Infectious Disease Outbreak Intelligence
Rapid response system for emerging pathogens using taxonomy analysis, target identification, structure prediction, and computational drug repurposing.
KEY PRINCIPLES:
- Speed is critical - Optimize for rapid actionable intelligence
- Target essential proteins - Focus on conserved, essential viral/bacterial proteins
- Leverage existing drugs - Prioritize FDA-approved compounds for repurposing
- Structure-guided - Use NvidiaNIM for rapid structure prediction and docking
- Evidence-graded - Grade repurposing candidates by evidence strength
- Actionable output - Prioritized drug candidates with rationale
- English-first queries - Always use English terms in tool calls (pathogen names, protein names, drug names), even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language
When to Use
Apply when user asks:
- "New pathogen detected - what drugs might work?"
- "Emerging virus [X] - therapeutic options?"
- "Drug repurposing candidates for [pathogen]"
- "What do we know about [novel coronavirus/bacteria]?"
- "Essential targets in [pathogen] for drug development"
- "Can we repurpose [drug] against [pathogen]?"
Critical Workflow Requirements
1. Report-First Approach (MANDATORY)
- Create the report file FIRST:
- File name: [PATHOGEN]_outbreak_intelligence.md - Initialize with section headers - Add placeholder: [Analyzing...]
- Progressively update as you gather data
- Output separate files:
- [PATHOGEN]_drug_candidates.csv - Ranked repurposing candidates - [PATHOGEN]_target_proteins.csv - Druggable targets
2. Citation Requirements (MANDATORY)
### Target: RNA-dependent RNA polymerase (RdRp)
- **UniProt**: P0DTD1 (NSP12)
- **Essentiality**: Required for replication
- **Conservation**: >95% across variants
- **Drug precedent**: Remdesivir targets RdRp
*Source: UniProt via `UniProt_search`, literature review*Phase 0: Tool Verification
Known Parameter Corrections
| Tool | WRONG Parameter | CORRECT Parameter |
|---|---|---|
NCBI_Taxonomy_search | name | query |
UniProt_search | name | query |
ChEMBL_search_targets | target | query |
NvidiaNIM_diffdock | protein_file | protein (content) |
Workflow Overview
Phase 1: Pathogen Identification
├── Taxonomic classification
├── Closest relatives (for knowledge transfer)
├── Genome/proteome availability
└── OUTPUT: Pathogen profile
↓
Phase 2: Target Identification
├── Essential genes/proteins
├── Conserved across strains
├── Druggability assessment
└── OUTPUT: Prioritized target list
↓
Phase 3: Structure Prediction (NvidiaNIM)
├── AlphaFold2/ESMFold for targets
├── Binding site identification
├── Quality assessment (pLDDT)
└── OUTPUT: Target structures
↓
Phase 4: Drug Repurposing Screen
├── Approved drugs for related pathogens
├── Broad-spectrum antivirals/antibiotics
├── Docking screen (NvidiaNIM_diffdock)
└── OUTPUT: Candidate drugs
↓
Phase 4.5: Pathway Analysis (NEW)
├── KEGG: Pathogen metabolism pathways
├── Essential metabolic targets
├── Host-pathogen interaction pathways
└── OUTPUT: Pathway-based drug targets
↓
Phase 5: Literature Intelligence (ENHANCED)
├── PubMed: Published outbreak reports
├── BioRxiv/MedRxiv: Recent preprints (CRITICAL for outbreaks)
├── ArXiv: Computational/ML preprints
├── OpenAlex: Citation tracking
└── OUTPUT: Evidence synthesis
↓
Phase 6: Report Synthesis
├── Top drug candidates
├── Clinical trial opportunities
├── Recommended immediate actions
└── OUTPUT: Final reportPhase 1: Pathogen Identification
1.1 Taxonomic Classification
def identify_pathogen(tu, pathogen_query):
"""Classify pathogen taxonomically."""
# NCBI Taxonomy search
taxonomy = tu.tools.NCBI_Taxonomy_search(query=pathogen_query)
return {
'taxid': taxonomy.get('taxid'),
'scientific_name': taxonomy.get('scientific_name'),
'rank': taxonomy.get('rank'),
'lineage': taxonomy.get('lineage'),
'type': classify_type(taxonomy) # virus, bacteria, fungus, parasite
}1.2 Related Pathogens (Knowledge Transfer)
def find_related_pathogens(tu, taxid):
"""Find related pathogens for drug knowledge transfer."""
# Get family/genus level relatives
relatives = tu.tools.NCBI_Taxonomy_get_children(
taxid=taxid,
rank="genus"
)
# Find relatives with approved drugs
related_with_drugs = []
for rel in relatives:
drugs = tu.tools.ChEMBL_search_targets(
query=rel['scientific_name'],
organism_contains=True
)
if drugs:
related_with_drugs.append({
'pathogen': rel,
'drugs': drugs
})
return related_with_drugs1.3 Output for Report
## 1. Pathogen Profile
### 1.1 Taxonomic Classification
| Property | Value |
|----------|-------|
| **Organism** | SARS-CoV-2 |
| **Taxonomy ID** | 2697049 |
| **Type** | RNA virus (positive-sense, single-stranded) |
| **Family** | Coronaviridae |
| **Genus** | Betacoronavirus |
| **Lineage** | Riboviria > Orthornavirae > Pisuviricota > Pisoniviricetes > Nidovirales |
### 1.2 Related Pathogens with Drug Precedent
| Relative | Similarity | Approved Drugs | Relevance |
|----------|------------|----------------|-----------|
| SARS-CoV | 79% genome | Remdesivir (EUA) | High |
| MERS-CoV | 50% genome | None approved | Medium |
| HCoV-229E | 45% genome | None specific | Low |
**Knowledge Transfer Opportunity**: SARS-CoV drug development data highly relevant.
*Source: NCBI Taxonomy, ChEMBL*Phase 2: Target Identification
2.1 Essential Protein Identification
def identify_targets(tu, pathogen_name):
"""Identify essential druggable targets."""
# Search UniProt for pathogen proteins
proteins = tu.tools.UniProt_search(
query=f"organism:{pathogen_name}",
reviewed=True
)
# Prioritize by essentiality and druggability
targets = []
for protein in proteins:
# Check for known drug interactions
chembl_target = tu.tools.ChEMBL_search_targets(
query=protein['gene_name']
)
targets.append({
'uniprot': protein['accession'],
'name': protein['protein_name'],
'function': protein['function'],
'has_drug_precedent': len(chembl_target) > 0,
'druggability': assess_druggability(protein)
})
return rank_targets(targets)2.2 Target Prioritization Criteria
| Criterion | Weight | Description |
|---|---|---|
| Essentiality | 30% | Required for replication/survival |
| Conservation | 25% | Conserved across strains/variants |
| Druggability | 25% | Structural features amenable to binding |
| Drug precedent | 20% | Existing drugs for homologous targets |
2.3 Output for Report
## 2. Druggable Targets
### 2.1 Prioritized Target List
| Rank | Target | UniProt | Function | Score | Drug Precedent |
|------|--------|---------|----------|-------|----------------|
| 1 | RdRp (NSP12) | P0DTD1 | RNA replication | 92 | Remdesivir |
| 2 | Main protease (Mpro) | P0DTD1 | Polyprotein cleavage | 88 | Nirmatrelvir |
| 3 | Papain-like protease | P0DTD1 | Polyprotein cleavage | 75 | GRL0617 (preclinical) |
| 4 | Spike protein | P0DTC2 | Host cell entry | 70 | Antibodies |
| 5 | Helicase (NSP13) | P0DTD1 | RNA unwinding | 65 | None approved |
### 2.2 Target Details
#### Target 1: RNA-dependent RNA polymerase (RdRp/NSP12)
| Property | Value |
|----------|-------|
| **UniProt** | P0DTD1 (polyprotein position 4393-5324) |
| **Length** | 932 amino acids |
| **Function** | Catalyzes RNA synthesis from RNA template |
| **Essentiality** | Absolute (no replication without RdRp) |
| **Conservation** | >99% across all SARS-CoV-2 variants |
| **Binding site** | Nucleotide binding pocket |
| **Drug precedent** | Remdesivir (FDA approved), Favipiravir |
*Source: UniProt, ChEMBL*Phase 3: Structure Prediction
3.1 AlphaFold2 Structure Prediction (NVIDIA NIM)
def predict_target_structure(tu, sequence, target_name):
"""Predict structure for target protein."""
# Use AlphaFold2 for high accuracy
structure = tu.tools.NvidiaNIM_alphafold2(
sequence=sequence,
algorithm="mmseqs2",
relax_prediction=False
)
# Parse pLDDT confidence
plddt_scores = parse_plddt(structure)
return {
'structure': structure['structure'],
'mean_plddt': np.mean(plddt_scores),
'high_confidence_regions': get_high_confidence(plddt_scores),
'predicted_binding_site': identify_binding_site(structure)
}3.2 Structure Quality Assessment
| pLDDT Range | Confidence | Use for Docking |
|---|---|---|
| >90 | Very High | Excellent |
| 70-90 | High | Good |
| 50-70 | Medium | Use caution |
| <50 | Low | Not recommended |
3.3 Output for Report
## 3. Target Structures
### 3.1 Structure Prediction Results
| Target | Method | Length | Mean pLDDT | Docking Ready |
|--------|--------|--------|------------|---------------|
| RdRp (NSP12) | AlphaFold2 | 932 aa | 91.2 | ✓ Yes |
| Mpro | AlphaFold2 | 306 aa | 93.5 | ✓ Yes |
| PLpro | AlphaFold2 | 315 aa | 88.7 | ✓ Yes |
### 3.2 RdRp Structure Quality
| Region | Residues | pLDDT | Functional Role |
|--------|----------|-------|-----------------|
| Palm domain | 582-620 | 94.2 | Catalytic site |
| Fingers domain | 397-581 | 91.8 | NTP entry |
| Thumb domain | 621-815 | 89.4 | RNA binding |
| Active site | D760, D761 | 96.1 | Catalysis |
**Docking Recommendation**: Structure suitable for docking; active site highly confident.
*Source: NVIDIA NIM via `NvidiaNIM_alphafold2`*Phase 4: Drug Repurposing Screen
4.1 Identify Repurposing Candidates
def get_repurposing_candidates(tu, target_name, pathogen_family):
"""Find approved drugs to repurpose."""
candidates = []
# 1. Drugs approved for related pathogens
related_drugs = tu.tools.ChEMBL_search_drugs(
query=pathogen_family,
max_phase=4
)
candidates.extend(related_drugs)
# 2. Broad-spectrum antivirals
antivirals = tu.tools.ChEMBL_search_drugs(
query="broad spectrum antiviral",
max_phase=4
)
candidates.extend(antivirals)
# 3. Drugs with known activity against target class
target_class_drugs = tu.tools.DGIdb_get_drug_gene_interactions(
genes=[target_name]
)
candidates.extend(target_class_drugs)
return deduplicate(candidates)4.2 Docking Screen (NVIDIA NIM)
def dock_candidates(tu, target_structure, candidate_smiles_list):
"""Dock candidate drugs against target."""
results = []
for smiles in candidate_smiles_list:
docking = tu.tools.NvidiaNIM_diffdock(
protein=target_structure,
ligand=smiles,
num_poses=5
)
results.append({
'smiles': smiles,
'top_score': docking['poses'][0]['confidence'],
'poses': docking['poses']
})
return sorted(results, key=lambda x: x['top_score'], reverse=True)4.3 Output for Report
## 4. Drug Repurposing Screen
### 4.1 Candidate Identification
| Source | Candidates | FDA Approved |
|--------|------------|--------------|
| Related pathogen drugs | 12 | 8 |
| Broad-spectrum antivirals | 15 | 11 |
| Target class drugs | 8 | 5 |
| **Total unique** | **28** | **19** |
### 4.2 Docking Results (RdRp Target)
| Rank | Drug | Indication | Docking Score | Evidence |
|------|------|------------|---------------|----------|
| 1 | **Remdesivir** | COVID-19 | 0.92 | ★★★ FDA approved |
| 2 | **Favipiravir** | Influenza | 0.87 | ★★☆ Phase 3 COVID |
| 3 | **Sofosbuvir** | HCV | 0.84 | ★★☆ In vitro active |
| 4 | Ribavirin | RSV, HCV | 0.78 | ★☆☆ Mixed results |
| 5 | Molnupiravir | COVID-19 | 0.76 | ★★★ FDA approved |
### 4.3 Top Candidate: Remdesivir
| Property | Value |
|----------|-------|
| **Docking score** | 0.92 (excellent) |
| **Mechanism** | RdRp inhibitor (nucleotide analog) |
| **FDA status** | Approved for COVID-19 |
| **Clinical evidence** | ACTT-1: Reduced recovery time |
| **Binding mode** | Active site, chain termination |
*Source: NVIDIA NIM via `NvidiaNIM_diffdock`, ChEMBL*Phase 4.5: Pathway Analysis (NEW)
4.5.1 Pathogen Metabolism Pathways
def analyze_pathogen_pathways(tu, pathogen_name, pathogen_type):
"""Identify druggable metabolic pathways in pathogen."""
# KEGG pathogen pathways
pathways = tu.tools.kegg_search_pathway(
query=f"{pathogen_name} metabolism"
)
# Essential metabolic genes
essential_genes = tu.tools.kegg_get_pathway_genes(
pathway_id=pathways[0]['pathway_id']
)
# Host-pathogen interaction pathways
host_pathogen = tu.tools.kegg_search_pathway(
query=f"{pathogen_name} host interaction"
)
return {
'metabolic_pathways': pathways,
'essential_genes': essential_genes,
'host_interaction': host_pathogen
}4.5.2 Output for Report
## 4.5 Pathway Analysis
### Pathogen Metabolic Pathways (KEGG)
| Pathway | Essentiality | Drug Targets |
|---------|--------------|--------------|
| Viral replication (ko03030) | Essential | RdRp, Helicase |
| Viral protein processing | Essential | Mpro, PLpro |
| Host membrane interaction | Essential | Spike, ACE2 |
### Druggable Pathway Targets
| Target | Pathway | Known Drugs | Evidence |
|--------|---------|-------------|----------|
| RdRp | Viral replication | Remdesivir | ★★★ |
| 3CLpro | Protein processing | Nirmatrelvir | ★★★ |
| PLpro | Protein processing | GRL-0617 | ★★☆ |
### Host-Pathogen Interaction Points
| Interaction | Host Protein | Pathway | Druggability |
|-------------|--------------|---------|--------------|
| Entry | ACE2 | Cell surface | ★★☆ |
| Fusion | TMPRSS2 | Protease | ★★★ |
| Replication | Host ribosomes | Translation | ★☆☆ |
*Source: KEGG, Reactome*Phase 5: Literature Intelligence (ENHANCED)
5.1 Comprehensive Literature Search
def comprehensive_outbreak_literature(tu, pathogen_name):
"""Search all literature sources for outbreak intelligence."""
# PubMed: Peer-reviewed
pubmed = tu.tools.PubMed_search_articles(
query=f"{pathogen_name} AND (outbreak OR treatment OR drug)",
limit=50,
sort="date"
)
# BioRxiv: CRITICAL for outbreaks - newest findings
biorxiv = tu.tools.BioRxiv_search_preprints(
query=f"{pathogen_name} treatment mechanism",
limit=20
)
# MedRxiv: Clinical preprints
medrxiv = tu.tools.MedRxiv_search_preprints(
query=f"{pathogen_name} clinical trial",
limit=20
)
# ArXiv: Computational/ML papers
arxiv = tu.tools.ArXiv_search_papers(
query=f"{pathogen_name} drug discovery",
category="q-bio",
limit=10
)
# Clinical trials
trials = tu.tools.search_clinical_trials(
condition=pathogen_name,
status="Recruiting"
)
# Citation analysis
key_papers = pubmed[:10]
for paper in key_papers:
citation = tu.tools.openalex_search_works(
query=paper['title'],
limit=1
)
paper['citations'] = citation[0].get('cited_by_count', 0) if citation else 0
return {
'pubmed': pubmed,
'biorxiv': biorxiv,
'medrxiv': medrxiv,
'arxiv': arxiv,
'trials': trials,
'key_papers': key_papers
}5.2 Output for Report
## 5. Literature Intelligence
### 5.1 Published Literature (Peer-Reviewed)
| Topic | Papers | Key Finding |
|-------|--------|-------------|
| Treatment | 234 | Paxlovid remains effective |
| Resistance | 45 | Nirmatrelvir resistance mutations identified |
| Variants | 189 | XBB variants maintain drug sensitivity |
| Vaccines | 312 | Updated boosters protective |
### 5.2 Preprints (CRITICAL for Emerging Outbreaks)
**⚠️ Note**: Preprints are NOT peer-reviewed. Critical for rapid intelligence but use with caution.
| Source | Title | Posted | Key Finding |
|--------|-------|--------|-------------|
| BioRxiv | Novel RdRp inhibitor shows activity... | 2024-02-01 | New candidate |
| MedRxiv | Real-world effectiveness of... | 2024-01-28 | Paxlovid 85% effective |
| BioRxiv | Resistance mutations in... | 2024-01-25 | Monitor L50F mutation |
### 5.3 Computational/ML Preprints (ArXiv)
| Title | Category | Relevance |
|-------|----------|-----------|
| Deep learning for antiviral discovery | q-bio.BM | Drug design |
| Structure prediction for novel... | q-bio.BM | Target modeling |
### 5.4 Active Clinical Trials
| NCT ID | Phase | Drug | Status |
|--------|-------|------|--------|
| NCT05012345 | 3 | Ensitrelvir | Recruiting |
| NCT05023456 | 2 | VV116 | Recruiting |
| NCT05034567 | 2 | S-217622 | Active |
### 5.5 Citation Analysis (High-Impact Papers)
| PMID | Title | Citations | Year |
|------|-------|-----------|------|
| 33123456 | Remdesivir for COVID-19 | 5,234 | 2020 |
| 34234567 | Paxlovid Phase 3 results | 2,876 | 2022 |
*Source: PubMed, BioRxiv, MedRxiv, ArXiv, OpenAlex, ClinicalTrials.gov*Report Template
# Outbreak Intelligence Report: [PATHOGEN]
**Generated**: [Date] | **Query**: [Original query] | **Status**: In Progress
---
## Executive Summary
[Analyzing...]
---
## 1. Pathogen Profile
### 1.1 Classification
[Analyzing...]
### 1.2 Related Pathogens
[Analyzing...]
---
## 2. Druggable Targets
### 2.1 Prioritized Targets
[Analyzing...]
### 2.2 Target Details
[Analyzing...]
---
## 3. Target Structures
### 3.1 Prediction Results
[Analyzing...]
### 3.2 Binding Sites
[Analyzing...]
---
## 4. Drug Repurposing Screen
### 4.1 Candidate Drugs
[Analyzing...]
### 4.2 Docking Results
[Analyzing...]
### 4.3 Top Candidates
[Analyzing...]
---
## 5. Literature Intelligence
### 5.1 Recent Findings
[Analyzing...]
### 5.2 Clinical Trials
[Analyzing...]
---
## 6. Recommendations
### 6.1 Immediate Actions
[Analyzing...]
### 6.2 Clinical Trial Opportunities
[Analyzing...]
### 6.3 Research Priorities
[Analyzing...]
---
## 7. Data Gaps & Limitations
[Analyzing...]
---
## 8. Data Sources
[Will be populated...]Evidence Grading
| Tier | Symbol | Criteria | Example |
|---|---|---|---|
| T1 | ★★★ | FDA approved for this pathogen | Remdesivir for COVID |
| T2 | ★★☆ | Clinical trial evidence OR approved for related pathogen | Favipiravir |
| T3 | ★☆☆ | In vitro activity OR strong docking + mechanism | Sofosbuvir |
| T4 | ☆☆☆ | Computational prediction only | Novel docking hits |
Completeness Checklist
Phase 1: Pathogen ID
- Taxonomic classification complete
- Related pathogens identified
- Genome/proteome availability noted
Phase 2: Targets
- ≥5 targets identified
- Essentiality documented
- Conservation assessed
- Drug precedent checked
Phase 3: Structures
- Structures predicted for top 3 targets
- pLDDT confidence reported
- Binding sites identified
Phase 4: Drug Screen
- ≥20 candidates screened
- FDA-approved drugs prioritized
- Docking scores reported
- Top 5 candidates detailed
Phase 5: Literature
- Recent papers summarized
- Active trials listed
- Resistance data noted
Phase 6: Recommendations
- ≥3 immediate actions
- Clinical trial opportunities
- Research priorities
Fallback Chains
| Primary Tool | Fallback 1 | Fallback 2 |
|---|---|---|
NvidiaNIM_alphafold2 | alphafold_get_prediction | NvidiaNIM_esmfold |
NvidiaNIM_diffdock | NvidiaNIM_boltz2 | Manual docking |
NCBI_Taxonomy_search | UniProt_taxonomy | Manual classification |
ChEMBL_search_drugs | DrugBank_search | PubChem bioassays |
Tool Reference
See TOOLS_REFERENCE.md for complete tool documentation.