi
Phase 4: Functional Context → ProtVar_get_function at key sites
Phase 4b: Linear Motif Context → ELM_get_instances for SLiM overlap
Phase 4c: Experimental Data → MassIVE/ProteomeXchange
Phase 5: Synthesis & Report
Phase 0: Disambiguation
iPTMnet_search(operation="search", search_term="TP53", role="Substrate")-- find UniProt IDs- If user provides UniProt accession directly, use it
- Select human entry if multiple hits
Phase 1: PTM Sites
iPTMnet_get_ptm_sites(operation="get_ptm_sites", uniprot_id="P04637") -- returns position, residue, modification type, enzyme, evidence. Group by modification type. Fallback: UniProt_get_entry_by_accession PTM annotations.
Phase 2: Proteoforms
iPTMnet_get_proteoforms(operation="get_proteoforms", uniprot_id=...) -- distinct PTM combinations. Focus on those with functional/disease annotations if >20.
Phase 3: PTM-Dependent Interactions
iPTMnet_get_ptm_ppi(operation="get_ptm_ppi", uniprot_id=...) -- interacting protein, PTM site, effect (enables/disrupts). Supplement with STRING_get_interaction_partners(identifiers=gene, species=9606, required_score=700).
Phase 4: Functional Context
ProtVar_get_function(accession=..., position=N, variant_aa=AA) -- domain, active site, binding site, conservation. Grade: active-site PTM > domain-core > disordered region.
Phase 4b: Linear Motifs (ELM)
ELM_get_instances(operation="get_instances", uniprot_id=..., motif_type="MOD") -- MOD = modification sites, DEG = degradation signals. Cross-reference with Phase 1 PTM positions. ELM_list_classes(operation="list_classes") for motif details.
Phase 4c: Experimental Data
MassIVE_search_datasets(species="9606"), MassIVE_get_dataset(accession="MSV...") for public MS datasets.
Evidence Grading
| Tier | Criteria | |------|----------| | T1 | PTM at validated active/binding site with functional data | | T2 | PTM in structured domain with ProtVar annotation | | T3 | Correlation data only (mass spec detection) | | T4 | Predicted, no experimental validation |
Tool Parameter Reference
| Tool | Key Params |
|------|-----------|
| iPTMnet_search | operation="search", search_term, role |
| iPTMnet_get_ptm_sites | operation="get_ptm_sites", uniprot_id |
| iPTMnet_get_proteoforms | operation="get_proteoforms", uniprot_id |
| iPTMnet_get_ptm_ppi | operation="get_ptm_ppi", uniprot_id |
| ELM_get_instances | operation="get_instances", uniprot_id, motif_type |
| ELM_list_classes | operation="list_classes" |
| MassIVE_search_datasets | page_size, species |
Critical: All iPTMnet and ELM tools require operation as first parameter (SOAP-style).
Fallbacks
| Situation | Fallback | |-----------|----------| | Not in iPTMnet | UniProt PTM/processing annotations | | No PTM-PPI data | STRING general PPI | | No ProtVar data | UniProt domain annotations | | No ELM data | Proceed with iPTMnet/UniProt only |
Limitations
- iPTMnet biased toward well-studied proteins
- Proteoform data covers observed combinations only
- PTM-PPI: only PTM-specific evidence; more PPIs exist in STRING
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