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name: tooluniverse-protein-interactions description: Protein-protein interaction (PPI) network analysis — STRING (predicted + experimental), BioGRID (curated), SASBDB (small-angle scattering), Complex Portal (curated complex membership and stoichiometry, includes CORUM data). Distinguishes physical interactions (binding) from functional associations (co-expression, co-regulation). Use for interactome queries, complex partner identification, curated complex composition lookup, and pathway-level interaction analysis. disable-model-invocation: true

Protein Interaction Network Analysis

Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.

Domain Reasoning: Interaction Type Clarification

When asked about protein interactions, ask: physical interaction (do they bind?) or functional interaction (do they affect the same pathway)? STRING combines both — a high combined_score does not mean physical binding. For physical binding evidence, check the experimental score (escore) specifically. A high tscore (text mining) or dscore (database) with a low escore suggests co-annotation or co-citation, not direct binding.

LOOK UP DON'T GUESS: protein interaction scores, experimental evidence types, and whether two specific proteins have known co-crystal structures. Use STRING escore and BioGRID experimental data — do not infer binding from pathway co-membership alone.

Databases Used

Database	Coverage	API Key	Purpose
STRING	14M+ proteins, 5,000+ organisms	Not required	Primary interaction source
BioGRID	2.3M+ interactions, 80+ organisms	Required	Fallback, curated data
SASBDB	2,000+ SAXS/SANS entries	Not required	Solution structures
Complex Portal	Curated complexes (incl. CORUM data)	Not required	Curated complex membership/stoichiometry

4-Phase Workflow

1. Identifier Mapping — STRING_map_identifiers(): validate protein names, get STRING IDs
2. Network Retrieval — STRING_get_network() (primary); BioGRID_get_interactions() (fallback, requires API key)
3. Enrichment Analysis — STRING_functional_enrichment() for GO/KEGG/Reactome; STRING_ppi_enrichment() to test functional coherence
4. Structural Data (optional) — SASBDB_search_entries() for SAXS/SANS solution structures

See python_implementation.py for runnable examples (example_tp53_analysis(), analyze_protein_network()).

Complex Portal: Curated Complex Membership

STRING/BioGRID answer "do A and B interact"; Complex Portal answers a different question — "what is the full, curated membership and stoichiometry of a known biological complex." Use it when the question is about a specific named or implied multi-subunit assembly (e.g. "what's in the SAGA complex," "what regulates p53 via a complex") rather than a pairwise or network-level interaction.

Phase 1: ComplexPortal_search_complexes(query=..., species=..., number=...) -> ranked candidate complexes,
         each with complex_id, name, description, and a subunits[] list (identifier, name,
         interactor_type: "protein" or "small molecule" for bound cofactors like zinc)
Phase 2: ComplexPortal_get_complex(complex_id) -> full detail for one specific complex by its CPX- ID

Real example (verified live): ComplexPortal_search_complexes(query="TP53", species="Homo sapiens") -> real complexes including CPX-6093 "TP53-MDM2-MDM4 transcription regulation complex" (subunits P04637/Q00987/O15151) and two SAGA-complex variants (KAT2A/KAT2B) that recruit via TP53/MYC — real multi-subunit assemblies with real UniProt-identified subunits, including small-molecule cofactors (e.g. a zinc atom, CHEBI:27363) alongside protein subunits.

Gotcha (verified live): ComplexPortal_get_complex can return complex_id: null, name: null in its own echoed fields even on a successful lookup — use systematic_name and the subunits list to confirm you got the right complex, and keep the complex_id you already had from ComplexPortal_search_complexes rather than trusting the field echoed back by get_complex.

Parameters

Parameter	Default	Description
proteins	Required	Gene symbols or UniProt IDs
species	9606	NCBI taxonomy ID
confidence_score	0.7	Min interaction confidence (0–1)
include_biogrid	False	BioGRID fallback (requires API key)
include_structure	False	SASBDB structural data (slower)

Confidence Score Guidelines

Score	Use Case
0.4	Exploratory analysis (default STRING threshold)
0.7	Recommended — reliable interactions
0.9	Core interactions only

Network Edge Fields (STRING)

Key fields returned per interaction edge:

• score — combined confidence (0–1)
• escore — experimental score (use for physical binding evidence)
• dscore — database score
• tscore — text mining score
• ascore — coexpression score
• preferredName_A, preferredName_B — gene names

Extended Analysis Tools

Signaling Pathways:

• OmniPath_get_signaling_interactions — directed, signed PPI (stimulation/inhibition)
• Reactome_map_uniprot_to_pathways — map proteins to Reactome pathways (param: uniprot_id)
• ReactomeAnalysis_pathway_enrichment — pathway enrichment for gene sets
• SIGNOR (causal signaling, literature-backed) — see below

SIGNOR: Causal Signaling Interactions

STRING/BioGRID above tell you protein A and protein B interact (physically or functionally) but not the direction or effect. SIGNOR fills that gap: it is a manually curated database where every edge is a directional causal statement — "A up-/down-regulates B's activity/quantity via mechanism M (phosphorylation, ubiquitination, transcriptional regulation, binding, ...), optionally at residue R" — each backed by a specific PMID. It's the same kind of directed, signed edge as OmniPath_get_signaling_interactions, but from one curated source with per-edge mechanism/residue/literature detail rather than an aggregation across many databases; use both when you need convergent support for a causal claim.

Tools (all real, live-verified — entity_id/protein identifiers should be UniProt accessions, e.g. P00533 for EGFR, not gene symbols):

Tool	Purpose	Key params
SIGNOR_get_interactions	Upstream regulators + downstream targets of one protein	entity_id (UniProt acc), organism (taxid, default 9606), limit
SIGNOR_list_pathways	Browse curated signaling pathways (disease- or process-named)	query (optional keyword, e.g. "apoptosis", "MAPK")
SIGNOR_get_pathway	All causal interactions within one curated pathway	pathway_id (from list_pathways, e.g. "SIGNOR-EGF"), limit
SIGNOR_connect_proteins	Shortest curated causal sub-network linking 2+ proteins	proteins (array of UniProt accs, 2+), level (1=direct only, 2=one intermediate, 3=two intermediates), limit

Each interaction returns source_entity/target_entity (+ their UniProt source_id/target_id), effect (e.g. "down-regulates activity", "up-regulates quantity by expression"), mechanism (e.g. "phosphorylation", "ubiquitination", "binding", "transcriptional regulation" — can be an empty string when unspecified), residue (nullable), pmid, direct (bool — true = no known intermediate; SIGNOR's own connect_proteins output can mix direct: true and direct: false edges in one path, so check this field per-edge rather than assuming the whole path is direct), and a confidence-adjacent score.

Workflow — trace a causal path between two proteins:

SIGNOR_connect_proteins(proteins=["P00533", "P28482"], level=2)  # EGFR -> ERK2(MAPK1)

returns the curated sub-network connecting them (verified live: real edges include EGFR's own upstream regulators like ERRFI1 and PRKG2, and transcriptional links such as JAK2->MYC and LCK->STAT3 that co-occur in the 2-hop neighborhood — connect_proteins returns the local causal graph around the requested proteins, not only a single shortest path, so filter for edges that actually chain from source to target if you need one specific route).

Workflow — browse a disease/process pathway then inspect one protein's role in it:

SIGNOR_list_pathways(query="cancer")          # -> pathway_id, e.g. "SIGNOR-EGF" (EGFR Signaling)
SIGNOR_get_pathway(pathway_id="SIGNOR-EGF")   # -> all curated edges in that pathway (metadata.total_interactions gives the true count if limit truncates)
SIGNOR_get_interactions(entity_id="P00533")   # -> zoom into EGFR specifically: e.g. real edges show VCB-Cul2 ubiquitinating/destabilizing EGFR (PMID:15590694) and EGFR phosphorylating IKBKE (PMID:27287717)

Druggability & Clinical Context:

• DGIdb_get_drug_gene_interactions — drug interactions for hub proteins (param: genes as array)
• DGIdb_get_gene_druggability — druggability categories
• gnomad_get_gene_constraints — gene essentiality metrics (pLI, oe_lof)
• civic_search_evidence_items — clinical evidence for mutations in network proteins
• UniProt_get_function_by_accession — protein function annotation

Tool-Specific Notes

IntAct Interaction Data

interaction_ids are in the metadata field of the response, NOT at the top level:

interaction_ids = result.get("metadata", {}).get("interaction_ids", [])

BioGRID Chemical Interactions

BioGRID_get_chemical_interactions always includes a limitation note — chemical interaction coverage may be incomplete. Defaults to taxId=9606 (human) when no organism is provided.

IntAct protein_name Alias

IntAct tools accept protein_name as an alias parameter in addition to the original identifier parameter.

EBI Proteins-Gateway IntAct Access

EBIProteins_get_interactions(accession, limit=50) and EBIProteins_get_interaction_details(accession) are a second, concrete access path into the same IntAct-sourced interaction data referenced above (via the EBI Proteins API rather than IntAct's own endpoint) — use these when a plain UniProt-accession-in, ranked-partners-out call is more convenient than mapping identifiers through STRING/IntAct first.

Tool	Returns
EBIProteins_get_interactions	Binary interaction partners sorted by supporting-experiment count, each with partner_accession, gene_name, experiments (count), organism_differ (bool)
EBIProteins_get_interaction_details	The query protein's own record (name, existence evidence, organism, disease associations, subcellular locations) plus its top interaction partners in one call — useful when you need protein context and its network in a single request instead of two

Real example (verified live): EBIProteins_get_interactions(accession="P04637") (TP53) → 198 partners incl. MDM2 (Q00987), ABL1, AIMP2 (6 experiments), EP300 (8 experiments). EBIProteins_get_interaction_details(accession="P04637") → same interaction data plus real disease associations (e.g. Li-Fraumeni syndrome) and subcellular locations in one response — skip the separate UniProt_get_function_by_accession call above when disease/localization context is the only extra thing needed.

Domain Reasoning: Multimeric Assemblies & Binding Valency

LOOK UP DON'T GUESS: oligomeric state, subunit stoichiometry, and binding valency. Use RCSB PDB (RCSBAdvSearch_search_structures, RCSBData_get_entry) or UniProt (UniProt_get_function_by_accession) to confirm whether a protein is a monomer, dimer, trimer, etc. Do not assume from gene name alone.

Calculating Multimer Valency from Binding Data

Valency = number of independent binding sites on a multimeric complex. A homodimer with one binding site per subunit has valency 2. A pentamer (e.g., IgM) with 2 Fab arms each has valency 10.

Key reasoning steps:

1. Determine oligomeric state: Look up quaternary structure in PDB/UniProt. A "dimer" in solution may be a dimer-of-dimers (tetramer) crystallographically.
2. Count binding sites per subunit: Each subunit contributes independently unless the binding site spans the interface (then the complex itself is the functional unit).
3. Valency = subunits x sites_per_subunit (only if sites are independent). If binding at one site affects another, you have cooperativity, not simple valency.
4. Avidity vs affinity: A multivalent complex binds more tightly than a single site (avidity effect). Apparent Kd_multivalent << Kd_monovalent. The enhancement depends on linker flexibility and target geometry.

Statistical Factors in Multimeric Binding

When a symmetric multimer binds a ligand, statistical factors affect the apparent rate constants:

• First ligand binding: kon_apparent = n x kon_intrinsic (n equivalent sites available)
• First ligand dissociation: koff_apparent = koff_intrinsic (only one ligand to dissociate)
• General rule: For a multimer with n identical sites, binding to the i-th site has forward statistical factor (n - i + 1) and reverse statistical factor i.
• Macroscopic vs microscopic Kd: Kd_macro(1st site) = Kd_micro / n. Kd_macro(last site) = n x Kd_micro. The ratio Kd_last / Kd_first = n^2 for non-cooperative binding.

If measured Kd values deviate from these statistical predictions, the protein shows positive cooperativity (Kd decreases more than expected) or negative cooperativity (Kd increases more than expected).

When to Use Binding Curve Analysis vs Stoichiometry

Approach	Use when	What it tells you
Stoichiometry (ITC, AUC, SEC-MALS)	You need the number of binding partners per complex	n (sites), not affinity
Binding curves (SPR, FP, ELISA)	You need Kd and kinetics	Affinity, but apparent Kd conflates valency and cooperativity
Hill plot (log-log binding curve)	You suspect cooperativity	Hill coefficient nH: nH=1 non-cooperative, nH>1 positive, nH<1 negative
Scatchard plot (bound/free vs bound)	Classic approach, now less common	Curved = multiple site classes or cooperativity; linear = single Kd

Obligate vs facultative multimers: An obligate dimer (e.g., many kinases) has NO monomeric activity. If your "purified protein" shows no activity, check if dimer formation is required. Use SEC or native PAGE to confirm oligomeric state. Low protein concentration, high salt, or wrong pH can dissociate obligate multimers.

Domain Reasoning: Coiled-Coil Oligomeric State Prediction

• Heptad repeat: (abcdefg)n where positions a and d are hydrophobic core residues.
• Oligomeric state from packing: dimer (leucine zipper, Leu at d), trimer (Ile/Val at a, Leu at d), tetramer (Leu at both a+d), pentamer (complex mixed packing, e.g., Trp or polar residues at a).
• Heptad net diagram: map residues onto helical wheel; a+d form the hydrophobic core interface. The identity of a/d residues determines packing geometry and thus oligomeric state.
• Polar residues at a/d (Asn, Gln) specify parallel vs antiparallel orientation and can select for specific oligomeric states.
• LOOK UP: search PubMed for "[sequence motif] coiled coil oligomeric state" and check CC+ or SOCKET databases before predicting oligomeric state from sequence alone.

Domain Reasoning: Detergent Effects on Membrane Proteins

• Mild detergents (DDM, LMNG, CHAPS, digitonin) preserve native oligomeric state and lipid interactions; preferred for structural studies.
• Harsh detergents (SDS, OG at high concentration above CMC) can dissociate native complexes and strip stabilizing lipids.
• Native MS in different detergents reveals whether specific lipids stabilize oligomeric assemblies; comparing CHAPS vs OG results distinguishes detergent-stable from lipid-dependent oligomers.

Protein Identification Questions

For "what protein does X" questions: ALWAYS search UniProt and PubMed first — do not guess from memory. Key pathways to know:

• Amyloid clearance: collagen degradation by matrix metalloproteinases is required to expose amyloid deposits, allowing macrophage engulfment. The answer is collagen, not serum amyloid P (SAP) or other amyloid-binding proteins.
• When a question asks "what protein", give JUST the protein name — no abbreviations, descriptions, or qualifications.

Troubleshooting

• No interactions found: verify protein names (case-sensitive), try confidence_score=0.4
• BioGRID not working: set BIOGRID_API_KEY in environment; STRING works without a key
• Verbose output: filter with 2>&1 | grep -v "Error loading tools" (see KNOWN_ISSUES.md)

References

• STRING: https://string-db.org/
• BioGRID: https://thebiogrid.org/ (register for free API key)
• SASBDB: https://www.sasbdb.org/
• Complex Portal: https://www.ebi.ac.uk/complexportal/
• ToolUniverse: https://github.com/mims-harvard/ToolUniverse