{
  "abstract": "Motivation\n\nNucleic-acid-binding proteins (NABPs) govern transcription, replication and genome organization, yet the distribution of aggregation susceptibility within regulatory proteomes remains unresolved. Here, we systematically examine amyloidogenic potential across the nucleic-acid-binding proteomes of four phylogenetically diverse organisms-Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, and Homo sapiens-spanning prokaryotes and eukaryotes, to determine whether aggregation-prone sequence architectures are selectively constrained within transcriptional networks.Results\n\nUsing VAEBAC, a representation-learning framework that integrates large-scale amyloid-family sequences with experimentally validated annotations, we performed proteome-scale prediction of amyloidogenicity across nucleic-acid-binding proteins in each organism. Predicted amyloidogenic proteins were consistently and significantly enriched in Gene Ontology categories associated with regulation of DNA-templated transcription, RNA biosynthesis, and gene expression across all four organisms, a pattern validated using three independent annotation tools. Fisher's exact test confirmed significant enrichment of amyloidogenic NABPs among transcription regulators in eukaryotic proteomes. Residue-level mapping demonstrated spatial separation between predicted aggregation-prone regions and DNA-binding interfaces, suggesting structural compatibility between regulatory function and conditional aggregation. Experimental validation confirmed fibrillar assembly of selected proteins. These findings support a conserved model in which aggregation susceptibility is functionally stratified across transcriptional regulatory tiers rather than uniformly suppressed within NABPs, a principle conserved across 3.5 billion years of evolution.Availability and implementation\n\nVAEBAC is freely accessible at https://www.vaebac.com.",
  "abstract_shown": true,
  "affiliations": [
    "Interdisciplinary Biotechnology Unit, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, Uttar Pradesh 202002, India."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Muhammad Uzair Ashraf"
    },
    {
      "affs": [
        0
      ],
      "name": "Owais Ahmad"
    },
    {
      "affs": [
        0
      ],
      "name": "Rizwan Hasan Khan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/af2c293pm5xzd.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag745",
  "fulltext_url": "https://europepmc.org/articles/PMC13682611",
  "id": "af2c293pm5xzd",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://europepmc.org/article/MED/42842425",
  "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|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7220831414",
  "pdf_url": "https://europepmc.org/articles/PMC13682611?pdf=render",
  "pmcid": "PMC13682611",
  "pmid": "42842425",
  "published": "2026-10-01",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/af2c293pm5xzd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:10Z",
      "native_id": "MED:42842425",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42842425"
    },
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag745",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag745/8875881"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Protein Structure and Dynamics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "VAEBAC: a representation-learning framework for proteome-scale prediction of amyloidogenic proteins and functional stratification of nucleic-acid-binding proteins.",
  "title_text": "VAEBAC: a representation-learning framework for proteome-scale prediction of amyloidogenic proteins and functional stratification of nucleic-acid-binding proteins.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/af2c293pm5xzd",
  "venue": "Bioinformatics (Oxford, England)",
  "versions": []
}