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<p>Despite substantial progress in single-cell screening techniques, antibody (Ab) repertoires still remain enigmatic. Here we show that Ab sequences can be linked to their functionality by using big data obtained from high-throughput sequencing. Using the expansive SARS-CoV-2 pandemic data, we develop an AI-based method to reveal the neutralization potential of Ab repertoires. We employ machine learning to process public 3D structural data of Ab-RBD complexes and create a comprehensive tool, RBD-AIM (<a href="https://rbdaim.2a2i.org/">https://rbdaim.2a2i.org/</a>), for high-throughput prediction of structural Ab epitopes based on Ab sequence. Using RBD-AIM, we analyze the local big data sources to evaluate the functional biodiversity of native B cell repertoires raised after vaccination and reconstructed in a yeast display system using single-cell microfluidics. This pipeline allows for rapid isolation of neutralizing Abs that promote the survival of transgenic hACE2+ mice in lethal models of SARS-CoV-2 infection. We believe that the AI-guided sequence-functionality link can be successfully employed for further high-throughput discovery of therapeutic Abs and functional analysis of Ab repertoires.</p>

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Mining antibody functionality via AI-guided structural landscape profiling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月15日

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  • Stanislav S. Terekhov,
  • Nikita V. Ivanisenko,
  • Nan Zhang,
  • Yuliana A. Mokrushina,
  • Dmitry E. Nolde,
  • Yakov A. Lomakin,
  • Arthur Zalevsky,
  • Leyla A. Ovchinnikova,
  • Diana M. Malabuiok,
  • Margarita N. Baranova,
  • Tatiana Shashkova,
  • Elena Aliper,
  • Mingxiu Zhang,
  • Kun Guo,
  • Sergey Duga,
  • Nikolay Akhmetyanov,
  • Stepan Mamontov,
  • Anastasia O. Smirnova,
  • Ilgar Mamedov,
  • Tatiana V. Bobik,
  • Nikita N. Kostin,
  • Aleksandr S. Chernov,
  • Igor E. Eliseev,
  • Igor Yaroshevich,
  • Vitali M. Boitsov,
  • Alexey V. Stepanov,
  • Ding Zhang,
  • Roman G. Efremov,
  • Ivan V. Smirnov,
  • Olga Kardymon,
  • Hongkai Zhang,
  • Yu Guo,
  • Richard Lerner,
  • Alexander G. Gabibov,
  • Roger D. Kornberg

摘要

Despite substantial progress in single-cell screening techniques, antibody (Ab) repertoires still remain enigmatic. Here we show that Ab sequences can be linked to their functionality by using big data obtained from high-throughput sequencing. Using the expansive SARS-CoV-2 pandemic data, we develop an AI-based method to reveal the neutralization potential of Ab repertoires. We employ machine learning to process public 3D structural data of Ab-RBD complexes and create a comprehensive tool, RBD-AIM (https://rbdaim.2a2i.org/), for high-throughput prediction of structural Ab epitopes based on Ab sequence. Using RBD-AIM, we analyze the local big data sources to evaluate the functional biodiversity of native B cell repertoires raised after vaccination and reconstructed in a yeast display system using single-cell microfluidics. This pipeline allows for rapid isolation of neutralizing Abs that promote the survival of transgenic hACE2+ mice in lethal models of SARS-CoV-2 infection. We believe that the AI-guided sequence-functionality link can be successfully employed for further high-throughput discovery of therapeutic Abs and functional analysis of Ab repertoires.