tooluniverse-infectious-disease

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

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

REASONING STRATEGY — Start Here: Start with pathogen identification: What type of organism? (virus, bacteria, fungus, parasite). Then ask:

• What are the essential proteins? (required for replication or viability — cannot be mutated away)
• Which are surface-exposed? (accessible to drugs and antibodies)
• Which are conserved across strains? (targeting conserved regions prevents resistance escape) These three questions define your drug targets and vaccine candidates. Organisms in the same genus share targets — look up drug precedent for related pathogens before predicting from scratch.

LOOK UP DON'T GUESS: Never assume a pathogen's taxonomy, genome size, or protein function. Always call BVBRC_search_taxonomy or UniProt_search first. Even well-known pathogens have strains with different drug susceptibility profiles — look up the specific strain when known.

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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]?"

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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*

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Phase 0: Tool Verification

Known Parameter Corrections

Tool	WRONG Parameter	CORRECT Parameter
NCBIDatasets_get_taxonomy	name	tax_id (integer) or use BVBRC_search_taxonomy for keyword search
UniProt_search	name	query
ChEMBL_search_targets	query, target	pref_name__contains (substring match)
get_diffdock_info	protein_file	protein (content)
drugbank_full_search	(may fail)	Use drugbank_vocab_search as primary DrugBank lookup

PubMed tip: Use sort="relevance" (default) not sort="pub_date" — date-sorted queries can return empty for narrow topics. Tool name: PubMed_search_articles. FDA labels: Use FDA_get_drug_label_info_by_field_value with targeted return_fields to avoid oversized responses from OpenFDA_search_drug_labels.

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Workflow Overview

Phase 1: Pathogen Identification
├── Taxonomic classification (NCBI Taxonomy)
├── Closest relatives (for knowledge transfer)
├── Genome/proteome availability
└── OUTPUT: Pathogen profile
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Phase 2: Target Identification
├── Essential genes/proteins (UniProt)
├── Conservation across strains
├── Druggability assessment (ChEMBL)
└── OUTPUT: Prioritized target list (scored by essentiality/conservation/druggability/precedent)
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Phase 3: Structure Prediction (NvidiaNIM)
├── AlphaFold2/ESMFold for targets
├── Binding site identification
├── Quality assessment (pLDDT)
└── OUTPUT: Target structures (docking-ready if pLDDT > 70)
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Phase 4: Drug Repurposing Screen
├── Approved drugs for related pathogens (ChEMBL)
├── Broad-spectrum antivirals/antibiotics
├── Docking screen (get_diffdock_info)
└── OUTPUT: Ranked candidate drugs
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Phase 4.5: Pathway Analysis
├── KEGG: Pathogen metabolism pathways
├── Essential metabolic targets
├── Host-pathogen interaction pathways
└── OUTPUT: Pathway-based drug targets
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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
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Phase 6: Report Synthesis
├── Top drug candidates with evidence grades
├── Clinical trial opportunities
├── Recommended immediate actions
└── OUTPUT: Final report

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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.

Genome assembly availability and QC: After classifying the pathogen, use NCBIDatasets_list_genomes_by_taxon (params taxon as tax_id, limit, reference_only) to find the reference genome, NCBIDatasets_get_genome_assembly (param accession, e.g. "GCF_000005845.2") for assembly metrics (length, N50, GC%, contig/chromosome counts), and NCBIDatasets_get_sequence_reports (param accession) to map replicons (chromosomes/plasmids with RefSeq/GenBank accessions). For the full assembly-QC-to-characterization workflow, see the tooluniverse-microbial-genome-characterization skill.

Open pathogen genomic surveillance: For the priority pathogens covered by Pathoplexus (west-nile, ebola-zaire, ebola-sudan, cchf, mpox), use Pathoplexus_count_sequences (params organism, group_by e.g. geoLocCountry or lineage) to gauge sequencing volume and geographic/lineage spread, and Pathoplexus_get_mutations (params organism, min_proportion e.g. 0.95) to pull characteristic high-prevalence mutations for the circulating population. Use early to quantify outbreak footprint and flag conserved mutations before target selection.

Knowledge transfer principle: Drugs effective against related pathogens are the highest-priority repurposing candidates. A protease inhibitor for SARS-CoV-1 is immediately relevant to SARS-CoV-2. Look up the related pathogen's approved drugs in ChEMBL before generating candidates from first principles.

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 ESMFold_predict_structure.

Phase 4: Drug Repurposing Screen

Source candidates from: related pathogen drugs, broad-spectrum antivirals, target class drugs (DGIdb). Dock top 20+ candidates via get_diffdock_info. 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.

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Evidence Grading

Tier	Symbol	Criteria	Example
T1	[T1]	FDA approved for this pathogen	Remdesivir for COVID
T2	[T2]	Clinical trial evidence OR approved for related pathogen	Favipiravir
T3	[T3]	In vitro activity OR strong docking + mechanism	Sofosbuvir
T4	[T4]	Computational prediction only	Novel docking hits

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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

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Fallback Chains

Primary Tool	Fallback 1	Fallback 2
NvidiaNIM_alphafold2 (requires NVIDIA_API_KEY env var; free key at build.nvidia.com)	alphafold_get_prediction (AlphaFold DB by UniProt)	ESMFold_predict_structure
get_diffdock_info	NvidiaNIM_boltz2 (requires NVIDIA_API_KEY env var; free key at build.nvidia.com)	Manual dock

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