Tooluniverse Infectious Disease

Infectious Disease Outbreak Intelligence

Rapid response system for emerging pathogens using taxonomy analysis, target identification, structure prediction, and computational drug repurposing.

KEY PRINCIPLES:

1. Speed is critical - Optimize for rapid actionable intelligence
2. Target essential proteins - Focus on conserved, essential viral/bacterial proteins
3. Leverage existing drugs - Prioritize FDA-approved compounds for repurposing
4. Structure-guided - Use NvidiaNIM for rapid structure prediction and docking
5. Evidence-graded - Grade repurposing candidates by evidence strength
6. Actionable output - Prioritized drug candidates with rationale
7. 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)

1. Create the report file FIRST:

   • File name: [PATHOGEN]_outbreak_intelligence.md
   • Initialize with section headers
   • Add placeholder: [Analyzing...]

2. Progressively update as you gather data

3. 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

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 report

Phase 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_drugs

1.3 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

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

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

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

Tool Reference

See TOOLS_REFERENCE.md for complete tool documentation.