{
  "abstract": "Protein engineering has important implications in the bioeconomy, enabling applications in materials, medicine, and energy. A key challenge is designing protein sequences that have a specific form and function. Protein inverse folding seeks to address this challenge by identifying amino acid sequences compatible with a desired protein backbone. This task is central to protein redesign and can provide a sequence design capability for de novo backbones produced by structure-generation methods. Ideally, inverse folding can provide diverse sequence alternatives, fixed residues or motifs, soft biochemical preferences at selected positions, and candidates that remain experimentally useful. We developed Inverse FoldDir, a controllable inverse-folding method that performs iterative denoising on the amino acid probability simplex. Given a backbone structure, the model updates all positions jointly through a learned Dirichlet flow, supporting full sequence generation, fixed-residue inpainting, and user-defined soft residue priors. On the held-out CATH 4.2 test set, Inverse FoldDir achieved a mean TM-score of 84.5 (on a 0-100 scale) and a mean C RMSD of 1.76[A], compared with 83.3 and 1.86[A], respectively, for ESM-IF1, the strongest evaluated baseline on both metrics. Denoising trajectory analyses showed that positions commit at different rates and that some residues change identity late in generation, illustrating whole-sequence refinement rather than one-shot prediction or irreversible sequential decoding. We experimentally tested Inverse FoldDir in an anti-GFP nanobody redesign task, where two of 35 redesigned sequences retained reproducible sfGFP-binding signal across independent assay runs with approximately 43% sequence divergence from the native nanobody. Inverse FoldDir is a structure-conditioned protein redesign method that combines structural recovery, user control, experimental validation, and a natural route toward future property-guided sampling.",
  "abstract_shown": true,
  "affiliations": [
    "Stanford University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. TARTICI"
    },
    {
      "name": "M. Stojkovic"
    },
    {
      "name": "A. Tian"
    },
    {
      "name": "M. C. Jewett"
    },
    {
      "name": "R. B. Altman"
    },
    {
      "name": "B. J. Wittmann"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ff4m5zvkbnbk8.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.06.749733",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749733v1.full",
  "id": "ff4m5zvkbnbk8",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749733v1",
  "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": "W7211942594",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749733v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ff4m5zvkbnbk8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:03Z",
      "native_id": "10.64898/2026.09.06.749733",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749733v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Inverse FoldDir: Structure-conditioned Protein Sequence Design by Dirichlet Flow Matching",
  "title_text": "Inverse FoldDir: Structure-conditioned Protein Sequence Design by Dirichlet Flow Matching",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:39Z",
  "url": "https://research.draconitic.com/paper/ff4m5zvkbnbk8",
  "venue": "bioRxiv",
  "versions": []
}