tooluniverse-target-research
Originally frommims-harvard/tooluniverse
SKILL.md
Comprehensive Target Intelligence Gatherer
Gather complete target intelligence by exploring 9 parallel research paths. Supports targets identified by gene symbol, UniProt accession, Ensembl ID, or gene name.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Tool parameter verification - Verify params via
get_tool_infobefore calling unfamiliar tools - Evidence grading - Grade all claims by evidence strength (T1-T4)
- Citation requirements - Every fact must have inline source attribution
- Mandatory completeness - All sections must exist with data minimums or explicit "No data" notes
- Disambiguation first - Resolve all identifiers before research
- Negative results documented - "No drugs found" is data; empty sections are failures
- Collision-aware literature search - Detect and filter naming collisions
- English-first queries - Always use English terms in tool calls, even if the user writes in another language. Translate gene names, disease names, and search terms to English. Only try original-language terms as a fallback if English returns no results. Respond in the user's language
Phase 0: Tool Parameter Verification (CRITICAL)
BEFORE calling ANY tool for the first time, verify its parameters:
# Always check tool params to prevent silent failures
tool_info = tu.tools.get_tool_info(tool_name="Reactome_map_uniprot_to_pathways")
# Reveals: takes `id` not `uniprot_id`
Known Parameter Corrections (Updated)
| Tool | WRONG Parameter | CORRECT Parameter |
|---|---|---|
Reactome_map_uniprot_to_pathways |
uniprot_id |
id |
ensembl_get_xrefs |
gene_id |
id |
GTEx_get_median_gene_expression |
gencode_id only |
gencode_id + operation="median" |
OpenTargets_* |
ensemblID |
ensemblId (camelCase) |
GTEx Versioned ID Fallback (CRITICAL)
GTEx often requires versioned Ensembl IDs. If ENSG00000123456 returns empty:
# Step 1: Get gene info with version
gene_info = tu.tools.ensembl_lookup_gene(id=ensembl_id, species="human")
version = gene_info.get('version', 1)
# Step 2: Try versioned ID
versioned_id = f"{ensembl_id}.{version}" # e.g., "ENSG00000123456.12"
result = tu.tools.GTEx_get_median_gene_expression(
gencode_id=versioned_id,
operation="median"
)
When to Use This Skill
Apply when users:
- Ask about a drug target, protein, or gene
- Need target validation or assessment
- Request druggability analysis
- Want comprehensive target profiling
- Ask "what do we know about [target]?"
- Need target-disease associations
- Request safety profile for a target
Critical Workflow Requirements
1. Report-First Approach (MANDATORY)
DO NOT show the search process or tool outputs to the user. Instead:
-
Create the report file FIRST - Before any data collection:
- File name:
[TARGET]_target_report.md - Initialize with all 14 section headers
- Add placeholder:
[Researching...]in each section
- File name:
-
Progressively update the report - As you gather data:
- Update each section immediately after retrieving data
- Replace
[Researching...]with actual content - Include "No data returned" when tools return empty results
-
Methodology in appendix only - If user requests methodology details, create separate
[TARGET]_methods_appendix.md
2. Evidence Grading System (MANDATORY)
CRITICAL: Grade every claim by evidence strength.
Evidence Tiers
| Tier | Symbol | Criteria | Examples |
|---|---|---|---|
| T1 | ★★★ | Direct mechanistic evidence, human genetic proof | CRISPR KO, patient mutations, crystal structure with mechanism |
| T2 | ★★☆ | Functional studies, model organism validation | siRNA phenotype, mouse KO, biochemical assay |
| T3 | ★☆☆ | Association, screen hits, computational | GWAS hit, DepMap essentiality, expression correlation |
| T4 | ☆☆☆ | Mention, review, text-mined, predicted | Review article, database annotation, computational prediction |
Required Evidence Grading Locations
Evidence grades MUST appear in:
- Executive Summary - Key disease claims graded
- Section 8.2 Disease Associations - Every disease link graded with source type
- Section 11 Literature - Key papers table with evidence tier
- Section 13 Recommendations - Scorecard items reference evidence quality
Per-Section Evidence Summary
---
**Evidence Quality for this Section**: Strong
- Mechanistic (T1): 12 papers
- Functional (T2): 8 papers
- Association (T3): 15 papers
- Mention (T4): 23 papers
**Data Gaps**: No CRISPR data; mouse KO phenotypes limited
---
3. Citation Requirements (MANDATORY)
Every piece of information MUST include its source:
EGFR mutations cause lung adenocarcinoma [★★★: PMID:15118125, activating mutations
in patients]. *Source: ClinVar, CIViC*
Core Strategy: 9 Research Paths
Execute 9 research paths (Path 0 is always first):
Target Query (e.g., "EGFR" or "P00533")
│
├─ IDENTIFIER RESOLUTION (always first)
│ └─ Check if GPCR → GPCRdb_get_protein
│
├─ PATH 0: Open Targets Foundation (ALWAYS FIRST - fills gaps in all other paths)
│
├─ PATH 1: Core Identity (names, IDs, sequence, organism)
│ └─ InterProScan_scan_sequence for novel domain prediction (NEW)
├─ PATH 2: Structure & Domains (3D structure, domains, binding sites)
│ └─ If GPCR: GPCRdb_get_structures (active/inactive states)
├─ PATH 3: Function & Pathways (GO terms, pathways, biological role)
├─ PATH 4: Protein Interactions (PPI network, complexes)
├─ PATH 5: Expression Profile (tissue expression, single-cell)
├─ PATH 6: Variants & Disease (mutations, clinical significance)
│ └─ DisGeNET_search_gene for curated gene-disease associations
├─ PATH 7: Drug Interactions (known drugs, druggability, safety)
│ ├─ Pharos_get_target for TDL classification (Tclin/Tchem/Tbio/Tdark)
│ ├─ BindingDB_get_ligands_by_uniprot for known ligands (NEW)
│ ├─ PubChem_search_assays_by_target_gene for HTS data (NEW)
│ ├─ If GPCR: GPCRdb_get_ligands (curated agonists/antagonists)
│ └─ DepMap_get_gene_dependencies for target essentiality
└─ PATH 8: Literature & Research (publications, trends)
Identifier Resolution (Phase 1)
CRITICAL: Resolve ALL identifiers before any research path.
def resolve_target_ids(tu, query):
"""
Resolve target query to ALL needed identifiers.
Returns dict with: query, uniprot, ensembl, ensembl_version, symbol,
entrez, chembl_target, hgnc
"""
ids = {
'query': query,
'uniprot': None,
'ensembl': None,
'ensembl_versioned': None, # For GTEx
'symbol': None,
'entrez': None,
'chembl_target': None,
'hgnc': None,
'full_name': None,
'synonyms': []
}
# [Resolution logic based on input type]
# ... (see current implementation)
# CRITICAL: Get versioned Ensembl ID for GTEx
if ids['ensembl']:
gene_info = tu.tools.ensembl_lookup_gene(id=ids['ensembl'], species="human")
if gene_info and gene_info.get('version'):
ids['ensembl_versioned'] = f"{ids['ensembl']}.{gene_info['version']}"
# Also get synonyms for literature collision detection
ids['full_name'] = gene_info.get('description', '').split(' [')[0]
# Get UniProt alternative names for synonyms
if ids['uniprot']:
alt_names = tu.tools.UniProt_get_alternative_names_by_accession(accession=ids['uniprot'])
if alt_names:
ids['synonyms'].extend(alt_names)
return ids
GPCR Target Detection (NEW)
~35% of approved drugs target GPCRs. After identifier resolution, check if target is a GPCR:
def check_gpcr_target(tu, ids):
"""
Check if target is a GPCR and retrieve specialized data.
Call after identifier resolution.
"""
symbol = ids.get('symbol', '')
# Build GPCRdb entry name
entry_name = f"{symbol.lower()}_human"
gpcr_info = tu.tools.GPCRdb_get_protein(
operation="get_protein",
protein=entry_name
)
if gpcr_info.get('status') == 'success':
# Target is a GPCR - get specialized data
# Get structures with receptor state
structures = tu.tools.GPCRdb_get_structures(
operation="get_structures",
protein=entry_name
)
# Get known ligands (critical for binder projects)
ligands = tu.tools.GPCRdb_get_ligands(
operation="get_ligands",
protein=entry_name
)
# Get mutation data
mutations = tu.tools.GPCRdb_get_mutations(
operation="get_mutations",
protein=entry_name
)
return {
'is_gpcr': True,
'gpcr_family': gpcr_info['data'].get('family'),
'gpcr_class': gpcr_info['data'].get('receptor_class'),
'structures': structures.get('data', {}).get('structures', []),
'ligands': ligands.get('data', {}).get('ligands', []),
'mutations': mutations.get('data', {}).get('mutations', []),
'ballesteros_numbering': True # GPCRdb provides this
}
return {'is_gpcr': False}
GPCRdb Report Section (add to Section 2 for GPCR targets):
### 2.x GPCR-Specific Data (GPCRdb)
**Receptor Class**: Class A (Rhodopsin-like)
**GPCR Family**: Adrenoceptors
**Structures by State**:
| PDB ID | State | Resolution | Ligand | Year |
|--------|-------|------------|--------|------|
| 3SN6 | Active | 3.2Å | Agonist (BI-167107) | 2011 |
| 2RH1 | Inactive | 2.4Å | Antagonist (carazolol) | 2007 |
**Known Ligands**: 45 agonists, 32 antagonists, 8 allosteric modulators
**Key Binding Site Residues** (Ballesteros-Weinstein): 3.32, 5.42, 6.48, 7.39
Collision Detection for Literature Search
Before literature search, detect naming collisions:
def detect_collisions(tu, symbol, full_name):
"""
Detect if gene symbol has naming collisions in literature.
Returns negative filter terms if collisions found.
"""
# Search by symbol in title
results = tu.tools.PubMed_search_articles(
query=f'"{symbol}"[Title]',
limit=20
)
# Check if >20% are off-topic
off_topic_terms = []
for paper in results.get('articles', []):
title = paper.get('title', '').lower()
# Check if title mentions biology/protein/gene context
bio_terms = ['protein', 'gene', 'cell', 'expression', 'mutation', 'kinase', 'receptor']
if not any(term in title for term in bio_terms):
# Extract potential collision terms
# e.g., "JAK" might collide with "Just Another Kinase" jokes
# e.g., "WDR7" might collide with other WDR family members in certain contexts
pass
# Build negative filter
collision_filter = ""
if off_topic_terms:
collision_filter = " NOT " + " NOT ".join(off_topic_terms)
return collision_filter
PATH 0: Open Targets Foundation (ALWAYS FIRST)
Objective: Populate baseline data for Sections 5, 8, 9, 10, 11 before specialized queries.
CRITICAL: Open Targets provides the most comprehensive aggregated data. Query ALL these endpoints:
| Endpoint | Section | Data Type |
|---|---|---|
OpenTargets_get_diseases_phenotypes_by_target_ensemblId |
8 | Diseases/phenotypes |
OpenTargets_get_target_tractability_by_ensemblId |
9 | Druggability assessment |
OpenTargets_get_target_safety_profile_by_ensemblId |
10 | Safety liabilities |
OpenTargets_get_target_interactions_by_ensemblId |
6 | PPI network |
OpenTargets_get_target_gene_ontology_by_ensemblId |
5 | GO annotations |
OpenTargets_get_publications_by_target_ensemblId |
11 | Literature |
OpenTargets_get_biological_mouse_models_by_ensemblId |
8/10 | Mouse KO phenotypes |
OpenTargets_get_chemical_probes_by_target_ensemblId |
9 | Chemical probes |
OpenTargets_get_associated_drugs_by_target_ensemblId |
9 | Known drugs |
Path 0 Implementation
def path_0_open_targets(tu, ids):
"""
Open Targets foundation data - fills gaps for sections 5, 6, 8, 9, 10, 11.
ALWAYS run this first.
"""
ensembl_id = ids['ensembl']
if not ensembl_id:
return {'status': 'skipped', 'reason': 'No Ensembl ID'}
results = {}
# 1. Diseases & Phenotypes (Section 8)
diseases = tu.tools.OpenTargets_get_diseases_phenotypes_by_target_ensemblId(
ensemblId=ensembl_id
)
results['diseases'] = diseases if diseases else {'note': 'No disease associations returned'}
# 2. Tractability (Section 9)
tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblId(
ensemblId=ensembl_id
)
results['tractability'] = tractability if tractability else {'note': 'No tractability data returned'}
# 3. Safety Profile (Section 10)
safety = tu.tools.OpenTargets_get_target_safety_profile_by_ensemblId(
ensemblId=ensembl_id
)
results['safety'] = safety if safety else {'note': 'No safety liabilities identified'}
# 4. Interactions (Section 6)
interactions = tu.tools.OpenTargets_get_target_interactions_by_ensemblId(
ensemblId=ensembl_id
)
results['interactions'] = interactions if interactions else {'note': 'No interactions returned'}
# 5. GO Annotations (Section 5)
go_terms = tu.tools.OpenTargets_get_target_gene_ontology_by_ensemblId(
ensemblId=ensembl_id
)
results['go_terms'] = go_terms if go_terms else {'note': 'No GO annotations returned'}
# 6. Publications (Section 11)
publications = tu.tools.OpenTargets_get_publications_by_target_ensemblId(
ensemblId=ensembl_id
)
results['publications'] = publications if publications else {'note': 'No publications returned'}
# 7. Mouse Models (Section 8/10)
mouse_models = tu.tools.OpenTargets_get_biological_mouse_models_by_ensemblId(
ensemblId=ensembl_id
)
results['mouse_models'] = mouse_models if mouse_models else {'note': 'No mouse model data returned'}
# 8. Chemical Probes (Section 9)
probes = tu.tools.OpenTargets_get_chemical_probes_by_target_ensemblId(
ensemblId=ensembl_id
)
results['chemical_probes'] = probes if probes else {'note': 'No chemical probes available'}
# 9. Associated Drugs (Section 9)
drugs = tu.tools.OpenTargets_get_associated_drugs_by_target_ensemblId(
ensemblId=ensembl_id
)
results['drugs'] = drugs if drugs else {'note': 'No approved/trial drugs found'}
return results
Negative Results Are Data
CRITICAL: Always document when a query returns empty:
### 9.3 Chemical Probes
**Status**: No validated chemical probes available for this target.
*Source: OpenTargets_get_chemical_probes_by_target_ensemblId returned empty*
**Implication**: Tool compound development would be needed for chemical biology studies.
PATH 2: Structure & Domains (Enhanced)
Objective: Robust structure coverage using 3-step chain.
3-Step Structure Search Chain
Do NOT rely solely on PDB text search. Use this chain:
def path_structure_robust(tu, ids):
"""
Robust structure search using 3-step chain.
"""
structures = {'pdb': [], 'alphafold': None, 'domains': [], 'method_notes': []}
# STEP 1: UniProt PDB Cross-References (most reliable)
if ids['uniprot']:
entry = tu.tools.UniProt_get_entry_by_accession(accession=ids['uniprot'])
pdb_xrefs = [x for x in entry.get('uniProtKBCrossReferences', [])
if x.get('database') == 'PDB']
for xref in pdb_xrefs:
pdb_id = xref.get('id')
# Get details for each PDB
pdb_info = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id)
if pdb_info:
structures['pdb'].append(pdb_info)
structures['method_notes'].append(f"Step 1: {len(pdb_xrefs)} PDB cross-refs from UniProt")
# STEP 2: Sequence-based PDB Search (catches missing annotations)
if ids['uniprot'] and len(structures['pdb']) < 5:
sequence = tu.tools.UniProt_get_sequence_by_accession(accession=ids['uniprot'])
if sequence and len(sequence) < 1000: # Reasonable length for search
similar = tu.tools.PDB_search_similar_structures(
sequence=sequence[:500], # Use first 500 AA if long
identity_cutoff=0.7
)
if similar:
for hit in similar[:10]: # Top 10 similar
if hit['pdb_id'] not in [s.get('pdb_id') for s in structures['pdb']]:
structures['pdb'].append(hit)
structures['method_notes'].append(f"Step 2: Sequence search (identity ≥70%)")
# STEP 3: Domain-based Search (for multi-domain proteins)
if ids['uniprot']:
domains = tu.tools.InterPro_get_protein_domains(uniprot_accession=ids['uniprot'])
structures['domains'] = domains if domains else []
# For large proteins with domains, search by domain sequence windows
if len(structures['pdb']) < 3 and domains:
for domain in domains[:3]: # Top 3 domains
domain_name = domain.get('name', '')
# Could search PDB by domain name
domain_hits = tu.tools.PDB_search_by_keyword(query=domain_name, limit=5)
if domain_hits:
structures['method_notes'].append(f"Step 3: Domain '{domain_name}' search")
# AlphaFold (always check)
alphafold = tu.tools.alphafold_get_prediction(uniprot_accession=ids['uniprot'])
structures['alphafold'] = alphafold if alphafold else {'note': 'No AlphaFold prediction'}
# IMPORTANT: Document limitations
if not structures['pdb']:
structures['limitation'] = "No direct PDB hit does NOT mean no structure exists. Check: (1) structures under different UniProt entries, (2) homolog structures, (3) domain-only structures."
return structures
Structure Section Output Format
### 4.1 Experimental Structures (PDB)
**Total PDB Entries**: 23 structures *(Source: UniProt cross-references)*
**Search Method**: 3-step chain (UniProt xrefs → sequence search → domain search)
| PDB ID | Resolution | Method | Ligand | Coverage | Year |
|--------|------------|--------|--------|----------|------|
| 1M17 | 2.6Å | X-ray | Erlotinib | 672-998 | 2002 |
| 3POZ | 2.8Å | X-ray | Gefitinib | 696-1022 | 2010 |
**Note**: "No direct PDB hit" ≠ "no structure exists". Check homologs and domain structures.
PATH 5: Expression Profile (Enhanced)
GTEx with Versioned ID Fallback
def path_expression(tu, ids):
"""
Expression data with GTEx versioned ID fallback.
"""
results = {'gtex': None, 'hpa': None, 'failed_tools': []}
# GTEx with fallback
ensembl_id = ids['ensembl']
versioned_id = ids.get('ensembl_versioned')
# Try unversioned first
gtex_result = tu.tools.GTEx_get_median_gene_expression(
gencode_id=ensembl_id,
operation="median"
)
# Fallback to versioned if empty
if not gtex_result or gtex_result.get('data') == []:
if versioned_id:
gtex_result = tu.tools.GTEx_get_median_gene_expression(
gencode_id=versioned_id,
operation="median"
)
if gtex_result and gtex_result.get('data'):
results['gtex'] = gtex_result
results['gtex_note'] = f"Used versioned ID: {versioned_id}"
if not results.get('gtex'):
results['failed_tools'].append({
'tool': 'GTEx_get_median_gene_expression',
'tried': [ensembl_id, versioned_id],
'fallback': 'See HPA data below'
})
else:
results['gtex'] = gtex_result
# HPA (always query as backup)
hpa_result = tu.tools.HPA_get_rna_expression_by_source(ensembl_id=ensembl_id)
results['hpa'] = hpa_result if hpa_result else {'note': 'No HPA RNA data'}
return results
Human Protein Atlas - Extended Expression (NEW)
HPA provides comprehensive protein expression data including tissue-level, cell-level, and cell line expression.
def get_hpa_comprehensive_expression(tu, gene_symbol):
"""
Get comprehensive expression data from Human Protein Atlas.
Provides:
- Tissue expression (protein and RNA)
- Subcellular localization
- Cell line expression comparison
- Tissue specificity
"""
# 1. Search for gene to get IDs
gene_info = tu.tools.HPA_search_genes_by_query(search_query=gene_symbol)
if not gene_info:
return {'error': f'Gene {gene_symbol} not found in HPA'}
# 2. Get tissue expression with specificity
tissue_search = tu.tools.HPA_generic_search(
search_query=gene_symbol,
columns="g,gs,rnat,rnatsm,scml,scal", # Gene, synonyms, tissue specificity, subcellular
format="json"
)
# 3. Compare expression in cancer cell lines vs normal tissue
cell_lines = ['a549', 'mcf7', 'hela', 'hepg2', 'pc3']
cell_line_expression = {}
for cell_line in cell_lines:
try:
expr = tu.tools.HPA_get_comparative_expression_by_gene_and_cellline(
gene_name=gene_symbol,
cell_line=cell_line
)
cell_line_expression[cell_line] = expr
except:
continue
return {
'gene_info': gene_info,
'tissue_data': tissue_search,
'cell_line_expression': cell_line_expression,
'source': 'Human Protein Atlas'
}
HPA Expression Output for Report:
### Tissue Expression Profile (Human Protein Atlas)
| Tissue | Protein Level | RNA nTPM | Specificity |
|--------|---------------|----------|-------------|
| Brain | High | 45.2 | Enriched |
| Liver | Medium | 23.1 | Enhanced |
| Kidney | Low | 8.4 | Not detected |
**Subcellular Localization**: Cytoplasm, Plasma membrane
### Cancer Cell Line Expression
| Cell Line | Cancer Type | Expression | vs Normal |
|-----------|-------------|------------|-----------|
| A549 | Lung | High | Elevated |
| MCF7 | Breast | Medium | Similar |
| HeLa | Cervical | High | Elevated |
*Source: Human Protein Atlas via `HPA_search_genes_by_query`, `HPA_get_comparative_expression_by_gene_and_cellline`*
Why HPA for Target Research:
- Drug target validation - Confirm expression in target tissue
- Safety assessment - Expression in essential organs
- Biomarker potential - Tissue-specific expression
- Cell line selection - Choose appropriate models
PATH 6: Variants & Disease (Enhanced)
6.1 ClinVar SNV vs CNV Separation
### 8.3 Clinical Variants (ClinVar)
#### Single Nucleotide Variants (SNVs)
| Variant | Clinical Significance | Condition | Review Status | PMID |
|---------|----------------------|-----------|---------------|------|
| p.L858R | Pathogenic | Lung cancer | 4 stars | 15118125 |
| p.T790M | Pathogenic | Drug resistance | 4 stars | 15737014 |
**Total Pathogenic SNVs**: 47
#### Copy Number Variants (CNVs) - Reported Separately
| Type | Region | Clinical Significance | Frequency |
|------|--------|----------------------|-----------|
| Amplification | 7p11.2 | Pathogenic | Common in cancer |
*Note: CNV data separated as it represents different mutation mechanism*
6.2 DisGeNET Integration (NEW)
DisGeNET provides curated gene-disease associations with evidence scores. Requires: DISGENET_API_KEY
def get_disgenet_associations(tu, ids):
"""
Get gene-disease associations from DisGeNET.
Complements Open Targets with curated association scores.
"""
symbol = ids.get('symbol')
if not symbol:
return {'status': 'skipped', 'reason': 'No gene symbol'}
# Get all disease associations for gene
gda = tu.tools.DisGeNET_search_gene(
operation="search_gene",
gene=symbol,
limit=50
)
if gda.get('status') != 'success':
return {'status': 'error', 'message': 'DisGeNET query failed'}
associations = gda.get('data', {}).get('associations', [])
# Categorize by evidence strength
strong = [] # score >= 0.7
moderate = [] # score 0.4-0.7
weak = [] # score < 0.4
for assoc in associations:
score = assoc.get('score', 0)
disease_name = assoc.get('disease_name', '')
umls_cui = assoc.get('disease_id', '')
entry = {
'disease': disease_name,
'umls_cui': umls_cui,
'score': score,
'evidence_index': assoc.get('ei'),
'dsi': assoc.get('dsi'), # Disease Specificity Index
'dpi': assoc.get('dpi') # Disease Pleiotropy Index
}
if score >= 0.7:
strong.append(entry)
elif score >= 0.4:
moderate.append(entry)
else:
weak.append(entry)
return {
'total_associations': len(associations),
'strong_associations': strong,
'moderate_associations': moderate,
'weak_associations': weak[:10], # Limit weak
'disease_pleiotropy': len(associations) # How many diseases linked
}
DisGeNET Report Section (add to Section 8 - Disease Associations):
### 8.x DisGeNET Gene-Disease Associations (NEW)
**Total Diseases Associated**: 47
**Disease Pleiotropy Index**: High (gene linked to many disease types)
#### Strong Associations (Score ≥0.7)
| Disease | UMLS CUI | Score | Evidence Index |
|---------|----------|-------|----------------|
| Non-small cell lung cancer | C0007131 | 0.85 | 0.92 |
| Glioblastoma | C0017636 | 0.78 | 0.88 |
#### Moderate Associations (Score 0.4-0.7)
| Disease | UMLS CUI | Score | DSI |
|---------|----------|-------|-----|
| Breast cancer | C0006142 | 0.62 | 0.45 |
*Note: DisGeNET score integrates curated databases, GWAS, animal models, and literature*
Evidence Tier Assignment:
- DisGeNET Score ≥0.7 → Consider T2 evidence (multiple validated sources)
- DisGeNET Score 0.4-0.7 → Consider T3 evidence
- DisGeNET Score <0.4 → T4 evidence only
PATH 7: Druggability & Target Validation (ENHANCED)
7.1 Pharos/TCRD - Target Development Level (NEW)
NIH's Illuminating the Druggable Genome (IDG) portal provides TDL classification for all human proteins:
def get_pharos_target_info(tu, ids):
"""
Get Pharos/TCRD target development level and druggability.
TDL Classification:
- Tclin: Approved drug targets
- Tchem: Targets with small molecule activities (IC50 < 30nM)
- Tbio: Targets with biological annotations
- Tdark: Understudied proteins
"""
gene_symbol = ids.get('symbol')
uniprot = ids.get('uniprot')
# Try by gene symbol first
if gene_symbol:
result = tu.tools.Pharos_get_target(
gene=gene_symbol
)
elif uniprot:
result = tu.tools.Pharos_get_target(
uniprot=uniprot
)
else:
return {'status': 'error', 'message': 'Need gene symbol or UniProt'}
if result.get('status') == 'success' and result.get('data'):
target = result['data']
return {
'name': target.get('name'),
'symbol': target.get('sym'),
'tdl': target.get('tdl'), # Tclin/Tchem/Tbio/Tdark
'family': target.get('fam'), # Kinase, GPCR, etc.
'novelty': target.get('novelty'),
'description': target.get('description'),
'publications': target.get('publicationCount'),
'interpretation': interpret_tdl(target.get('tdl'))
}
return None
def interpret_tdl(tdl):
"""Interpret Target Development Level for druggability."""
interpretations = {
'Tclin': 'Approved drug target - highest confidence for druggability',
'Tchem': 'Small molecule active - good chemical tractability',
'Tbio': 'Biologically characterized - may require novel modalities',
'Tdark': 'Understudied - limited data, high novelty potential'
}
return interpretations.get(tdl, 'Unknown')
def search_disease_targets(tu, disease_name):
"""Find targets associated with a disease via Pharos."""
result = tu.tools.Pharos_get_disease_targets(
disease=disease_name,
top=50
)
if result.get('status') == 'success':
targets = result['data'].get('targets', [])
# Group by TDL for prioritization
by_tdl = {'Tclin': [], 'Tchem': [], 'Tbio': [], 'Tdark': []}
for t in targets:
tdl = t.get('tdl', 'Unknown')
if tdl in by_tdl:
by_tdl[tdl].append(t)
return by_tdl
return None
Pharos Report Section (add to Section 9 - Druggability):
### 9.x Pharos/TCRD Target Classification (NEW)
**Target Development Level**: Tchem
**Protein Family**: Kinase
**Novelty Score**: 0.35 (moderately studied)
**Publication Count**: 12,456
**TDL Interpretation**: Target has validated small molecule activities with IC50 < 30nM. Good chemical starting points exist.
**Disease Targets Analysis** (for disease-centric queries):
| TDL | Count | Examples |
|-----|-------|----------|
| Tclin | 12 | EGFR, ALK, RET |
| Tchem | 45 | KRAS, SHP2, CDK4 |
| Tbio | 78 | Novel kinases |
| Tdark | 23 | Understudied |
*Source: Pharos/TCRD via `Pharos_get_target`*
7.2 DepMap - Target Essentiality Validation (NEW)
CRISPR knockout data from cancer cell lines to validate target essentiality:
def assess_target_essentiality(tu, ids):
"""
Is this target essential for cancer cell survival?
Negative effect scores = gene is essential (cells die upon KO)
"""
gene_symbol = ids.get('symbol')
if not gene_symbol:
return {'status': 'error', 'message': 'Need gene symbol'}
deps = tu.tools.DepMap_get_gene_dependencies(
gene_symbol=gene_symbol
)
if deps.get('status') == 'success':
return {
'gene': gene_symbol,
'data': deps.get('data', {}),
'interpretation': 'Negative scores indicate gene is essential for cell survival',
'note': 'Score < -0.5 is strongly essential, < -1.0 is extremely essential'
}
return None
def get_cancer_type_essentiality(tu, gene_symbol, cancer_type):
"""Check if gene is essential in specific cancer type."""
# Get cell lines for cancer type
cell_lines = tu.tools.DepMap_get_cell_lines(
cancer_type=cancer_type,
page_size=20
)
return {
'gene': gene_symbol,
'cancer_type': cancer_type,
'cell_lines': cell_lines.get('data', {}).get('cell_lines', []),
'note': 'Query individual cell lines for dependency scores via DepMap portal'
}
DepMap Report Section (add to Section 9 - Druggability):
### 9.x Target Essentiality (DepMap) (NEW)
**Gene Essentiality Assessment**:
| Context | Effect Score | Interpretation |
|---------|--------------|----------------|
| Pan-cancer | -0.42 | Moderately essential |
| Lung cancer | -0.78 | Strongly essential |
| Breast cancer | -0.21 | Weakly essential |
**Selectivity**: Differential essentiality suggests cancer-type selective target
**Cell Lines Tested**: 1,054 cancer cell lines from DepMap
*Interpretation*: Score < -0.5 indicates strong dependency. This target is more essential in lung cancer than other cancer types - suggesting lung-selective targeting may be feasible.
*Source: DepMap via `DepMap_get_gene_dependencies`*
7.3 InterProScan - Novel Domain Prediction (NEW)
For uncharacterized proteins, run InterProScan to predict domains and function:
def predict_protein_domains(tu, sequence, title="Query protein"):
"""
Run InterProScan for de novo domain prediction.
Use when:
- Protein has no InterPro annotations
- Novel/uncharacterized protein
- Custom sequence analysis
"""
result = tu.tools.InterProScan_scan_sequence(
sequence=sequence,
title=title,
go_terms=True,
pathways=True
)
if result.get('status') == 'success':
data = result.get('data', {})
# Job may still be running
if data.get('job_status') == 'RUNNING':
return {
'job_id': data.get('job_id'),
'status': 'running',
'note': 'Use InterProScan_get_job_results to retrieve when ready'
}
# Parse completed results
return {
'domains': data.get('domains', []),
'domain_count': data.get('domain_count', 0),
'go_annotations': data.get('go_annotations', []),
'pathways': data.get('pathways', []),
'sequence_length': data.get('sequence_length')
}
return None
def check_interproscan_job(tu, job_id):
"""Check status and get results for InterProScan job."""
status = tu.tools.InterProScan_get_job_status(job_id=job_id)
if status.get('data', {}).get('is_finished'):
results = tu.tools.InterProScan_get_job_results(job_id=job_id)
return results.get('data', {})
return status.get('data', {})
When to use InterProScan:
- Novel/uncharacterized proteins (Tdark in Pharos)
- Custom sequences (e.g., protein variants)
- Proteins with outdated/sparse InterPro annotations
- Validating domain predictions
InterProScan Report Section (for novel proteins):
### Domain Prediction (InterProScan) (NEW)
*Used for uncharacterized protein analysis*
**Predicted Domains**:
| Domain | Database | Start-End | E-value | InterPro Entry |
|--------|----------|-----------|---------|----------------|
| Protein kinase domain | Pfam | 45-305 | 1.2e-89 | IPR000719 |
| SH2 domain | SMART | 320-410 | 3.4e-45 | IPR000980 |
**Predicted GO Terms**:
- GO:0004672 protein kinase activity
- GO:0005524 ATP binding
**Predicted Pathways**:
- Reactome: Signal Transduction
*Source: InterProScan via `InterProScan_scan_sequence`*
7.4 BindingDB - Known Ligands & Binding Data (NEW)
BindingDB provides experimental binding affinity data (Ki, IC50, Kd) for target-ligand pairs:
def get_bindingdb_ligands(tu, uniprot_id, affinity_cutoff=10000):
"""
Get ligands with measured binding affinities from BindingDB.
Critical for:
- Identifying chemical starting points
- Understanding existing chemical matter
- Assessing tractability with small molecules
Args:
uniprot_id: UniProt accession (e.g., P00533 for EGFR)
affinity_cutoff: Maximum affinity in nM (lower = more potent)
"""
# Get ligands by UniProt
result = tu.tools.BindingDB_get_ligands_by_uniprot(
uniprot=uniprot_id,
affinity_cutoff=affinity_cutoff
)
if result:
ligands = []
for entry in result:
ligands.append({
'smiles': entry.get('smile'),
'affinity_type': entry.get('affinity_type'), # Ki, IC50, Kd
'affinity_nM': entry.get('affinity'),
'monomer_id': entry.get('monomerid'),
'pmid': entry.get('pmid')
})
# Sort by affinity (most potent first)
ligands.sort(key=lambda x: float(x['affinity_nM']) if x['affinity_nM'] else float('inf'))
return {
'total_ligands': len(ligands),
'ligands': ligands[:20], # Top 20 most potent
'best_affinity': ligands[0]['affinity_nM'] if ligands else None
}
return {'total_ligands': 0, 'ligands': [], 'note': 'No ligands found in BindingDB'}
def get_ligands_by_structure(tu, pdb_id, affinity_cutoff=10000):
"""Get ligands for a protein by PDB structure ID."""
result = tu.tools.BindingDB_get_ligands_by_pdb(
pdb_ids=pdb_id,
affinity_cutoff=affinity_cutoff,
sequence_identity=100
)
return result
def find_compound_targets(tu, smiles, similarity_cutoff=0.85):
"""Find other targets for a compound (polypharmacology)."""
result = tu.tools.BindingDB_get_targets_by_compound(
smiles=smiles,
similarity_cutoff=similarity_cutoff
)
return result
BindingDB Report Section (add to Section 9 - Druggability):
### Known Ligands (BindingDB) (NEW)
**Total Ligands with Binding Data**: 156
**Best Reported Affinity**: 0.3 nM (Ki)
#### Most Potent Ligands
| SMILES | Affinity Type | Value (nM) | Source PMID |
|--------|---------------|------------|-------------|
| CC(=O)Nc1ccc(cc1)c2... | Ki | 0.3 | 15737014 |
| CN(C)C/C=C/C(=O)Nc1... | IC50 | 0.8 | 15896103 |
| COc1cc2ncnc(Nc3ccc... | Kd | 2.1 | 16460808 |
**Chemical Tractability Assessment**:
- ✅ **Tchem-level target**: Multiple ligands with <30nM affinity
- ✅ **Diverse chemotypes**: Multiple scaffolds identified
- ✅ **Published literature**: Ligands have PMID references
*Source: BindingDB via `BindingDB_get_ligands_by_uniprot`*
Affinity Interpretation for Druggability:
| Affinity Range | Interpretation | Drug Development Potential |
|---|---|---|
| <1 nM | Ultra-potent | Clinical compound likely |
| 1-10 nM | Highly potent | Drug-like |
| 10-100 nM | Potent | Good starting point |
| 100-1000 nM | Moderate | Needs optimization |
| >1000 nM | Weak | Early hit only |
7.5 PubChem BioAssay - Screening Data (NEW)
PubChem BioAssay provides HTS screening data and dose-response curves:
def get_pubchem_assays_for_target(tu, gene_symbol):
"""
Get bioassays targeting a gene from PubChem.
Provides:
- HTS screening results
- Dose-response data (IC50/EC50)
- Active compound counts
"""
# Search assays by target gene
assays = tu.tools.PubChem_search_assays_by_target_gene(
gene_symbol=gene_symbol
)
assay_info = []
if assays.get('data', {}).get('aids'):
for aid in assays['data']['aids'][:10]: # Top 10 assays
# Get assay details
summary = tu.tools.PubChem_get_assay_summary(aid=aid)
targets = tu.tools.PubChem_get_assay_targets(aid=aid)
assay_info.append({
'aid': aid,
'summary': summary.get('data', {}),
'targets': targets.get('data', {})
})
return {
'total_assays': len(assays.get('data', {}).get('aids', [])),
'assay_details': assay_info
}
def get_active_compounds_from_assay(tu, aid):
"""Get active compounds from a specific bioassay."""
actives = tu.tools.PubChem_get_assay_active_compounds(aid=aid)
return {
'aid': aid,
'active_cids': actives.get('data', {}).get('cids', []),
'count': len(actives.get('data', {}).get('cids', []))
}
PubChem BioAssay Report Section:
### PubChem BioAssay Data (NEW)
**Assays Targeting This Gene**: 45
| AID | Assay Type | Active Compounds | Target Info |
|-----|------------|------------------|-------------|
| 1053104 | Dose-response | 12 | EGFR kinase |
| 504526 | HTS | 234 | EGFR binding |
| 651564 | Confirmatory | 8 | EGFR cellular |
**Total Active Compounds Across Assays**: ~500
*Source: PubChem via `PubChem_search_assays_by_target_gene`, `PubChem_get_assay_active_compounds`*
PATH 8: Literature & Research (Collision-Aware)
Collision-Aware Query Strategy
def path_literature_collision_aware(tu, ids):
"""
Literature search with collision detection and filtering.
"""
symbol = ids['symbol']
full_name = ids.get('full_name', '')
uniprot = ids['uniprot']
synonyms = ids.get('synonyms', [])
# Step 1: Detect collisions
collision_filter = detect_collisions(tu, symbol, full_name)
# Step 2: Build high-precision seed queries
seed_queries = [
f'"{symbol}"[Title] AND (protein OR gene OR expression)', # Symbol in title
f'"{full_name}"[Title]' if full_name else None, # Full name in title
f'"UniProt:{uniprot}"' if uniprot else None, # UniProt accession
]
seed_queries = [q for q in seed_queries if q]
# Add key synonyms
for syn in synonyms[:3]:
seed_queries.append(f'"{syn}"[Title]')
# Step 3: Execute seed queries and collect PMIDs
seed_pmids = set()
for query in seed_queries:
if collision_filter:
query = f"({query}){collision_filter}"
results = tu.tools.PubMed_search_articles(query=query, limit=30)
for article in results.get('articles', []):
seed_pmids.add(article.get('pmid'))
# Step 4: Expand via citation network (for sparse targets)
if len(seed_pmids) < 30:
expanded_pmids = set()
for pmid in list(seed_pmids)[:10]: # Top 10 seeds
# Get related articles
related = tu.tools.PubMed_get_related(pmid=pmid, limit=20)
for r in related.get('articles', []):
expanded_pmids.add(r.get('pmid'))
# Get citing articles
citing = tu.tools.EuropePMC_get_citations(pmid=pmid, limit=20)
for c in citing.get('citations', []):
expanded_pmids.add(c.get('pmid'))
seed_pmids.update(expanded_pmids)
# Step 5: Classify papers by evidence tier
papers_by_tier = {'T1': [], 'T2': [], 'T3': [], 'T4': []}
# ... classification logic based on title/abstract keywords
return {
'total_papers': len(seed_pmids),
'collision_filter_applied': collision_filter if collision_filter else 'None needed',
'seed_queries': seed_queries,
'papers_by_tier': papers_by_tier
}
Retry Logic & Fallback Chains
Retry Policy
For each critical tool, implement retry with exponential backoff:
def call_with_retry(tu, tool_name, params, max_retries=3):
"""
Call tool with retry logic.
"""
for attempt in range(max_retries):
try:
result = getattr(tu.tools, tool_name)(**params)
if result and not result.get('error'):
return result
except Exception as e:
if attempt < max_retries - 1:
time.sleep(2 ** attempt) # Exponential backoff
else:
return {'error': str(e), 'tool': tool_name, 'attempts': max_retries}
return None
Fallback Chains (CRITICAL)
| Primary Tool | Fallback 1 | Fallback 2 | Failure Action |
|---|---|---|---|
ChEMBL_get_target_activities |
GtoPdb_get_target_ligands |
OpenTargets drugs |
Note in report |
intact_get_interactions |
STRING_get_protein_interactions |
OpenTargets interactions |
Note in report |
GO_get_annotations_for_gene |
OpenTargets GO |
MyGene GO |
Note in report |
GTEx_get_median_gene_expression |
HPA_get_rna_expression |
Note as unavailable | Document in report |
gnomad_get_gene_constraints |
OpenTargets constraint |
- | Note in report |
DGIdb_get_drug_gene_interactions |
OpenTargets drugs |
GtoPdb |
Note in report |
Failure Surfacing Rule
NEVER silently skip failed tools. Always document:
### 7.1 Tissue Expression
**GTEx Data**: Unavailable (API timeout after 3 attempts)
**Fallback Data (HPA)**:
| Tissue | Expression Level | Specificity |
|--------|-----------------|-------------|
| Liver | High | Enhanced |
| Kidney | Medium | - |
*Note: For complete GTEx data, query directly at gtexportal.org*
Per-Section Data Minimums & Completeness Audit
Minimum Data Requirements (Enforced)
| Section | Minimum Data | If Not Met |
|---|---|---|
| 6. PPIs | ≥20 interactors | Document which tools failed + why |
| 7. Expression | Top 10 tissues with TPM + HPA RNA summary | Note "limited data" with specific gaps |
| 8. Disease | Top 10 OT diseases + gnomAD constraints + ClinVar summary | Separate SNV/CNV; note if constraint unavailable |
| 9. Druggability | OT tractability + probes + drugs + DGIdb + GtoPdb fallback | "No drugs/probes" is valid data |
| 11. Literature | Total count + 5-year trend + 3-5 key papers with evidence tiers | Note if sparse (<50 papers) |
Post-Run Completeness Audit
Before finalizing the report, run this checklist:
## Completeness Audit (REQUIRED)
### Data Minimums Check
- [ ] PPIs: ≥20 interactors OR explanation why fewer
- [ ] Expression: Top 10 tissues with values OR explicit "unavailable"
- [ ] Diseases: Top 10 associations with scores OR "no associations"
- [ ] Constraints: All 4 scores (pLI, LOEUF, missense Z, pRec) OR "unavailable"
- [ ] Druggability: All modalities assessed; probes + drugs listed OR "none"
### Negative Results Documented
- [ ] Empty tool results noted explicitly (not left blank)
- [ ] Failed tools with fallbacks documented
- [ ] "No data" sections have implications noted
### Evidence Quality
- [ ] T1-T4 grades in Executive Summary disease claims
- [ ] T1-T4 grades in Disease Associations table
- [ ] Key papers table has evidence tiers
- [ ] Per-section evidence summaries included
### Source Attribution
- [ ] Every data point has source tool/database cited
- [ ] Section-end source summaries present
Data Gap Table (Required if minimums not met)
## 15. Data Gaps & Limitations
| Section | Expected Data | Actual | Reason | Alternative Source |
|---------|---------------|--------|--------|-------------------|
| 6. PPIs | ≥20 interactors | 8 | Novel target, limited studies | Literature review needed |
| 7. Expression | GTEx TPM | None | Versioned ID not recognized | See HPA data |
| 9. Probes | Chemical probes | None | No validated probes exist | Consider tool compound dev |
**Recommendations for Data Gaps**:
1. For PPIs: Query BioGRID with broader parameters; check yeast-2-hybrid studies
2. For Expression: Query GEO directly for tissue-specific datasets
Report Template (Initial File)
File: [TARGET]_target_report.md
# Target Intelligence Report: [TARGET NAME]
**Generated**: [Date] | **Query**: [Original query] | **Status**: In Progress
---
## 1. Executive Summary
[Researching...]
<!-- REQUIRED: 2-3 sentences, disease claims must have T1-T4 grades -->
## 2. Target Identifiers
[Researching...]
<!-- REQUIRED: UniProt, Ensembl (versioned), Entrez, ChEMBL, HGNC, Symbol -->
## 3. Basic Information
### 3.1 Protein Description
[Researching...]
### 3.2 Protein Function
[Researching...]
### 3.3 Subcellular Localization
[Researching...]
## 4. Structural Biology
### 4.1 Experimental Structures (PDB)
[Researching...]
<!-- METHOD: 3-step chain (UniProt xrefs → sequence search → domain search) -->
### 4.2 AlphaFold Prediction
[Researching...]
### 4.3 Domain Architecture
[Researching...]
### 4.4 Key Structural Features
[Researching...]
## 5. Function & Pathways
### 5.1 Gene Ontology Annotations
[Researching...]
<!-- REQUIRED: Evidence codes mapped to T1-T4 -->
### 5.2 Pathway Involvement
[Researching...]
## 6. Protein-Protein Interactions
[Researching...]
<!-- MINIMUM: ≥20 interactors OR explanation -->
## 7. Expression Profile
### 7.1 Tissue Expression (GTEx/HPA)
[Researching...]
<!-- NOTE: Use versioned Ensembl ID for GTEx if needed -->
### 7.2 Tissue Specificity
[Researching...]
<!-- MINIMUM: Top 10 tissues with TPM values -->
## 8. Genetic Variation & Disease
### 8.1 Constraint Scores
[Researching...]
<!-- REQUIRED: pLI, LOEUF, missense Z, pRec with interpretations -->
### 8.2 Disease Associations
[Researching...]
<!-- REQUIRED: Top 10 with OT scores; T1-T4 evidence grades -->
### 8.3 Clinical Variants (ClinVar)
[Researching...]
<!-- REQUIRED: Separate SNV and CNV tables -->
### 8.4 Mouse Model Phenotypes
[Researching...]
## 9. Druggability & Pharmacology
### 9.1 Tractability Assessment
[Researching...]
<!-- REQUIRED: All modalities (SM, Ab, PROTAC, other) -->
### 9.2 Known Drugs
[Researching...]
### 9.3 Chemical Probes
[Researching...]
<!-- NOTE: "No probes" is valid data - document explicitly -->
### 9.4 Clinical Pipeline
[Researching...]
### 9.5 ChEMBL Bioactivity
[Researching...]
## 10. Safety Profile
### 10.1 Safety Liabilities
[Researching...]
### 10.2 Expression-Based Toxicity Risk
[Researching...]
### 10.3 Mouse KO Phenotypes
[Researching...]
## 11. Literature & Research Landscape
### 11.1 Publication Metrics
[Researching...]
<!-- REQUIRED: Total, 5y, 1y, drug-related, clinical -->
### 11.2 Research Trend
[Researching...]
### 11.3 Key Publications
[Researching...]
<!-- REQUIRED: Table with PMID, title, year, evidence tier -->
### 11.4 Evidence Summary by Theme
[Researching...]
<!-- REQUIRED: T1-T4 breakdown per research theme -->
## 12. Competitive Landscape
[Researching...]
## 13. Summary & Recommendations
### 13.1 Target Validation Scorecard
[Researching...]
<!-- REQUIRED: 6 criteria, 1-5 scores, evidence quality noted -->
### 13.2 Strengths
[Researching...]
### 13.3 Challenges & Risks
[Researching...]
### 13.4 Recommendations
[Researching...]
<!-- REQUIRED: ≥3 prioritized (HIGH/MEDIUM/LOW) -->
## 14. Data Sources & Methodology
[Will be populated as research progresses...]
## 15. Data Gaps & Limitations
[To be populated post-audit...]
Quick Reference: Tool Parameters
| Tool | Parameter | Notes |
|---|---|---|
Reactome_map_uniprot_to_pathways |
id |
NOT uniprot_id |
ensembl_get_xrefs |
id |
NOT gene_id |
GTEx_get_median_gene_expression |
gencode_id, operation |
Try versioned ID if empty |
OpenTargets_* |
ensemblId |
camelCase, not ensemblID |
STRING_get_protein_interactions |
protein_ids, species |
List format for IDs |
intact_get_interactions |
identifier |
UniProt accession |
When NOT to Use This Skill
- Simple protein lookup → Use
UniProt_get_entry_by_accessiondirectly - Drug information only → Use drug-focused tools
- Disease-centric query → Use disease-intelligence-gatherer skill
- Sequence retrieval → Use sequence-retrieval skill
- Structure download → Use protein-structure-retrieval skill
Use this skill for comprehensive, multi-angle target analysis with guaranteed data completeness.
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646
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5 days ago
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