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 the top drug targets for a curated set of common diseases in one call.

Use this for a fast landscape scan across a whole disease area: given a category (e.g. 'oncology'), it looks up the curated MONDO diseases in that category and returns each one's top Open Targets evidence-scored targets, in parallel. To profile a single disease you already have a MONDO ID for, use get_disease_targets instead — this tool is its category-level, multi-disease counterpart and does not accept a raw MONDO ID.

Queries Open Targets live. Returns a JSON string with the category, the number of diseases profiled, and a profile object mapping each disease to its MONDO ID and top targets (per-disease errors are reported inline, not raised). Returns a JSON error object listing the valid values when the category — or a disease_name filter within it — is not recognised.

Comprehensive clinical triage for a missense variant.

Fuses the upstream signals this tool currently wires into a single prioritised report:

1. Pathogenicity — ClinVar interpretation + review status. The alphamissense_score / alphamissense_interpretation fields are always null / "Not available" here: AlphaMissense is not wired into this tool. For an AlphaMissense pathogenicity score use generate_variant_clinical_report.

2. Population genetics — gnomAD LOEUF / pLI gene-constraint scores. Per-variant allele frequencies and the per-ancestry breakdown are not wired into this tool.

3. Disease associations — a placeholder note pointing at get_target_diseases(); the Open Targets / MONDO traversal is a roadmap (Wave-3) item.

4. Structural context — a text note pointing at analyze_structural_confidence (resolve the gene to a UniProt accession first); the AlphaFold pLDDT / PAE join into this report is a roadmap (Wave-3) item.

Returns a pathogenicity_tier: HIGH / MEDIUM / LOW / UNKNOWN (derived from ClinVar; the AlphaMissense input is always absent here).

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 analyze_structural_confidence to retrieve AlphaFold structural confidence (pLDDT/PAE).

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 stored variants.

Returns variants matching the filter criteria. No upstream API calls are made — all data is served from the local SQLite knowledge graph, which is populated by the curated boot seed and by any explicit writes through the knowledge-graph storage API (the analysis tools do not write to it on their own).

Recall proteins already stored in the local knowledge graph.

This is a local-recall query, not a live lookup: it returns only proteins that have previously been written to the local SQLite store (the curated boot seed, plus anything added through the knowledge-graph storage API). No upstream API is called. To assess a protein that may not be stored yet, use assess_target_druggability (druggability tier) or analyze_structural_confidence (pLDDT), which query live sources.

Filters are combined with AND; omit a filter to leave that dimension unconstrained. Returns a JSON record with the applied query, a result_count, and the matching proteins rows. The list is empty when nothing stored matches — common when only the boot seed is loaded, so a broad filter returning few rows usually means the store is small, not that no such protein exists.

Return statistics about the local knowledge graph.

Shows entity counts, database size, and last activity — useful for understanding the current contents and coverage of the local store.

Export the stored knowledge-graph 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 a seed (UniProt ID, gene symbol, or MONDO disease ID), expands its immediate neighbourhood in the stored drug-gene-disease graph: a gene symbol resolves to its encoded proteins and reported variants, a UniProt accession resolves to its stored protein record, and a MONDO disease resolves to drugs with an indication for it. The store is populated by the curated boot seed and by explicit writes through the storage API.

Generate a multi-source variant interpretation report.

Cross-references evidence from up to seven 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 (gnomAD v4, ~807k individuals)

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

5. Open Targets — disease-gene evidence scores

6. DisGeNET — curated gene-disease association scores

7. 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.

Rank existing clinical-stage drugs as repurposing candidates for a disease.

Surfaces approved or trial-stage drugs whose target carries genetic/association evidence for the disease — i.e. drug-repurposing hypotheses. For the full approved-plus-pipeline drug picture of a disease (not only repurposing candidates), use map_disease_drug_landscape instead.

How it works: take the top target_limit Open Targets evidence-scored targets for the disease; for each, fetch its ChEMBL drugs at or above min_phase; drop duplicate molecules; rank by composite_repurposing_score = OT evidence score × (max ChEMBL phase / 4). Ranking uses clinical and association evidence only — no protein structure.

Returns a JSON record whose candidates list holds up to 20 drugs (each with ChEMBL ID, name, max phase, target gene/UniProt, OT evidence score, composite score, and mechanism), plus the candidate count and the methodology string. Returns an empty candidates list with a message when the disease has no Open Targets associations. The composite score is a prioritisation aid, not an efficacy prediction — validate mechanistically before acting on it.

Analyze AlphaFold structural confidence using pLDDT and PAE.

Returns a structural reliability summary (not a per-residue profile):

• pLDDT: the model's mean confidence (AlphaFold DB globalMetricValue) plus a coarse confidence tier

• PAE (predicted aligned error): mean and max inter-residue uncertainty and PAE-derived domain boundaries

• Druggability pre-screen: an ordered-fraction estimate and a structure-based-drug-design suitability flag

Compute a rotation-invariant topological fingerprint of a protein's fold.

Fetches the AlphaFold model for uniprot_id and runs persistent homology (a Vietoris-Rips filtration over the Cα point cloud) to produce a 64-dimensional fingerprint vector plus Betti numbers β₀, β₁, β₂. Use it as the per-protein input to structure-similarity comparisons: compare_proteins_topologically and find_evolutionary_structural_shifts consume these fingerprints.

The Betti numbers summarise fold topology: β₀ counts connected components (single- vs multi-domain or fragmented chains), β₁ counts loops/holes (β-barrels, large macrocycles), β₂ counts enclosed voids (cavities). Because they are invariant to rotation and translation, two orientations of the same fold produce the same fingerprint.

Returns the fingerprint vector, the Betti numbers, the residue count, and which method ran. Full persistent homology needs the optional [tda] extra (gudhi); without it a coarse fallback runs that does NOT compute persistent homology, and the result flags this. Returns a no-structure result when AlphaFold DB has no model for the accession. This is a coarse, geometry-only summary — not a substitute for sequence alignment, RMSD, or functional-homology assessment.

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 taken as the pocket-forming core, 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 centroid offset (distance of the pocket centroid from the structure centroid; larger means more peripheral — a solvent-accessible cleft rather than a dead-central cavity, and NOT a measure of solvent burial), 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 centroid offset.

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 pLDDT-as-disorder-proxy approach is consistent with Ruff & Pappu (2021) and scales to the full human proteome from precomputed AlphaFold confidence.

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

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

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

Retrieve a protein's AlphaFold model: metadata, download URLs, optional coordinates.

The single entry point for getting the predicted structure itself. Returns the AlphaFold DB entry metadata — entry ID, model version and creation date, organism, gene, UniProt description, the amino-acid sequence and its length, and the model's mean pLDDT — plus stable download URLs for the PDB and mmCIF coordinate files, the PAE matrix and image, and the AlphaMissense substitutions CSV. Set include_coordinates to embed the full PDB coordinate text directly.

Use the sibling structure tools for interpretation rather than retrieval, so their scopes don't overlap: analyze_structural_confidence for a pLDDT/PAE confidence read, score_binding_pocket_geometry for pockets, compute_topology_fingerprint for fold topology, and detect_intrinsically_disordered for disorder. This tool hands you the structure and its handles; it does not score or interpret the model.

Returns structure_available: false with an explanatory note when AlphaFold DB has no model for the accession — an expected coverage gap, not a server fault.