Deorphanize a peptide: given a peptide sequence plus an observed phenotype (and often a hypothesized target the peptide does NOT actually bind), find its likely real protein target(s) — using ToolUniverse's keyless characterization, homology, target-family, phenotype, cross-species, and (optional, key-gated) co-folding tools.
The target can be anything, not just a GPCR. A target-class router (GPCR ligand / ion-channel toxin / protease target / cytokine or growth-factor receptor / integrin ligand / antimicrobial / unknown) classifies the peptide up front and adapts the enumeration strategy. GPCRs are the best-trodden case (and the validated control), but the pipeline's spine — homology, motif, phenotype, cross-species, co-fold — is target-class-agnostic, and family enumeration uses HGNC gene-family (general) + InterPro (general) + GPCRdb (GPCR-only cross-check).
Core reasoning: LOOK UP, DON'T GUESS
The failure mode this skill defends against is guessing a target from the peptide's name or its assumed mechanism. A peptide can be phenotypically active yet not bind the hypothesized target — because it hits a paralog, a different family member, or the same receptor in a different species whose binding interface has diverged. So:
- Never assert a target from memory. Every candidate must come from a tool result (homology hit, family enumeration, phenotype association, or structural co-fold), with the tool name and accession recorded.
- Anchor on PHENOTYPE × STRUCTURE/SEQUENCE plausibility, not on the peptide's reputed mechanism. The real target is the intersection of (a) what the sequence/motif/structure says it could bind and (b) what the phenotype says is biologically relevant. A name-level guess ("it's a GLP-1 analog so it's GLP1R") is exactly what produces off-target errors.
- Reconcile across species. "Binds in species A but not B" is usually interface sequence divergence, not a different target. Always pull the ortholog set and align the candidate receptor's ligand-binding interface across the assay species before concluding the peptide "doesn't work."
- A non-binding result against the hypothesized target is a clue, not a dead end. It promotes the paralogs and phenotype-shared receptors to the top of the candidate list.
This skill is built on validated, mostly keyless tools (BLAST, ELM/PROSITE, GPCRdb/HGNC/GtoPdb, OpenTargets, EnsemblCompara/Alliance). The single key-gated step is the optional structural confirmation by co-folding (NVIDIA NIM), used only to rank an already-narrowed shortlist.
Automated pipeline (scripts) — the fast path
Two runnable scripts in scripts/ execute the whole pipeline so you don't have to chain the phase calls by hand. Both load ToolUniverse via the SDK and run from the repo root.
deorphanize_peptide.py — keyless candidate generation + ranking (Phases 1–4)
No API key. For each peptide it characterizes it (PepCalc/ProtParam), flags non-canonical/cyclic residues, scans PROSITE + ELM signatures, flags protease/degradation liability, classifies the target class (GPCR / channel / protease / cytokine-receptor / integrin / …) and enumerates the candidate target family accordingly, anchors on phenotype (OpenTargets), and — for the top candidates — resolves the ortholog protein sequences and aligns the binding interface across human / assay-species / source-species (the mechanistic "binds in A, not B" step) and suggests a ClusPro-ready PDB structure. Prints a ranked candidate shortlist with evidence tiers.
python3 scripts/deorphanize_peptide.py \
--sequence <PEPTIDE_SEQ> \ # OR --fasta peptides.fasta for BATCH mode
--hypothesized-target <GENE> \ # optional; seeds family enumeration (e.g. GLP1R). OMIT for SEEDLESS mode
--phenotype "<disease name>" \ # optional, REPEATABLE; OpenTargets anchor (use the DISEASE node, not a symptom) — pass several to union plausible phenotypes
--assay-species mus_musculus \ # species of the NEGATIVE binding assay
--source-species <organism> \ # optional; species where binding WAS observed -> 3-way interface alignment
[--no-blast] [--out result.json]
Modes:
- Seeded (
--hypothesized-target GLP1R) — enumerate that gene's family as the candidate panel (cleanest). Even seeded, the sequence-derived candidates (below) are always unioned in, so a wrong hypothesized seed cannot blind the search to the real target's family — exactly the deorphanization premise. - Seedless (omit it) — derive candidate targets from PROSITE and BLAST-homolog keywords × the target-class nouns (e.g.
receptor/channel/protease, chosen by the router) via UniProt; degrades to phenotype-only if that resolver is transiently down. No longer receptor-only. - Multi-phenotype —
--phenotypeis repeatable; pass every plausible disease and the anchor is the union (max score per target). Best when you don't know the single right phenotype. - Batch (
--fasta) — one record per FASTA entry, sharing--phenotype/--assay-species.
Extra signals it always reports:
- Target class — the router's call (gpcr_ligand / ion_channel_toxin / protease_inhibitor_or_substrate / cytokine_or_growth_factor / integrin_ligand / guanylyl_cyclase_ligand / antimicrobial / unknown) with the evidence that triggered it and the seedless nouns it selected. This is what makes the skill general rather than GPCR-only.
- DPP4 / protease liability — a peptide can be assay-negative because it is cleaved, not because it fails to bind. Native GLP-1 (
A@P2) is DPP4-LABILE; exendin-4 (G@P2) is resistant. A labile flag triggers a "re-test with a DPP4 inhibitor or protease-resistant analog" note — a key alternative explanation for "works in vitro, not in the mouse assay." - ELM LIG motifs (ranked by rarity) + the Pfam binding domain each engages — low-confidence context for peptides without a named PROSITE family.
- Non-canonical / cyclic flag — any residue outside the 20 standard L-amino acids is surfaced, because BLAST/PROSITE/ProtParam silently assume a canonical linear peptide and will mischaracterize a non-ribosomal/cyclic peptide (common for unicellular-organism natural products). Look such peptides up by name with
Norine_get_peptideand pass--cyclictocofold_screen.py. - Cross-species interface alignment (top ≤3 candidates) — resolves each candidate's human + assay-species (+ optional source-species) ortholog sequence (UniProt) and aligns them (
EBI_msa_align), reporting per-pair % identity and substitution count. A low human-vs-assay identity flags the ortholog whose binding interface most plausibly diverged — the mechanistic answer to "binds in A, not B". If the source organism is a protist absent from UniProt, it reportsinsufficientand tells you to supply the partner sequence by hand. - ClusPro-ready PDB (top ≤3 candidates) —
PDBeSIFTS_get_best_structuresresolves a representative solved PDB id you can feed straight toClusPro_submit_peptide_docking.
Validated on the control (--sequence HGEGTFTSDLSKQMEEEAVRLFIEWLKNGGPSSGAPPPS --hypothesized-target GLP1R --phenotype "type 2 diabetes mellitus"): recovers the class-B panel {GCGR, GHRHR, GIPR, GLP1R, GLP2R, SCTR}, flags GLP1R as hypothesized (tested negative), and promotes GIPR to Tier 1 (family + phenotype, score 0.674) as the leading real-target hypothesis — exactly the deorphanization re-ranking, produced with zero API keys.
cofold_screen.py — structural confirmation (Phase 5, key-gated)
Co-folds the peptide against each shortlisted receptor and ranks by interface confidence (ipTM). Requires NVIDIA_API_KEY; without it, runs a DRY RUN that still resolves every receptor sequence (GPCRdb → UniProt fallback) and prints the co-fold plan, so you can verify inputs before paying for GPU time.
python3 scripts/cofold_screen.py --peptide <SEQ> --candidates GIPR GCGR GLP2R \
[--backend boltz2|alphafold2_multimer|openfold3] [--assay-species mus_musculus] [--out cofold.json]
Use the scripts for the fast path. When you need to run, debug, or extend a single step by hand, the full per-phase manual reference — every tool call the scripts automate, with exact parameter names, gotchas, fallback chains, runtime notes, and two fully worked examples — lives in references/phases.md. Read it when a script step fails, when you want to drive a phase manually, or when you extend the pipeline to a new tool.
The pipeline at a glance
Six phases; the scripts automate 1–4 (and the Phase-5 dry run). Full detail + exact tool calls + gotchas are in references/phases.md — read it before driving any phase by hand.
| Phase | What it does | Key tools |
|---|---|---|
| 0 Verify | Confirm every tool loads; substitute fallbacks | tooluniverse.cli run … |
| 1 Characterize + motif + classify | Properties, non-canonical/cyclic flag, PROSITE/ELM signature → ligand family, target-class router (GPCR/channel/protease/cytokine/integrin/…) | PepCalc/ProtParam, ScanProsite→PROSITE_get_entry, ELM_*, ESMFold |
| 2 Candidate generation | 4 independent routes — homology, motif→domain, target-family enumeration (class-aware), phenotype anchor → union | BLAST/EBI_msa_align, HGNC+InterPro+GPCRdb+GtoPdb, OpenTargets |
| 3 Cross-species | Resolve "binds in A not B": align the ortholog interface across human / assay / source species | EnsemblCompara, Alliance, UniProt, EBI_msa_align |
| 4 Narrow + rank | Score on sequence × phenotype × pharmacology × cross-species → shortlist ≤15 | (intersection logic) |
| 5 Structural confirm (optional, key-gated) | Co-fold top candidates, rank by interface ipTM; or academic-free ClusPro docking | NvidiaNIM_boltz2/…, ClusPro_submit_peptide_docking |
| 6 Report | Ranked shortlist with evidence tiers + wet-lab plan | (inline report) |
Core intersection rule: the real target is where phenotype-plausible (Route 2D) meets sequence/structure-plausible (Routes 2A–2C). A name-level guess ("it's a GLP-1 analog so it's GLP1R") is exactly what produces off-target errors — never assert a target the union of routes didn't surface.
Output format — ranked shortlist report (return inline, no extra files)
- Peptide characterization — length, MW, pI, GRAVY, instability, PROSITE/ELM signature, non-canonical/cyclic flag, fold confidence (if run).
- Ranked candidate table — one row per candidate target: gene + accession · target class · evidence tier · routes that surfaced it · OpenTargets phenotype score · known-pharmacology note (GtoPdb peptide ligands?) · cross-species status (
ortholog_one2one? interface % identity in the assay species?). - Cross-species reconciliation note — for the lead, the human-vs-assay(-vs-source) interface comparison and what it predicts for "binds A not B."
- Recommended wet-lab validation — binding/competition + a class-appropriate functional assay against the top ≤3 candidates and their assay-species orthologs (cAMP/β-arrestin for GPCRs; electrophysiology for channels; enzymatic/inhibition for proteases; reporter for cytokine receptors), with the GtoPdb-listed family antagonist as control where one exists.
Evidence tiers — always state which evidence is keyless/validated vs key-gated (co-fold not run), and flag any candidate that is a negative against the originally hypothesized target so the reader sees the re-ranking:
- Tier 1 (strong): ≥2 independent sequence/structure routes AND present in the phenotype anchor AND (if Phase 5 run) top interface ipTM.
- Tier 2 (moderate): 1 sequence/structure route + phenotype support, OR ≥2 sequence routes without phenotype.
- Tier 3 (weak/hypothesis): single-route only (deep paralog, or a phenotype-only hit not corroborated by sequence/structure).
Peptide Bioactivity Property Databases (supplementary characterization)
Separate from target deorphanization itself, ToolUniverse has 7 small, single-property peptide databases. None of them tell you what a peptide's target is — they each answer a narrower "does this peptide have property X" question, useful as supplementary evidence once a peptide is on your radar (e.g. flagging a candidate as also hemolytic/toxic before recommending it, or checking measured half-life before a stability claim). Query the one matching the property you actually need — searching all 7 for every peptide is wasted effort.
| Database | Tools | Property covered | Search fields |
|---|---|---|---|
| DBAASP | DBAASP_search_peptides, DBAASP_get_peptide |
Antimicrobial activity — per-target MIC values, terminus modifications, structure | sequence, name, target_species, target_group, kingdom, uniprot, dbaasp_id |
| AMPSphere | AMPSphere_search_amps, AMPSphere_get_family, AMPSphere_get_amp_distributions, AMPSphere_get_amp_features |
Antimicrobial peptides predicted from 863K+ prokaryotic metagenomic smORFs — family clustering, geographic/ecological distribution, physicochemical features (MW, pI, charge, molar extinction) | habitat, family, microbial_source, pep_length_interval, mw_interval, charge_interval, or a specific accession |
| ConoServer | ConoServer_search_conopeptides, ConoServer_get_conopeptide |
Cone-snail venom peptides (conotoxins) — sequence, PTMs, cysteine framework, gene superfamily, pharmacological family | name, sequence, pharmacological_family, gene_superfamily, cysteine_framework, organism, or conoserver_id |
| Hemolytik 2.0 | Hemolytik2_search_peptides |
Experimentally validated hemolytic/toxic activity | dataType in {nature, source, seq} + dataValue — no name-based search; use seq for a sequence/substring query |
| CancerPPD 2.0 | CancerPPD2_search_peptides |
Experimentally validated anticancer activity, by cancer type / cell line | dataType in {cancer_type, cell_line, seq} + dataValue |
| TumorHoPe 2 | TumorHope2_search_peptides |
Tumor-homing peptides used in drug delivery | source, target_tumor, name_source, conjugate (all optional filters) |
| PEPlife 2.0 | PEPlife2_search_peptides |
Experimentally measured half-life / proteolytic stability | dataType in {lin_cyc, org, seq} + dataValue — no name-based search, same pattern as Hemolytik2 |
Verified live behavior worth knowing before you rely on these:
- Hemolytik2 and PEPlife2 both reject a
name/dataTypevalue outside their fixed enum ({nature, source, seq}and{lin_cyc, org, seq}respectively) — there is no cross-database "search by peptide name" here except DBAASP, ConoServer, and TumorHope2'sname_source. If you only have a name, search DBAASP/TumorHope2 first, or search byseqif you have the sequence. - The
seqfilter on Hemolytik2/PEPlife2/CancerPPD2 is a loose substring/fragment match, not an exact-sequence lookup — querying a well-known peptide's full sequence can return unrelated peptides that merely share a short subsequence. Always check the returnedseqfield actually matches before treating a hit as the peptide you queried. ConoServer_search_conopeptideswas found live-broken at the time of writing — the underlying bulk XML download (conoserver.org/download/conoserver_protein.xml.gz) returned HTTP 403 on every attempt (3/3 intu test, repeated manual retries also failed), andConoServer_get_conopeptide(which loads the same bulk file) also failed on manual retry after passing once undertu test's cached run. Treat ConoServer as currently unreliable — check its live status with a cheap call before depending on it, and do not fabricate a conopeptide record if it 403s.- The other 6 databases (DBAASP, AMPSphere, Hemolytik2, CancerPPD2, TumorHope2, PEPlife2) were confirmed live and working at the time of writing.
Validation & test set
Validated on the exendin-4 → GLP1R control: recovers the class-B panel {GCGR, GHRHR, GIPR, GLP1R, GLP2R, SCTR}, flags GLP1R as the (negative) hypothesized target, and promotes GIPR to Tier 1 — the deorphanization re-ranking, produced with zero API keys. The full step-by-step (control and the real "binds in the source organism, not in mouse" case) is in references/phases.md.
Reproducible test prompts + checkable assertions are in evals/evals.json (the exendin-4 control, the source-organism/mouse-negative case, and a non-GPCR seedless case). To check the skill still behaves, run a prompt through it and verify the output against that eval's assertions.