{
  "abstract": "Accurate design of hydrogen-bonding (H-bonding) interactions is a longstanding goal in protein design, as they can facilitate specific protein-protein interactions while improving the solubility of the proteins in the unbound state. Despite this, computational design of H-bond networks remains underexplored in the deep learning era. Here, we present HBDesigner, a novel algorithm for H-bond network design. Through a combination of deep learning-based sampling and atomistic energy scoring, HBDesigner outperforms existing tools in designing connected H-bond networks onto protein scaffolds. We demonstrate the usefulness of HBDesigner by creating monomeric proteins with buried polar interactions and homodimers with extended interface H-bond networks, and by installing specificity into a family of homologous heterodimers where prior design tools fail to do so. The ability to design H-bond networks into arbitrary protein scaffolds should be broadly useful for a wide range of design applications.",
  "abstract_shown": true,
  "affiliations": [
    "University of North Carolina at Chapel Hill"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "H. Dieckhaus"
    },
    {
      "name": "B. T. Harvey"
    },
    {
      "name": "T. Mulikova"
    },
    {
      "name": "J. T. Horenstein"
    },
    {
      "name": "N. I. Nicely"
    },
    {
      "name": "N. Z. Randolph"
    },
    {
      "affs": [
        0
      ],
      "name": "B. Kuhlman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2bkchxqasnx5p.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.06.08.730848",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.06.08.730848v2.full",
  "id": "2bkchxqasnx5p",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.06.08.730848v2",
  "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",
      "bioengineering"
    ],
    [
      "openalex",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7164311098",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.06.08.730848v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2bkchxqasnx5p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:24Z",
      "native_id": "10.64898/2026.06.08.730848",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.06.08.730848v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Deep learning based design of buried hydrogen bond networks with HBDesigner",
  "title_text": "Deep learning based design of buried hydrogen bond networks with HBDesigner",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:24Z",
  "url": "https://research.draconitic.com/paper/2bkchxqasnx5p",
  "venue": "bioRxiv",
  "versions": []
}