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<p>DNA aptamer sequences were selected in silico with assistance from artificial intelligence (AI) using the new Xelari.com platform against three known immunogenic surface proteins of <i>Treponema pallidum</i> designated Tpp17, Tpp47 and Tp0751. The in silico aptamers were synthesized with 5’ Alexa Fluor 647 labels and shown to bind live <i>Treponema pallidum</i> by fluorescence microscopy and spectrofluorometry. While AI-predicted K<sub>d</sub> values differed somewhat from measured K<sub>d</sub> values, it is clear that the aptamers bound the <i>T. pallidum</i> surface which is consistent with the molecular docking models for each aptamer with each cognate target protein all of which are thought to be largely accessible for binding (i.e., not predominately embedded in the outer membrane or cell wall). All three aptamers also demonstrated good specificity in cross-reactivity binding studies.</p>

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Evaluation of Artificial Intelligence-Generated DNA Aptamers Against Treponema pallidum Surface Proteins

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • John G. Bruno,
  • Shamsudin Nasaev,
  • Dmitry Ufaev,
  • Jeffrey C. Sivils

摘要

DNA aptamer sequences were selected in silico with assistance from artificial intelligence (AI) using the new Xelari.com platform against three known immunogenic surface proteins of Treponema pallidum designated Tpp17, Tpp47 and Tp0751. The in silico aptamers were synthesized with 5’ Alexa Fluor 647 labels and shown to bind live Treponema pallidum by fluorescence microscopy and spectrofluorometry. While AI-predicted Kd values differed somewhat from measured Kd values, it is clear that the aptamers bound the T. pallidum surface which is consistent with the molecular docking models for each aptamer with each cognate target protein all of which are thought to be largely accessible for binding (i.e., not predominately embedded in the outer membrane or cell wall). All three aptamers also demonstrated good specificity in cross-reactivity binding studies.