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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)
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Listed on Skillselion
Installs186
repo stars1.6k
Last updatedAugust 4, 2026
Repositorymims-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

SKILL.mdMarkdownGitHub ↗

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.

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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

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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

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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.

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Evidence Grading

TierCriteria
T1PTM at validated active/binding site with functional data
T2PTM in structured domain with ProtVar annotation
T3Correlation data only (mass spec detection)
T4Predicted, no experimental validation

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Tool Parameter Reference

ToolKey Params
iPTMnet_searchoperation="search", search_term, role
iPTMnet_get_ptm_sitesoperation="get_ptm_sites", uniprot_id
iPTMnet_get_proteoformsoperation="get_proteoforms", uniprot_id
iPTMnet_get_ptm_ppioperation="get_ptm_ppi", uniprot_id
ELM_get_instancesoperation="get_instances", uniprot_id, motif_type
ELM_list_classesoperation="list_classes"
MassIVE_search_datasetspage_size, species

Critical: All iPTMnet and ELM tools require operation as first parameter (SOAP-style).

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Fallbacks

SituationFallback
Not in iPTMnetUniProt PTM/processing annotations
No PTM-PPI dataSTRING general PPI
No ProtVar dataUniProt domain annotations
No ELM dataProceed 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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