tooluniverse-infectious-disease

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0
Source

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.

Install

mkdir -p .claude/skills/tooluniverse-infectious-disease && curl -L -o skill.zip "https://mcp.directory/api/skills/download/3410" && unzip -o skill.zip -d .claude/skills/tooluniverse-infectious-disease && rm skill.zip

Installs to .claude/skills/tooluniverse-infectious-disease

About this skill

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; respond in 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 [PATHOGEN]_outbreak_intelligence.md FIRST with section headers
  2. Progressively update as data is gathered
  3. Output separate files: [PATHOGEN]_drug_candidates.csv, [PATHOGEN]_target_proteins.csv

2. Citation Requirements (MANDATORY)

Every finding must have inline source attribution:

### Target: RNA-dependent RNA polymerase (RdRp)
- **UniProt**: P0DTD1 (NSP12)
- **Essentiality**: Required for replication
*Source: UniProt via `UniProt_search`, literature review*

Phase 0: Tool Verification

Known Parameter Corrections

ToolWRONG ParameterCORRECT Parameter
NCBI_Taxonomy_searchnamequery
UniProt_searchnamequery
ChEMBL_search_targetstargetquery
NvidiaNIM_diffdockprotein_fileprotein (content)

Workflow Overview

Phase 1: Pathogen Identification
├── Taxonomic classification (NCBI Taxonomy)
├── Closest relatives (for knowledge transfer)
├── Genome/proteome availability
└── OUTPUT: Pathogen profile
    |
Phase 2: Target Identification
├── Essential genes/proteins (UniProt)
├── Conservation across strains
├── Druggability assessment (ChEMBL)
└── OUTPUT: Prioritized target list (scored by essentiality/conservation/druggability/precedent)
    |
Phase 3: Structure Prediction (NvidiaNIM)
├── AlphaFold2/ESMFold for targets
├── Binding site identification
├── Quality assessment (pLDDT)
└── OUTPUT: Target structures (docking-ready if pLDDT > 70)
    |
Phase 4: Drug Repurposing Screen
├── Approved drugs for related pathogens (ChEMBL)
├── Broad-spectrum antivirals/antibiotics
├── Docking screen (NvidiaNIM_diffdock)
└── OUTPUT: Ranked candidate drugs
    |
Phase 4.5: Pathway Analysis
├── KEGG: Pathogen metabolism pathways
├── Essential metabolic targets
├── Host-pathogen interaction pathways
└── OUTPUT: Pathway-based drug targets
    |
Phase 5: Literature Intelligence
├── PubMed: Published outbreak reports
├── BioRxiv/MedRxiv: Recent preprints (CRITICAL for outbreaks)
├── ArXiv: Computational/ML preprints
├── OpenAlex: Citation tracking
├── ClinicalTrials.gov: Active trials
└── OUTPUT: Evidence synthesis
    |
Phase 6: Report Synthesis
├── Top drug candidates with evidence grades
├── Clinical trial opportunities
├── Recommended immediate actions
└── OUTPUT: Final report

Phase Summaries

Phase 1: Pathogen Identification

Classify via NCBI Taxonomy (query param). Identify related pathogens with existing drugs for knowledge transfer. Determine genome/proteome availability.

Phase 2: Target Identification

Search UniProt for pathogen proteins (reviewed). Check ChEMBL for drug precedent. Score targets by: Essentiality (30%), Conservation (25%), Druggability (25%), Drug precedent (20%). Aim for 5+ targets.

Phase 3: Structure Prediction

Use NvidiaNIM AlphaFold2 for top 3 targets. Assess pLDDT confidence. Only dock structures with pLDDT > 70 (active site > 90 preferred). Fallback: alphafold_get_prediction or NvidiaNIM_esmfold.

Phase 4: Drug Repurposing Screen

Source candidates from: related pathogen drugs, broad-spectrum antivirals, target class drugs (DGIdb). Dock top 20+ candidates via NvidiaNIM_diffdock. Rank by docking score and evidence tier.

Phase 4.5: Pathway Analysis

Use KEGG to identify essential metabolic pathways. Map host-pathogen interaction points. Identify pathway-based drug targets beyond direct protein inhibition.

Phase 5: Literature Intelligence

Search PubMed (peer-reviewed), BioRxiv/MedRxiv (preprints - critical for outbreaks), ArXiv (computational), ClinicalTrials.gov (active trials). Track citations via OpenAlex. Note: preprints are NOT peer-reviewed.

Phase 6: Report Synthesis

Aggregate all findings into final report. Grade every candidate. Provide 3+ immediate actions, clinical trial opportunities, and research priorities.


Evidence Grading

TierSymbolCriteriaExample
T1[T1]FDA approved for this pathogenRemdesivir for COVID
T2[T2]Clinical trial evidence OR approved for related pathogenFavipiravir
T3[T3]In vitro activity OR strong docking + mechanismSofosbuvir
T4[T4]Computational prediction onlyNovel 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 ToolFallback 1Fallback 2
NvidiaNIM_alphafold2alphafold_get_predictionNvidiaNIM_esmfold
NvidiaNIM_diffdockNvidiaNIM_boltz2Manual docking
NCBI_Taxonomy_searchUniProt_taxonomyManual classification
ChEMBL_search_drugsDrugBank_searchPubChem bioassays

References

FileContents
TOOLS_REFERENCE.mdComplete tool documentation
phase_details.mdDetailed code examples and procedures for each phase
report_template.mdReport template with section headers, checklist, and evidence grading
CHECKLIST.mdPre-delivery verification checklist (quality, citations, docking)
EXAMPLES.mdFull worked examples (coronavirus, CRKP, limited-info scenarios)

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