<p>Chiral recognition plays a crucial role in the fields of chemistry, biomedicine, and food science. In this study, β-cyclodextrin-modified silver nanoparticles (β-CD@AgNPs) with enhanced chiral binding ability were successfully prepared using the reduction method and utilized for the selective recognition of tryptophan (Trp) enantiomers, one of the representatives of non-polar amino acids. Interestingly, the β-CD@AgNPs with <i>L</i>-Trp system exhibited a red color (with a new ultraviolet–visible (UV–vis) absorption peak at around 550&#xa0;nm), while no change occurred with <i>D</i>-Trp. Compared with&#xa0;unmodified silver nanoparticles (AgNPs), the recognition results of chiral Trp by β-CD@AgNPs were reversed. β-CD@AgNPs were also used to recognize non-polar (phenylalanine (Phe), valine (Val) and leucine (Leu)), polar (tyrosine (Tyr) and serine (Ser)), and basic (histidine (His)) and acidic (aspartic acid (Asp)) amino acids, respectively. It was found that β-CD@AgNPs demonstrated high selectivity only for Trp among non-polar amino acids. Furthermore, the recognition mechanism was studied through calculations, and it indicated that the binding energy strength between <i>L</i>-Trp and β-CD@AgNPs (-2540.76&#xa0;kcal/mol) was higher than that between <i>D</i>-Trp and β-CD@AgNPs (-2326.91&#xa0;kcal/mol). Owing to the significant electronegativity of the β-CD cavity, the indole ring of <i>L</i>-Trp exhibited susceptibility to electrostatic interactions, leading to enhanced adsorption within the β-CD cavity. This effect consequently improved the aggregation capability of the β-CD@AgNPs. Finally, β-CD@AgNPs were used for determining the enantiomeric excess (ee%) of Trp. A favorable linear relationship between the UV–vis absorption intensity and the ee% of Trp was established (<i>R</i><sup>2</sup> = 0.959). This study provided an effective method for exploring the chiral recognition mechanism of amino acid enantiomers based on nanomaterials.</p> Graphical Abstract <p></p>

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β-cyclodextrin-modified silver nanoparticles for highly selective recognition of chiral Trp and its mechanism

  • Zaiyou Tao,
  • Hongmei Liu,
  • Yanran Chen,
  • Shaoying He,
  • Baomin Yang

摘要

Chiral recognition plays a crucial role in the fields of chemistry, biomedicine, and food science. In this study, β-cyclodextrin-modified silver nanoparticles (β-CD@AgNPs) with enhanced chiral binding ability were successfully prepared using the reduction method and utilized for the selective recognition of tryptophan (Trp) enantiomers, one of the representatives of non-polar amino acids. Interestingly, the β-CD@AgNPs with L-Trp system exhibited a red color (with a new ultraviolet–visible (UV–vis) absorption peak at around 550 nm), while no change occurred with D-Trp. Compared with unmodified silver nanoparticles (AgNPs), the recognition results of chiral Trp by β-CD@AgNPs were reversed. β-CD@AgNPs were also used to recognize non-polar (phenylalanine (Phe), valine (Val) and leucine (Leu)), polar (tyrosine (Tyr) and serine (Ser)), and basic (histidine (His)) and acidic (aspartic acid (Asp)) amino acids, respectively. It was found that β-CD@AgNPs demonstrated high selectivity only for Trp among non-polar amino acids. Furthermore, the recognition mechanism was studied through calculations, and it indicated that the binding energy strength between L-Trp and β-CD@AgNPs (-2540.76 kcal/mol) was higher than that between D-Trp and β-CD@AgNPs (-2326.91 kcal/mol). Owing to the significant electronegativity of the β-CD cavity, the indole ring of L-Trp exhibited susceptibility to electrostatic interactions, leading to enhanced adsorption within the β-CD cavity. This effect consequently improved the aggregation capability of the β-CD@AgNPs. Finally, β-CD@AgNPs were used for determining the enantiomeric excess (ee%) of Trp. A favorable linear relationship between the UV–vis absorption intensity and the ee% of Trp was established (R2 = 0.959). This study provided an effective method for exploring the chiral recognition mechanism of amino acid enantiomers based on nanomaterials.

Graphical Abstract