{
  "abstract": "Motivation\n\nAccurate identification of antibody paratopes-the residues contacting the antigen-is vital for understanding antibody-antigen recognition, antibody engineering, and vaccine design. However, structure-based predictors require antibody structural inputs, which are often obtained from experimentally determined antibody-antigen complexes in benchmark settings but are generally unavailable for new sequences. Conversely, sequence-based predictors scale efficiently but remain largely antigen-agnostic, limiting their use of pair-specific antigen context.Results\n\nWe introduce ParaEM, an antigen-aware sequence-based paratope predictor that operates directly on antibody and antigen sequences. ParaEM leverages pretrained ESM3 embeddings to encode both sequences, augmenting antibody residues with trainable, complementarity-determining regions (CDR)-informed embeddings to introduce a biologically informed inductive bias. It models interactions via a residue-level compatibility scorer that defines latent antigen-anchor distributions. During training, a generalized Expectation-Maximization (EM) framework alternates between estimating label-conditioned posterior responsibilities (E-step) and performing gradient-based parameter updates (M-step). Evaluated across three established benchmarks, ParaEM outperforms existing sequence-based baselines in AUC-PR and achieves performance competitive with structure-based predictors without requiring explicit three-dimensional structural inputs. Ablation studies show consistent improvements from generalized EM optimization and an additional gain from CDR-informed embeddings. Antibody-side and antigen-information controls further show that antibody sequence carries substantial predictive signal, while the matching antigen provides additional predictive information. Together, these results support ParaEM as a scalable framework for incorporating matching-antigen context into sequence-based paratope prediction.Availability and implementation\n\nParaEM is available at https://github.com/kimtaegyuu/ParaEM. An archival snapshot of the software corresponding to this study is available on Zenodo at DOI: 10.5281/zenodo.22157100.",
  "abstract_shown": true,
  "affiliations": [
    "Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Republic of Korea.",
    "AIGENDRUG Co., Ltd, Seoul 08758, South Korea.",
    "School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.",
    "Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea.",
    "Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Taegyu Kim"
    },
    {
      "affs": [
        1
      ],
      "name": "Changyun Cho"
    },
    {
      "affs": [
        2
      ],
      "name": "Inuk Jung"
    },
    {
      "affs": [
        0,
        1,
        3,
        4
      ],
      "name": "Sun Kim",
      "orcid": "0000-0001-5385-9546"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/12yqdb6h36f4g.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag739",
  "fulltext_url": "https://europepmc.org/articles/PMC13676383",
  "id": "12yqdb6h36f4g",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://europepmc.org/article/MED/42834434",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|vaccines and immunoinformatics approaches"
    ]
  ],
  "openalex": "W7220657622",
  "pdf_url": "https://europepmc.org/articles/PMC13676383?pdf=render",
  "pmcid": "PMC13676383",
  "pmid": "42834434",
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/12yqdb6h36f4g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:10Z",
      "native_id": "MED:42834434",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42834434"
    },
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag739",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag739/8866641"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|vaccines and immunoinformatics approaches",
      "subfield": "molecular-biology"
    }
  ],
  "title": "ParaEM: antigen-aware sequence-based paratope prediction with expectation-maximization.",
  "title_text": "ParaEM: antigen-aware sequence-based paratope prediction with expectation-maximization.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/12yqdb6h36f4g",
  "venue": "Bioinformatics (Oxford, England)",
  "versions": []
}