agentleFS
PharMolixPharMolix/OpenBioMed/skills/ppi-string-query/SKILL.md

ppi-string-query

Skill1.1k starsChanged 8 months agoMITView on GitHub

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.

Trending

Quiet this year.thin dataabout the whole repository

Dec 2025: 0 mentionsJan 2026: 0 mentionsFeb 2026: 0 mentionsMar 2026: 0 mentionsApr 2026: 0 mentionsMay 2026: 0 mentionsJun 2026: 0 mentionsJul 2026: 0 mentionsAug 2026: 0 mentionsSep 2026: 0 mentionsOct 2026: 0 mentionsNov 2026: 0 mentions
Dec 2025Jun 2026Nov 2026
Mentions a month of the PharMolix/OpenBioMed repository that our weekly crawl found: only 1 in all, so this is a hint, not a trend. There is no adoption data for this file yet.

Who's talking about PharMolix/OpenBioMed

The biggest 1 of 1 mention linking to the repository that our crawl found. Each line is their own headline.

The file

Files
SKILL.md

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

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

# 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

ParameterTypeDefaultDescription
uniprot_idstrrequiredUniProt accession (e.g., P04637)
speciesint9606NCBI taxonomy ID (9606=human)
required_scoreint700Min confidence (150/400/700/900)
limitint50Max interactors to return

Confidence Score Thresholds

ScoreLevelUse Case
150LowExploratory analysis
400MediumBalanced retrieval
700HighReliable interactions (default)
900HighestVery confident only

Expected Output

[
  {
    "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 TypeSourceRange
combined_scoreWeighted combination0-1
experimentalWet-lab experiments0-1
text_miningLiterature co-occurrence0-1
databaseCurated databases (BioGRID, etc.)0-1
coexpressionExpression correlation0-1
phylogeneticPhylogenetic profiles0-1
gene_fusionFusion events0-1
neighborhoodGenomic proximity0-1

Error Handling

ErrorSolution
No interactions foundLower required_score threshold
UniProt ID not recognizedVerify ID format (e.g., P04637)
Rate limitedWait and retry; STRING allows ~5 req/sec
Wrong speciesCheck NCBI taxonomy ID

Common Organism IDs

OrganismTaxonomy ID
Human9606
Mouse10090
Rat10116
E. coli83333
S. cerevisiae4932

References

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.