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Disease tools

alphafold_sovereign.tools.disease

Disease ontology and clinical intelligence MCP tools.

18 tools spanning: - MONDO disease lookup and hierarchy traversal - HPO phenotype-to-disease and gene-to-phenotype - Common disease protein target profiling (all major ICD chapters) - Open Targets disease-target evidence scoring - Variant 3-D triage (HGVS → structure → AlphaMissense → ClinVar → gnomAD) - Phenotype-to-structure pipeline - Cross-disease structural comparison - Orphan disease structural atlas

All tools are read-only, idempotent, and append a provenance footer with server version, request timestamp, and data-source identifiers.

lookup_disease async 

lookup_disease(params: MONDOLookupInput) -> str

Retrieve a disease record from the MONDO unified disease ontology.

Returns the canonical MONDO entry with: - Disease name, definition, synonyms - ICD-10 / ICD-11 codes (for clinical coding / EHR integration) - OMIM, Orphanet, MeSH, DOID cross-references - Immediate parent and child terms in the MONDO hierarchy

Example: lookup_disease(mondo_id='MONDO:0004995') returns the record for coronary artery disease.

search_diseases async 

search_diseases(params: MONDOSearchInput) -> str

Search for diseases by name or keyword using the MONDO ontology.

Returns a ranked list of matching diseases with MONDO IDs and cross-references. Useful for resolving a clinical term to a canonical identifier before querying targets or phenotypes.

Example: search_diseases(query='breast cancer', limit=5)

lookup_phenotype async 

lookup_phenotype(params: HPOTermInput) -> str

Retrieve an HPO phenotype term with associated disease annotations.

Returns: - Phenotype label, definition, synonyms - Diseases annotated with this phenotype (from HPO + OMIM + Orphanet) - Parent phenotype terms

Example: lookup_phenotype(hpo_id='HP:0001250') returns the Seizure phenotype with ~400 associated diseases.

get_gene_phenotype_profile async 

get_gene_phenotype_profile(params: GenePhenotypeInput) -> str

Return all HPO phenotypes associated with a gene, plus gnomAD constraint.

Useful for understanding the clinical consequences of variants in a gene before requesting structural context.

Returns: - HPO phenotypes linked to the gene (from HPO association database) - gnomAD LOEUF / pLI constraint scores - Interpretation of constraint (haploinsufficient / tolerant / moderate)

Example: get_gene_phenotype_profile(gene_symbol='SCN1A')

get_disease_targets async 

get_disease_targets(params: DiseaseTargetsInput) -> str

Return top protein targets for a disease with Open Targets evidence scores.

Evidence score breakdown (0–1 per data type): - genetic_association: GWAS + rare-variant signals - somatic_mutation: Cancer somatic variant evidence - known_drug: Approved or clinical-stage drugs - affected_pathway: Pathway membership (Reactome, SIGNOR) - literature: Text-mining evidence (Europe PMC) - animal_model: Knockout / model organism phenotypes - rna_expression: Differential expression evidence

Example: get_disease_targets(disease_id='MONDO:0007254', limit=15) returns top 15 targets for breast carcinoma.

get_target_diseases async 

get_target_diseases(params: TargetDiseaseInput) -> str

Return all diseases associated with a protein target via Open Targets.

Accepts a UniProt accession and returns the full disease landscape for that target — essential for target-validation and indication-expansion.

Example: get_target_diseases(uniprot_id='P04637') returns all diseases associated with TP53 / p53.

get_common_disease_targets async 

get_common_disease_targets(params: CommonDiseaseInput) -> str

Profile protein targets for major common diseases across ICD chapters.

Covers 9 disease categories with curated MONDO IDs and Open Targets evidence scores. Designed for target-identification in drug discovery and for understanding the structural landscape of disease-relevant proteins.

Categories: cardiovascular, oncology, neurodegeneration, metabolic, autoimmune, respiratory, infectious, psychiatric, rare.

Example: get_common_disease_targets(category='neurodegeneration') returns top targets for AD, PD, ALS, MS, and Huntington disease.

triage_variant_3d async 

triage_variant_3d(params: VariantTriageInput) -> str

Comprehensive clinical triage for a missense variant.

Fuses structural, pathogenicity, population-genetics, and disease context into a single prioritised report:

  1. Structural context — AlphaFold pLDDT at the mutated residue, PAE in the local neighbourhood (confidence of structural context)
  2. Pathogenicity — AlphaMissense score (0–1, calibrated to P/LP threshold ≥ 0.564), ClinVar interpretation + review status
  3. Population genetics — gnomAD global AF, per-ancestry breakdown, LOEUF gene constraint score
  4. Disease associations — MONDO disease record, Open Targets evidence scores for the host gene

Returns a pathogenicity_tier: HIGH / MEDIUM / LOW / UNKNOWN.

Example: triage_variant_3d(hgvs='BRCA1:c.181T>G')

phenotype_to_structures async 

phenotype_to_structures(params: PhenotypeToStructureInput) -> str

Map a clinical phenotype to the protein structures of its disease targets.

Pipeline: 1. Resolve HPO term → associated diseases 2. For each disease → top protein targets (Open Targets) 3. For each target → UniProt ID (for AlphaFold retrieval)

Use the returned UniProt IDs with get_structure or get_enriched_protein to retrieve structural data.

Example: phenotype_to_structures(hpo_id='HP:0002621') maps Atherosclerosis → disease targets → UniProt IDs.

get_orphan_disease_atlas async 

get_orphan_disease_atlas(params: OrphanDiseaseInput) -> str

Map an Orphanet rare disease to its MONDO record, HPO phenotypes, and protein targets.

Rare / orphan diseases are often under-studied because their small patient populations make large trials impractical. This tool aggregates the available structural and clinical intelligence into one report to accelerate research.

Returns: - MONDO record with ICD-10 coding - HPO phenotype profile of the disease - Open Targets protein target evidence scores - UniProt IDs for AlphaFold structural retrieval

Example: get_orphan_disease_atlas(orphanet_id='79318') returns the Gaucher disease atlas.

compare_disease_target_overlap async 

compare_disease_target_overlap(params: DiseaseSimilarityInput) -> str

Compare the protein target landscapes of two diseases.

Identifies shared and unique targets between two diseases — a key analysis for drug repurposing, identifying shared mechanisms, and understanding comorbidity.

Returns: - Shared targets (present in both disease target sets) - Unique to Disease A / Disease B - Jaccard similarity score of target sets

``compare_disease_target_overlap(

mondo_id_a='MONDO:0004975', # Alzheimer disease mondo_id_b='MONDO:0005180', # Parkinson disease

)``

resolve_icd10_to_mondo async 

resolve_icd10_to_mondo(params: ICD10ToMONDOInput) -> str

Resolve an ICD-10 clinical code to MONDO disease ontology terms.

Enables integration between clinical / EHR data (which uses ICD-10) and the research-grade MONDO ontology used by Open Targets, HPO, and this MCP.

Example: resolve_icd10_to_mondo(icd10_code='I21.0') maps ST-elevation MI (ICD-10) to MONDO coronary disease terms.