{
  "abstract": "Post-translational modifications (PTMs) are major regulators of G protein-coupled receptor (GPCR) signaling, trafficking and surface expression, yet residue-level evidence is fragmented across databases and computational predictions often ignore the compartment constraints imposed by the seven-transmembrane topology. We developed GPCR-PTM, an interpretable framework that integrates database-backed annotations with topology-constrained, rule-based prediction of phosphorylation, N-linked glycosylation, S-palmitoylation and ubiquitination. Sites are separated into Verified (UniProt ECO:0000269 experimental annotation), Supported (other database-backed evidence) and Predicted (rule-based) evidence layers; database citations are screened at the PubMed title/abstract level; and predicted sites are ranked using explicit sequence rules, membrane topology and evolutionary conservation. Across 839 reviewed human GPCRs, the pipeline consolidated 9559 source records into 7247 unique database-backed PTM sites spanning 827 receptors. Among 13,453 merged citation-site pairs, 13,216 (98.2%) had retrievable cached abstracts, but only 353 (2.7%) were residue-explicit, showing that abstract granularity should not be equated with evidence quality. In a retrospective benchmark against 246 UniProt ECO:0000269 experimental sites, the rule system recovered 216 sites (87.8%), including 75.4% of phosphorylation sites and all evaluable N-linked glycosylation and S-palmitoylation sites. Because the glycosylation and palmitoylation rules encode the canonical recognition patterns themselves, complete recovery for these classes reflects rule coverage rather than predictive discrimination; phosphorylation, the least pattern-bound class, reached a strict precision of 7.0% and an AUPRC of 0.074, so the overall recovery should be read as coverage under blinded lookup rather than calibrated predictive performance. Compartment-matched enrichment further supported the pXpp phosphorylation cluster (3.19-fold enrichment) and confirmed the expected localization of canonical N-glycosylation and juxtamembrane palmitoylation rules. Interactive bilingual reports combine evidence filtering, a PTM landscape overview, topology-aware sequence annotation and AlphaFold-based 3D context. GPCR-PTM is intended as an auditable hypothesis-generation resource for basic GPCR research and pharmacology rather than a calibrated probability predictor. Prospective validation and direct comparison with learned predictors will be required to quantify predictive performance on previously unseen biology.",
  "abstract_shown": true,
  "affiliations": [
    "AlphaMol Science Ltd., Shenzhen, 518000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yinzhen Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Shiyu Wang"
    },
    {
      "name": "Shuguang Yuan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1gz3rd7bhpmwc.bib",
  "cited_by": 0,
  "doi": "10.1016/j.medidd.2026.100267",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2590098626000217",
  "id": "1gz3rd7bhpmwc",
  "issns": [
    "2590-0986"
  ],
  "landing_url": "https://doi.org/10.1016/j.medidd.2026.100267",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RS1-441"
    ],
    [
      "openalex",
      "1312|Machine Learning in Bioinformatics"
    ]
  ],
  "openalex": "W7222222499",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/1gz3rd7bhpmwc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:53Z",
      "native_id": "42dcf4c3c6ff4155a1bf1a1e074ec9d7",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.medidd.2026.100267"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "GPCR-PTM: an interpretable, topology-aware rule-based framework for residue-level post-translational modification annotation and prediction across the human GPCRome",
  "title_text": "GPCR-PTM: an interpretable, topology-aware rule-based framework for residue-level post-translational modification annotation and prediction across the human GPCRome",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:53Z",
  "url": "https://research.draconitic.com/paper/1gz3rd7bhpmwc",
  "venue": "Medicine in Drug Discovery",
  "versions": []
}