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Protein-protein interaction (PPI) network analysis — STRING (predicted + experimental), BioGRID (curated), SASBDB (small-angle scattering). Distinguishes physical interactions (binding) from functional associations (co-expression, co-regulation). Use for interactome queries, complex partner identification, 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
4-Phase Workflow
Identifier Mapping — STRING_map_identifiers(): validate protein names, get STRING IDs
Network Retrieval — STRING_get_network() (primary); BioGRID_get_interactions() (fallback, requires API key)
Enrichment Analysis — STRING_functional_enrichment() for GO/KEGG/Reactome; STRING_ppi_enrichment() to test functional coherence
Structural Data (optional) — SASBDB_search_entries() for SAXS/SANS solution structures
See python_implementation.py for runnable examples (example_tp53_analysis(), analyze_protein_network()).
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
Druggability & Clinical Context:
DGIdb_get_drug_gene_interactions — drug interactions for hub proteins (param: genes as array)
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.
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:
Determine oligomeric state: Look up quaternary structure in PDB/UniProt. A "dimer" in solution may be a dimer-of-dimers (tetramer) crystallographically.
Count binding sites per subunit: Each subunit contributes independently unless the binding site spans the interface (then the complex itself is the functional unit).
Valency = subunits x sites_per_subunit (only if sites are independent). If binding at one site affects another, you have cooperativity, not simple valency.
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)
