
Tooluniverse Protein Modification Analysis
- 186 installs
- 1.6k repo stars
- Updated August 4, 2026
- mims-harvard/tooluniverse
Identify and interpret protein post-translational modifications—phosphorylation, acetylation, ubiquitination—from proteomics data with site localization and pathway impact summaries.
About
ToolUniverse protein modification analysis skill for Claude Code agents processing proteomics PTM data: detecting modification sites, assigning localization confidence, mapping signaling pathway effects, and summarizing functional consequences of phosphorylation and related post-translational changes.
- Post-translational modification site analysis
- Covers phosphorylation and related PTMs
- Links modifications to signaling pathways
- Agent-driven proteomics interpretation
- ToolUniverse proteomics API orchestration
Tooluniverse Protein Modification Analysis by the numbers
- 186 all-time installs (skills.sh)
- +5 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #677 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-protein-modification-analysisAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 186 |
|---|---|
| repo stars | ★ 1.6k |
| Last updated | August 4, 2026 |
| Repository | mims-harvard/tooluniverse ↗ |
What it does
Identify and interpret protein post-translational modifications—phosphorylation, acetylation, ubiquitination—from proteomics data with site localization and pathway impact summaries.
Files
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
1. Disambiguation first -- resolve to UniProt accession before iPTMnet calls 2. iPTMnet is SOAP-style -- every call requires operation parameter 3. Evidence-graded -- distinguish experimental (T1) from predicted (T4) 4. 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