tooluniverse-infectious-disease

Original：🇺🇸 English

Translated

Rapid pathogen characterization and drug repurposing analysis for infectious disease outbreaks. Identifies pathogen taxonomy, essential proteins, predicts structures, and screens existing drugs via docking. Use when facing novel pathogens, emerging infections, or needing rapid therapeutic options during outbreaks.

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Sourcemims-harvard/tooluniverse

Added on2026-02-08

NPX Install

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SKILL.md Content

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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)

markdown

### 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 report

Phase 1: Pathogen Identification

1.1 Taxonomic Classification

python

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)

python

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

markdown

## 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

python

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

markdown

## 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)

python

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

markdown

## 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

python

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)

python

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

markdown

## 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

python

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

markdown

## 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

python

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

markdown

## 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

markdown

# 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.

tooluniverse-infectious-disease

NPX Install

Tags

SKILL.md Content

Infectious Disease Outbreak Intelligence

When to Use

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

2. Citation Requirements (MANDATORY)

Phase 0: Tool Verification

Known Parameter Corrections

Workflow Overview

Phase 1: Pathogen Identification

1.1 Taxonomic Classification

1.2 Related Pathogens (Knowledge Transfer)

1.3 Output for Report

Phase 2: Target Identification

2.1 Essential Protein Identification

2.2 Target Prioritization Criteria

2.3 Output for Report

Phase 3: Structure Prediction

3.1 AlphaFold2 Structure Prediction (NVIDIA NIM)

3.2 Structure Quality Assessment

3.3 Output for Report

Phase 4: Drug Repurposing Screen

4.1 Identify Repurposing Candidates

4.2 Docking Screen (NVIDIA NIM)

4.3 Output for Report

Phase 4.5: Pathway Analysis (NEW)

4.5.1 Pathogen Metabolism Pathways

4.5.2 Output for Report

Phase 5: Literature Intelligence (ENHANCED)

5.1 Comprehensive Literature Search

5.2 Output for Report

Report Template

Evidence Grading

Completeness Checklist

Phase 1: Pathogen ID

Phase 2: Targets

Phase 3: Structures

Phase 4: Drug Screen

Phase 5: Literature

Phase 6: Recommendations

Fallback Chains

Tool Reference