{
  "abstract": "T cells play a crucial role in reducing disease severity during SARS-CoV-2 infection and in shaping long-term immune memory. However, the precise molecular immune responses, particularly involving T-cell receptor (TCR) repertoire changes after full vaccination, and the use of TCR analysis to evaluate vaccine efficacy, remain incompletely understood. In this study, we developed the DeepAir-Cov19 model (AUC=0.94), a large language model tailored to identify SARS-CoV-2-specific TCRs, and observed significant differences in the TCR profiles between antibody-negative and antibody-positive populations before and after vaccination, indicating that the immune status of pre-vaccine recipients can directly assess the efficacy of vaccination. Notably, SARS-CoV-2-specific TCRs expanded to peak levels after the second dose and remained detectable in most subjects up to 10 months post-vaccination. Meanwhile, we also identified three specific V genes, 20 V-J combinations, and 3 epitopes associated with these responses. Finally, by leveraging vaccine-specific TCRs as novel biomarkers, we developed a vaccine efficacy model that predicts antibody levels with a mean AUC of 0.96. These findings underscore the accuracy of the large model in predicting SARS-CoV-2-specific TCRs and reveal a strong correlation between SARS-CoV-2-specific TCR responses and antibody levels. This highlights the complementary and synergistic roles of T cells and antibodies in providing vaccine-mediated protection. Our results offer valuable insights into the longitudinal dynamics of SARS-CoV-2-specific TCRs and illustrate the potential for developing potency evaluation models based on these TCR insights.",
  "abstract_shown": true,
  "affiliations": [
    "National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China Nati"
  ],
  "article_type": "Confirmatory results",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Z. Wang"
    },
    {
      "name": "Y. Zhao"
    },
    {
      "name": "X. Xiao"
    },
    {
      "name": "B. He"
    },
    {
      "name": "Y. Sun"
    },
    {
      "name": "S. Xiong"
    },
    {
      "name": "C. Qin"
    },
    {
      "name": "Z. Zhou"
    },
    {
      "name": "L. Chang"
    },
    {
      "name": "J. Bai"
    },
    {
      "name": "W. Zhao"
    },
    {
      "name": "W. Liang"
    },
    {
      "name": "J. Yao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/92zhggx8dcg2x.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.16.752071",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752071v1.full",
  "id": "92zhggx8dcg2x",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752071v1",
  "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",
      "bioinformatics"
    ],
    [
      "openalex",
      "2725|SARS-CoV-2 and COVID-19 Research"
    ]
  ],
  "openalex": "W7213668987",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752071v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/92zhggx8dcg2x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:10Z",
      "native_id": "10.64898/2026.09.16.752071",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752071v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "An Artificial Intelligence Model for Longitudinal Assessment of TCR Repertoires in SARS-CoV-2 Vaccine Recipients",
  "title_text": "An Artificial Intelligence Model for Longitudinal Assessment of TCR Repertoires in SARS-CoV-2 Vaccine Recipients",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:10Z",
  "url": "https://research.draconitic.com/paper/92zhggx8dcg2x",
  "venue": "bioRxiv",
  "versions": []
}