home / scout

Targets — browse / sort / filter (view)

One row per human protein target with names + key evidence. Click column headers to SORT; use the facets to FILTER (tier, activation pair, known aptamer, EV-Map). UniProt IDs link to the source. Default sort is evidence_priority — a DETERMINISTIC, reproducible score computed from harvested evidence (structure / disease / predicted-surface / EV-detection). Filter has_structure=1 for the core set: targets with a reported 3D structure (the requirement the Kd layer is built around).

Data license: CC BY 4.0 · Data source: apt-scout automated curation pipeline (E. Dohi, NCNP) — values harvested from public databases; raw source stored per target

id
UniProt accession (human). Links to uniprot.org.
gene_symbol
Gene symbol (e.g. ITGB3).
protein_name
Protein name (UniProt).
tier
Tier 1 = PDB-anchored; Tier 1.5 = AlphaFold-confident.
has_structure
1 if the target protein has a reported experimental 3D structure (PDB). This is the CORE requirement the aptamer-Kd layer is built around — filter to 1 for the structure-backed set.
evidence_priority
DETERMINISTIC, reproducible prioritisation (0-1) computed at build time from real harvested columns: 0.35*experimental-structure(PDB=1 / AlphaFold-only=0.3) + 0.30*Open-Targets-top-disease-score + 0.20*PREDICTED-surface-accessibility(A_surface=1) + 0.15*EV-detection(EV-Map). A transparent ranking AID, not a validation; the surface/EV terms are PREDICTIONS/DETECTIONS, not measured EV-surface exposure. Recomputable exactly from this DB.
has_activation_state_pdb_pair
1 if a curated ACTIVE/INACTIVE PDB pair exists (only 11 targets).
in_cev_map
Detected in the EV-Map plasma-EV dataset (Rai & Greening 2025, Nat Cell Biol, 10.1038/s41556-025-01795-7) = 3,422 apt-scout targets (broad detected proteome). The EV-Map CONSERVED EV proteome is 182 proteins (42 non-EV); apt-scout matches 104 of these conserved EV-hallmark proteins as targets by gene (the other 78 are not apt-scout targets). Use the ev_hallmark_targets query for that subset. IMPORTANT: EV-Map detection means the protein is EV CARGO (present in the vesicle) — it does NOT mean surface-exposed. Rai & Greening 2025 run a separate membrane-impermeant biotinylation assay giving a 151-protein SURFACEOME, and show the conserved SDCBP/syntenin is luminal (not surface-accessible). For genuine EV-surface accessibility use that 151-protein set, not EV-Map presence.
has_known_aptamer
1 if PubMed '(gene) AND (aptamer OR SELEX)' returned any hit (KEYWORD co-mention, includes false positives — NOT a verified aptamer). For verified aptamers with Kd, see the Binding-affinities (v_kd) layer.
opentargets_top_disease_score
Open Targets association score (0-1).
aptamer_count_pubmed
Number of PubMed hits for (this protein) AND (aptamer OR SELEX). Keyword co-occurrence — verify each (many co-mention without a real aptamer).
aptamer_pmids
The actual PubMed IDs behind the aptamer evidence — click each to read the paper. This is the source of the 'has aptamer' claim.
surface_class
PREDICTED membrane topology (HPA-derived, not a measurement): A_surface = integral/ecto cell-surface (predicted EV-surface accessible); A2_pm_peripheral = plasma-membrane cytoplasmic-leaflet (SRC/LYN/RHOA, predicted not reachable); A_assoc = secreted/corona; B_cargo = luminal cargo; unknown = no HPA localization. Lipid asymmetry can partially flip (PS via scramblase); confirm by protease-protection / intact-EV surface labelling. See v_surface_targets.

852 rows where has_activation_state_pdb_pair = 0 and surface_class = "unknown" sorted by evidence_priority descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: aptamer_count_pubmed

tier 2

  • Tier 1.5 677
  • Tier 1 175

in_cev_map 2

  • 0 560
  • 1 292

has_structure 2

  • 0 675
  • 1 177

has_known_aptamer 2

  • 0 844
  • 1 8

has_cryoEM 2

  • 0 787
  • 1 65

surface_class 1

  • unknown · 852 ✖

has_activation_state_pdb_pair 1

  • - · 852 ✖
id gene_symbol protein_name tier evidence_priority ▲ has_structure surface_class pdb_count_total alphafold_mean_pLDDT has_cryoEM has_activation_state_pdb_pair activation_state_pdb_active activation_state_pdb_inactive has_known_aptamer aptamer_count_pubmed aptamer_pmids in_cev_map opentargets_top_disease_name opentargets_top_disease_score
P10721     Tier 1 0.768 1 unknown 52 78.19 0 0     0 0   1 gastrointestinal stromal tumor 0.8922566232622926
Q07889     Tier 1 0.761 1 unknown 91 76.38 0 0     0 0   1 Noonan syndrome 0.8708318719184144
P06865     Tier 1.5 0.759 1 unknown 2 93.44 0 0     0 0   1 Tay-Sachs disease 0.8629597356732491
Q05086     Tier 1 0.753 1 unknown 27 80.75 1 0     0 0   1 Angelman syndrome 0.8429347259838658
P06400     Tier 1 0.752 1 unknown 19 76.06 0 0     0 0   1 retinoblastoma 0.8408754966665243
P04275     Tier 1.5 0.749 1 unknown 48 75.5 1 0     0 0   1 Von Willebrand disease 0.8296081124598669
P56589     Tier 1.5 0.749 1 unknown 2 88.88 0 0     0 0   1 Zellweger syndrome 0.8285793075049609
P52732     Tier 1 0.747 1 unknown 62 74.38 0 0     0 0   1 microcephaly with or without chorioretinopathy, lymphedema, or intellectual disability 0.8220137873148912
Q15858     Tier 1 0.747 1 unknown 43 69.06 1 0     0 0   1 primary erythermalgia 0.8237835765987434
Q04771     Tier 1 0.745 1 unknown 85 83.12 0 0     0 0   1 fibrodysplasia ossificans progressiva 0.8164474171712364
Q9NP72     Tier 1 0.745 1 unknown 1 85.56 0 0     0 0   1 Micro syndrome 0.816071079017092
P35222     Tier 1 0.743 1 unknown 50 81.06 0 0     0 0   1 severe intellectual disability-progressive spastic diplegia syndrome 0.8101632011131414
P02751     Tier 1 0.74 1 unknown 69 69.62 0 0     0 0   1 spondylometaphyseal dysplasia, 'corner fracture' type 0.8013288836166759
Q92608     Tier 1 0.734 1 unknown 6 79.81 1 0     0 0   1 DOCK2 deficiency 0.7814025939223274
O43318     Tier 1 0.73 1 unknown 25 69.0 0 0     0 0   1 frontometaphyseal dysplasia 2 0.767839253516465
P84243     Tier 1 0.719 1 unknown 100 85.94 0 0     0 0   1 Bryant-Li-Bhoj neurodevelopmental syndrome 2 0.7290694534626342
Q8TD19     Tier 1 0.714 1 unknown 2 73.94 0 0     0 0   1 NEK9-related lethal skeletal dysplasia 0.7132294892240114
Q9H6S3     Tier 1 0.705 1 unknown 2 73.06 0 0     0 0   1 hearing loss, autosomal recessive 0.6818863306678549
Q5T5Y3     Tier 1 0.694 1 unknown 4 54.34 1 0     0 0   1 cortical dysplasia, complex, with other brain malformations 12 0.6471266259726068
P17948     Tier 1 0.685 1 unknown 12 72.62 0 0     0 0   1 neoplasm 0.6169964471592176
P55210     Tier 1 0.684 1 unknown 47 81.69 0 0     0 0   1 cataract 0.6121422420887519
P01861     Tier 1 0.676 1 unknown 15 86.75 0 0     0 0   1 cutaneous Leishmaniasis 0.5868213846274001
Q08945 SSRP1 FACT complex subunit SSRP1 Tier 1 0.675 1 unknown 11 74.19 1 0     1 1 39653795 1 HIV infection 0.5844684701481319
P27694     Tier 1 0.672 1 unknown 48 83.81 0 0     0 0   1 pulmonary fibrosis and/or bone marrow failure, telomere-related, 6 0.5720870296163543
Q96PU8     Tier 1 0.668 1 unknown 1 68.69 0 0     0 0   1 cancer 0.5616375835401949
P45985     Tier 1 0.665 1 unknown 4 77.0 0 0     0 0   1 neurodegenerative disease 0.5507437937196141
P17676     Tier 1 0.661 1 unknown 16 59.69 0 0     0 0   1 neurodegenerative disease 0.5352429213193124
O14618     Tier 1 0.658 1 unknown 7 87.38 0 0     0 0   1 neurodegenerative disease 0.5272415940824882
P54764     Tier 1 0.656 1 unknown 17 83.5 0 0     0 0   1 medullary thyroid gland carcinoma 0.5195314334008982
P30419     Tier 1 0.655 1 unknown 52 83.25 0 0     0 0   1 neurodegenerative disease 0.5174483590720084
Q8N1W1     Tier 1 0.654 1 unknown 2 61.56 0 0     0 0   1 hearing loss 0.5135177841424435
P43487     Tier 1 0.651 1 unknown 4 83.38 1 0     0 0   1 HIV infection 0.5030392466541774
Q16611     Tier 1 0.639 1 unknown 55 81.31 0 0     0 0   1 chronic lymphocytic leukemia 0.46377569349166126
Q13432     Tier 1 0.634 1 unknown 9 77.88 0 0     0 0   1 idiopathic CD4 lymphocytopenia 0.4456292509980772
Q9BXB4     Tier 1 0.633 1 unknown 1 74.06 0 0     0 0   1 neurodegenerative disease 0.4435303946057042
Q14213     Tier 1 0.628 1 unknown 4 87.62 1 0     0 0   1 neurodegenerative disease 0.42776938116451996
F8WCM5     Tier 1 0.624 1 unknown 4 48.81 1 0     0 0   1 neurodegenerative disease 0.4145162522265602
P57735     Tier 1 0.621 1 unknown 2 86.62 0 0     0 0   1 atrial fibrillation 0.40258191116027553
P09603     Tier 1.5 0.618 1 unknown 8 57.41 0 0     0 0   1 type 2 diabetes mellitus 0.39219340856589974
Q13131     Tier 1 0.614 1 unknown 12 79.56 1 0     0 0   1 cardiovascular disease 0.37913864498012223
O75365     Tier 1.5 0.612 1 unknown 4 86.88 0 0     0 0   1 hypertension 0.3732487322932303
P07360     Tier 1 0.611 1 unknown 15 89.75 1 0     0 0   1 complement deficiency 0.3695798546847018
P20339     Tier 1 0.611 1 unknown 14 84.88 1 0     0 0   1 tuberculosis 0.3708733841760982
P08034     Tier 1 0.604 1 unknown 15 80.25 1 0     0 0   0 Charcot-Marie-Tooth disease X-linked dominant 1 0.8483281160610058
Q96HW7     Tier 1 0.599 1 unknown 11 83.19 1 0     0 0   1 systemic lupus erythematosus 0.3316421846062003
Q8IYB1     Tier 1 0.598 1 unknown 1 83.56 0 0     0 0   1 Hashimoto's thyroiditis 0.32654837821466426
Q8NBJ9     Tier 1 0.598 1 unknown 7 80.25 1 0     0 0   1 hypothyroidism 0.32686531914177924
P23415     Tier 1 0.597 1 unknown 9 84.0 1 0     0 0   0 hereditary hyperekplexia 0.822516157871453
Q15061     Tier 1 0.593 1 unknown 3 69.62 1 0     0 0   1 triple-negative breast cancer 0.31136131887350776
Q5IJ48     Tier 1.5 0.593 1 unknown 1 76.44 0 0     0 0   0 ventriculomegaly-cystic kidney disease 0.8112741813611002

Next page

Advanced export

JSON shape: default, array, newline-delimited

CSV options:

CREATE VIEW v_targets AS
SELECT f.id, n.gene_symbol, n.protein_name, tg.tier,
 -- evidence_priority: DETERMINISTIC, reproducible from real DB columns (no LLM).
 -- Transparent weights over harvested evidence: structure 0.35, disease importance
 -- 0.30 (Open Targets top-disease score), PREDICTED surface accessibility 0.20,
 -- EV detection 0.15. A heuristic ranking aid, not a validation; surface/EV terms
 -- are predictions/detections, NOT measured EV-surface exposure.
 ROUND(
   0.35*(CASE WHEN CAST(COALESCE(f.pdb_count_total,'0') AS INTEGER)>0 THEN 1.0
              WHEN CAST(COALESCE(NULLIF(f.alphafold_mean_pLDDT,''),'0') AS REAL)>0 THEN 0.3 ELSE 0.0 END)
 + 0.30*MIN(CAST(COALESCE(NULLIF(f.opentargets_top_disease_score,''),'0') AS REAL), 1.0)
 + 0.20*(CASE sc.surface_class WHEN 'A_surface' THEN 1.0 WHEN 'A_assoc' THEN 0.5 WHEN 'A2_pm_peripheral' THEN 0.2 ELSE 0.0 END)
 + 0.15*(CASE WHEN CAST(COALESCE(f.in_cev_map,0) AS INTEGER)=1 THEN 1.0 ELSE 0.0 END)
 , 3) AS evidence_priority,
 (CASE WHEN CAST(COALESCE(f.pdb_count_total,'0') AS INTEGER)>0 THEN 1 ELSE 0 END) AS has_structure,
 sc.surface_class,
 f.pdb_count_total, f.alphafold_mean_pLDDT, f.has_cryoEM,
 f.has_activation_state_pdb_pair, f.activation_state_pdb_active, f.activation_state_pdb_inactive,
 f.has_known_aptamer, f.aptamer_count_pubmed, ap.aptamer_pmids,
 f.in_cev_map, f.opentargets_top_disease_name, f.opentargets_top_disease_score
FROM v_target_full f
LEFT JOIN target_names n ON n.target_id=f.id
LEFT JOIN targets tg ON tg.id=f.id
LEFT JOIN target_aptamer_pmids ap ON ap.target_id=f.id
LEFT JOIN membrane_surface_class sc ON sc.target_id=f.id;
Powered by Datasette · Queries took 787.39ms · Data license: CC BY 4.0 · Data source: apt-scout automated curation pipeline (E. Dohi, NCNP) — values harvested from public databases; raw source stored per target