{
  "abstract": "Protein complex structure prediction depends on both the breadth of candidate coverage and the ability to select accurate models within a limited output budget. Most existing approaches emphasize only one stage. End-to-end models and molecular docking workflows generate candidate structures, whereas quality-assessment models primarily rerank a predefined candidate pool. A complete strategy must account for complementary sampling across generators, differences among scoring scales and the allocation of final candidate quotas. Here, EnsPlex is presented as a multi-source framework that couples structural sampling with candidate selection for protein-protein interaction complex prediction. EnsPlex combines conformation-expanded docking, AlphaFold-Multimer, AlphaFold3 and Boltz-1 to expand the sampled conformational space. Conformation-expanded docking comprises monomer conformational expansion followed by flexible HADDOCK docking. FACET is trained to predict candidate quality using DockQ and its component metrics as supervision. Across 102 antigen-antibody systems, EnsPlex achieved up to a 27.8% relative improvement in target success rate over AlphaFold3 with its built-in ranking under matched output budgets. FACET also improved within-source ranking in the internal candidate pools and in homology-filtered external data. These findings support the utility of EnsPlex for structure prediction in the evaluated antigen-antibody systems.",
  "abstract_shown": true,
  "affiliations": [
    "East China Normal University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "B. Hu"
    },
    {
      "name": "Z. Lu"
    },
    {
      "name": "K. Zaman"
    },
    {
      "name": "Z. Sun"
    },
    {
      "affs": [
        0
      ],
      "name": "X. He"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cgq4zar6hvr1z.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.26.754739",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.26.754739v1.full",
  "id": "cgq4zar6hvr1z",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.26.754739v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7215952024",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.26.754739v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cgq4zar6hvr1z.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "10.64898/2026.09.26.754739",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.26.754739v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "EnsPlex: Integrative Protein Complex Prediction through Multi-source Complementary Structural Sampling and Topology-Aware Candidate Selection",
  "title_text": "EnsPlex: Integrative Protein Complex Prediction through Multi-source Complementary Structural Sampling and Topology-Aware Candidate Selection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/cgq4zar6hvr1z",
  "venue": "bioRxiv",
  "versions": []
}