AGENT SKILL

tooluniverse-protein-modification-analysis

Analyze post-translational modifications (PTMs) of proteins — modification sites, types, proteoforms, functional effects at PTM sites, and PTM-dependent protein interactions. Integrates iPTMnet, ProtVar, UniProt, and STRING databases. Use when asked about protein phosphorylation, ubiquitination, acetylation, glycosylation, methylation, SUMOylation, or other PTMs; proteoform diversity; PTM-regulated interactions; or functional impact of PTM sites.

License 许可
MIT
最近更新
2026-11-02
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01 前言 Front Matter

name
tooluniverse-protein-modification-analysis
description
Analyze post-translational modifications (PTMs) of proteins — modification sites, types, proteoforms, functional effects at PTM sites, and PTM-dependent protein interactions. Integrates iPTMnet, ProtVar, UniProt, and STRING databases. Use when asked about protein phosphorylation, ubiquitination, acetylation, glycosylation, methylation, SUMOylation, or other PTMs; proteoform diversity; PTM-regulated interactions; or functional impact of PTM sites.

03 SKILL.md 正文

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

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