{
  "abstract": "Conventional linear eplet-matching algorithms (HLAMatchmaker, HLAMM) evaluate primary sequence polymorphisms across human leukocyte antigen (HLA) loci without integrating three-dimensional (3D) steric accessibility and local physicochemical constraints. Here, we developed an exploratory computational antigenicity pipeline using AlphaFold 3 to evaluate 10,000 simulated European donor–recipient pairs across pre-computed, full-length HLA class I (HLA-A, -B, -C) and heterodimeric class II (HLA-DR, -DQ, -DP) complexes modeled in a standardized peptide-free conformation. Candidate eplets were sequentially screened using a spatial solvent-accessible surface area threshold (SASA ≥ 10.0 Å2) and an empirical biophysical exclusion gate (5th percentile of absolute net charge |Qnet| ≥ 0.3326 e and hydropathy Htotal ≥ −7.265, calibrated on N = 170 antibody-verified eplets). Across the simulated cohort (n = 10,000), filtration reduced median [Q1; Q3] eplet counts from 19 [12; 26] to 17 [10; 23] for HLA-A, 22 [15; 29] to 18 [12; 24] for HLA-B, 12 [7; 17] to 10 [7; 15] for HLA-C, 15 [10; 19] to 11 [7; 14] for HLA-DR, 12 [6; 17] to 9 [4; 14] for HLA-DQ, and 7 [3; 12] to 6 [2; 9] for HLA-DP. Component ablation demonstrated that 3D steric accessibility (SASA ≥ 10.0 Å2) accounted for the vast majority of eplet exclusions—87.8% in HLA-DR and HLA-DP and 58.2% to 69.1% across HLA-DQ and class I loci—whereas the registry-calibrated biophysical gate acted as a secondary refinement. Within the subset of simulated pairs initially exceeding the arbitrary ≥10-eplet linear cutoff, the proportion dropping below 10 eplets after filtering was low for class I (HLA-A: 4.4%, HLA-B: 8.0%, HLA-C: 13.2%) but substantially higher for class II high-load subsets: HLA-DQ (21.8%), HLA-DR (26.0%), and HLA-DP (33.7% of 3693 baseline high-load pairs). Furthermore, class II loci, particularly HLA-DP, exhibited weakened covariance between heuristic charge and hydropathy indices that persisted after 1:1 count-matched subsampling (ρmatched = −0.268) and partial correlation adjustment (ρpartial = −0.385). This dual-filter pipeline defines a restricted mathematical subset of spatially accessible and biophysically suprathreshold candidate eplets. Because this is a hypothesis-generating in silico framework, multicenter retrospective registry analyses and prospective clinical trials evaluating de novo donor-specific antibody formation, biopsy-proven rejection, and allograft survival are strictly required before clinical translation.",
  "abstract_shown": true,
  "affiliations": [
    "St. Panteleimon Hospital, First Lviv Medical Union, 79059 Lviv, Ukraine",
    "Department of Clinical Immunology and Allergology, Danylo Halytsky Lviv National Medical University, 79010 Lviv, Ukraine",
    "Department of Therapy No. 1, Medical Diagnostics, Hematology, and Transfusiology, Faculty of Postgraduate Education, Danylo Halytsky Lviv National Medical University, 79010 Lviv, Ukraine"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Vladyslav Bardash",
      "orcid": "0009-0005-8193-4941"
    },
    {
      "affs": [
        0
      ],
      "name": "Oleh Samchuk"
    },
    {
      "affs": [
        1
      ],
      "name": "Yaroslav Tolstyak",
      "orcid": "0000-0002-5990-5977"
    },
    {
      "affs": [
        2
      ],
      "name": "Hanna Demydenko"
    },
    {
      "affs": [
        2
      ],
      "name": "Eugen Sklyarov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8km1t126a6t6t.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27219442",
  "fulltext_url": null,
  "id": "8km1t126a6t6t",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://www.mdpi.com/1422-0067/27/21/9442",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|vaccines and immunoinformatics approaches"
    ]
  ],
  "openalex": "W7223417361",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/8km1t126a6t6t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:57Z",
      "native_id": "10.3390/ijms27219442",
      "source": "crossref",
      "url": "https://www.mdpi.com/1422-0067/27/21/9442"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|vaccines and immunoinformatics approaches",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Structural and Biophysical Profiling of HLA Class II Mismatches Using AlphaFold 3: A Computational Study",
  "title_text": "Structural and Biophysical Profiling of HLA Class II Mismatches Using AlphaFold 3: A Computational Study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:57Z",
  "url": "https://research.draconitic.com/paper/8km1t126a6t6t",
  "venue": "International Journal of Molecular Sciences",
  "versions": []
}