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 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)

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.

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')

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.

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.

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.

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')

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.

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 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

Example: compare_disease_target_overlap( mondo_id_a='MONDO:0004975', # Alzheimer disease mondo_id_b='MONDO:0005180', # Parkinson disease )

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.

Search the local knowledge graph for previously analysed variants.

Returns variants matching the filter criteria from your accumulated research sessions. No API calls are made — all data is served from the local SQLite knowledge graph.

This is how AlphaFold Sovereign enables longitudinal research: every variant triaged by generate_variant_clinical_report is automatically stored and becomes instantly searchable here.

Search the local knowledge graph for previously assessed proteins.

Returns proteins matching the filter criteria from accumulated research. Serves from local SQLite — no API calls.

Return statistics about the local knowledge graph.

Shows entity counts, database size, and last activity — useful for understanding the breadth of your accumulated research.

Export accumulated research data for downstream analysis.

Returns all stored entities as JSON-serialisable dicts, suitable for:

• Loading into pandas DataFrames for ML feature engineering

• Importing into R or Julia for statistical analysis

• Feeding into downstream bioinformatics pipelines

Example (Python)::

import pandas as pd
result = await export_research_dataset(ExportInput(tables=["variants"]))
df = pd.DataFrame(result["data"]["variants"])
high_tier = df[df["clinical_tier"] == "HIGH"]

Traverse the local knowledge graph from a seed entity.

Given any seed (UniProt ID, gene symbol, or MONDO disease ID), expands up to max_hops through the drug-gene-disease graph stored in the local knowledge graph.

This reveals hidden connections between entities accumulated across multiple research sessions — a form of network medicine powered by your own research history.

Generate a multi-source variant interpretation report.

Cross-references evidence from up to eight upstream databases for a single HGVS variant into one structured report. The report is a research aid: it surfaces the upstream evidence and the ACMG/AMP criteria that the available evidence supports, but it is not a clinical interpretation and must not be used as a diagnostic without independent review by a qualified clinical laboratory.

1. Ensembl VEP — functional consequence, SIFT/PolyPhen/CADD predictions

2. ClinVar — clinical pathogenicity classifications and review status

3. gnomAD v4 — population allele frequencies across 807,162 individuals

4. AlphaMissense — deep-learning missense pathogenicity (Cheng et al. 2023)

5. MONDO — disease ontology context

6. Open Targets — disease-gene evidence scores

7. DisGeNET — curated gene-disease association scores

8. ChEMBL — approved drugs acting on the gene product

The report includes a draft ACMG/AMP criteria checklist with evidence mapping, a structural impact summary, and an actionability statement.

Comprehensive druggability assessment for a protein target.

Integrates four independent druggability signals into a HOT/WARM/COLD/NOT_DRUGGABLE classification:

1. Drug precedent — ChEMBL approved drugs + clinical compounds

2. Tractability — Open Targets tractability labels (small-molecule, antibody, PROTAC)

3. Structural confidence — AF2 pLDDT (ordered → analysable binding pockets)

4. Population constraint — gnomAD LOEUF (highly constrained → safety risk on inhibition)

It assembles existing public-database evidence into one tier; it does not add scientific judgement and is not a validated predictive model.

Generate a complete protein intelligence dossier from 7 data sources.

It assembles disease associations, drug precedent, population constraint, ClinVar variants, and cross-species orthologs for one protein into a single structured record. It composes upstream databases; it does not add scientific judgement.

Map the complete therapeutic landscape for a disease.

Returns approved drugs, pipeline agents, top druggable targets, and an investability summary for a given MONDO disease.

Combines Open Targets evidence with ChEMBL drug indications and MONDO disease hierarchy to produce a comprehensive landscape report used in business development, competitive intelligence, and R&D portfolio decisions.

Generate a draft ACMG/AMP variant classification framework.

Populates ACMG/AMP 2015 criteria (Richards et al.) automatically from computational evidence. Designed to pre-populate variant interpretation forms for clinical laboratory review — NOT a substitute for expert review.

Find clinical-stage drugs that may be repurposed for a disease.

Analyze AlphaFold structural confidence using pLDDT and PAE matrices.

Returns a multi-layered structural reliability assessment:

• pLDDT (per-residue): mean confidence, low-confidence segments (disordered/novel)

• PAE (predicted aligned error): inter-domain uncertainty, domain boundaries

• Druggability pre-screen: high-pLDDT + low-PAE regions → ordered pockets

Compute a topological fingerprint for a protein structure.

Uses persistent homology (Vietoris-Rips filtration) over the Cα coordinate cloud to derive a 64-dimensional fingerprint vector and Betti numbers β₀, β₁, β₂. Requires gudhi (install with pip install alphafold-sovereign-mcp[tda]); without gudhi, a coarse fallback runs that does not compute persistent homology (see _fallback_tda_fingerprint).

What the Betti numbers count, intuitively:

• β₀ — connected components of the Vietoris-Rips complex at the chosen filtration scale. Distinguishes single-domain from multi-domain or fragmented chains.

• β₁ — 1-dimensional holes / loops. Picks up ring-like topology (e.g. β-barrels, large macrocycles).

• β₂ — 2-dimensional voids. Picks up enclosed cavities.

Topological features are invariant to rigid-body rotation and translation. They are not a substitute for sequence alignment, RMSD, or functional homology assessment; they are a coarse, geometry-only summary.

Compare multiple proteins using a TDA-fingerprint distance.

Computes a pairwise distance matrix between the TDA fingerprints of the provided proteins. Distance metric: L2 distance between length-normalised 64-dimensional fingerprint vectors (see _fingerprint_distance). Distance = 0 means identical fingerprints; larger values mean more divergent fingerprints. This is not a Wasserstein distance between persistence diagrams.

Applications: Possible uses (all of which require independent validation before any downstream use):

• Drug-repurposing triage: proteins with low fingerprint distance may share gross topology.

• Off-target screening: family members with near-zero distance.

• Cross-species comparison of the same gene's structure.

None of these are direct functional or sequence-similarity measures.

Quantify cross-species structural and sequence divergence for a gene.

For each ortholog, attempts to fetch the AlphaFold structure and compute a TDA fingerprint distance against the human structure. When an ortholog structure is available in AlphaFold DB, the divergence_method is tda_fingerprint and the distance is the L2 distance between length-normalised fingerprint vectors. When the ortholog has no AlphaFold model, the method falls back to sequence_identity (1 - identity/100).

AlphaFold DB coverage of non-human proteomes is partial: model organisms (mouse, rat, zebrafish) are well-covered; others may not be. The divergence_method field on each result tells you which method was used.

Identify and score putative binding pockets from AlphaFold geometry.

Detects pockets with a geometry-only heuristic. Residues in the inner 60 percent of the structure by distance from the centroid are treated as buried, then grown greedily into clusters within an 8 Angstrom radius. A cluster is kept as a putative pocket when it has at least min_pocket_residues members and a mean pLDDT of at least 50.

Each pocket reports a radius of gyration (compactness of the pocket residues), a burial value (distance of the pocket centroid from the structure centroid), a mean pLDDT, and a druggability index. The druggability index runs 0 to 100 and is the sum of four equally weighted 0 to 25 sub-scores: residue count, radius of gyration, mean pLDDT, and burial.

This is a fast, dependency-free pre-screen, not a substitute for a validated pocket detector such as fpocket or P2Rank. It needs no ML model, is fully reproducible from AlphaFold coordinates, and runs in air-gapped deployments.

Map intrinsically disordered regions (IDRs) using pLDDT as proxy.

IDRs with pLDDT < 50 are predicted to be disordered in isolation by AlphaFold. This approach is validated by Ruff & Pappu (2021) and is the highest-throughput IDR detection method available for the full human proteome.

IDR functional categories returned:

• Linkers: short (< 20 aa) disordered regions between domains

• Tails: N/C terminal IDRs

• Long IDRs: candidate intrinsically disordered protein (IDP) segments

Clinical relevance:

• IDRs are enriched for disease-causing mutations (40% of cancer driver mutations)

• IDRs host post-translational modification sites (phosphorylation, ubiquitination)

• Long IDRs are emerging drug targets (targeted covalent inhibitors, phase separation modulators)