Skill: hollyya/ppi-string-query (version 1.0.0)

--- name: ppi-string-query description: > Query STRING database for protein-protein interactions with confidence scores. Use this skill when: (1) Finding interaction partners for a protein of interest, (2) Retrieving confidence scores for protein-protein interactions, (3) Building protein interaction networks for pathway analysis. license: MIT category: knowledge-retrieval tags: [string, protein-protein-interaction, ppi, network, interactions] --- # STRING Protein-Protein Interaction Query Query the STRING database to retrieve protein-protein interactions with comprehensive confidence scores. ## When to Use - Find interaction partners for a protein (by UniProt ID) - Retrieve confidence scores for PPIs (experimental, text mining, database) - Build protein interaction networks for pathway analysis - Identify potential protein complexes or functional modules ## Workflow ### Basic Query ```python from open_biomed.tools.tool_registry import TOOLS # Query STRING for interaction partners tool = TOOLS["ppi_string_request"] results, _ = tool.run(uniprot_id="P04637") # TP53 # Access results for interaction in results: print(f"{interaction['partner_gene']}: {interaction['combined_score']}") ``` ### Custom Parameters ```python # High confidence interactions only, limit to 20 results, _ = tool.run( uniprot_id="P04637", species=9606, # Human (default) required_score=700, # High confidence (default) limit=20 # Max interactors ) ``` ## Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `uniprot_id` | str | required | UniProt accession (e.g., P04637) | | `species` | int | 9606 | NCBI taxonomy ID (9606=human) | | `required_score` | int | 700 | Min confidence (150/400/700/900) | | `limit` | int | 50 | Max interactors to return | ## Confidence Score Thresholds | Score | Level | Use Case | |-------|-------|----------| | 150 | Low | Exploratory analysis | | 400 | Medium | Balanced retrieval | | 700 | High | Reliable interactions (default) | | 900 | Highest | Very confident only | ## Expected Output ```json [ { "query_protein": "TP53", "partner_string_id": "9606.ENSP00000340989", "partner_gene": "SFN", "combined_score": 0.999, "scores": { "experimental": 0.981, "text_mining": 0.859, "database": 0.75, "coexpression": 0.0, "phylogenetic": 0.0, "gene_fusion": 0.0, "neighborhood": 0.0 }, "ncbi_taxon_id": 9606 } ] ``` ## Score Interpretation | Score Type | Source | Range | |------------|--------|-------| | `combined_score` | Weighted combination | 0-1 | | `experimental` | Wet-lab experiments | 0-1 | | `text_mining` | Literature co-occurrence | 0-1 | | `database` | Curated databases (BioGRID, etc.) | 0-1 | | `coexpression` | Expression correlation | 0-1 | | `phylogenetic` | Phylogenetic profiles | 0-1 | | `gene_fusion` | Fusion events | 0-1 | | `neighborhood` | Genomic proximity | 0-1 | ## Error Handling | Error | Solution | |-------|----------| | No interactions found | Lower `required_score` threshold | | UniProt ID not recognized | Verify ID format (e.g., P04637) | | Rate limited | Wait and retry; STRING allows ~5 req/sec | | Wrong species | Check NCBI taxonomy ID | ## Common Organism IDs | Organism | Taxonomy ID | |----------|-------------| | Human | 9606 | | Mouse | 10090 | | Rat | 10116 | | E. coli | 83333 | | S. cerevisiae | 4932 | ## References - `examples/basic_query.py` - Complete example script - `references/score_details.md` - Detailed score methodology - STRING API Docs: https://string-db.org/help/api/


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