{
  "abstract": "Protein-DNA recognition specificity underlies transcriptional regulation, DNA replication, chromatin remodeling and genome editing. Protein-DNA recognition depends on both interacting sequences, yet language models that learn this cross-molecular dependence explicitly during pretraining remain limited. Here, we developed the Protein-DNA Language Models (PDLMs), interaction-aware language models that jointly represent protein and DNA sequences. PDLMs were built by continued pretraining from ESM2 with masked language modeling on paired protein-DNA sequences. A shared encoder places amino acids and nucleotides in the same molecular context, enabling conditional sequence scoring and transferable pair representations. We further introduced a domain-aware masking strategy that increased masking probability within DNA-binding domains to strengthen learning of interaction-relevant regions. Across downstream tasks, PDLMs improved protein-DNA pair classification relative to independent-encoder baselines and supported prediction of mutation-associated binding changes. Zero-shot scoring recovered known changes in DNA-recognition specificity and prioritized variants with higher editing activity. Learned representations also supported cross-study prediction of base editor activity. After fine-tuning on transcription factor binding data, PDLMs predicted DNA motifs directly from protein sequences. Together, PDLMs provide a framework for interaction-aware biomolecular language modeling, with potential applications in protein engineering and genome editing.",
  "abstract_shown": true,
  "affiliations": [
    "Peking University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Y. Xiong"
    },
    {
      "name": "J. Ren"
    },
    {
      "affs": [
        0
      ],
      "name": "X. Wei"
    },
    {
      "name": "X. Li"
    },
    {
      "name": "J. Li"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Wei"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0naffyn2d31f0.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.05.763984",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.763984v1.full",
  "id": "0naffyn2d31f0",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.763984v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.763984v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0naffyn2d31f0.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T00:21:26Z",
      "native_id": "10.64898/2026.11.05.763984",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.05.763984v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Joint protein-DNA language modeling enables prediction and engineering of DNA recognition specificity",
  "title_text": "Joint protein-DNA language modeling enables prediction and engineering of DNA recognition specificity",
  "type": "preprint",
  "updated_at": "2026-11-07T00:21:26Z",
  "url": "https://research.draconitic.com/paper/0naffyn2d31f0",
  "venue": "bioRxiv",
  "versions": []
}