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Ciprofloxacin and Clinafloxacin Antibodies for an Immunoassay of Quinolones: Quantitative Structure⁻Activity Analysis of Cross-Reactivities.

Andrey A BuglakIlya A ShaninSergei A EreminHong-Tao LeiXiangmei LiAnatoly V ZherdevBoris B Dzantiev
Published in: International journal of molecular sciences (2019)
A common problem in the immunodetection of structurally close compounds is understanding the regularities of immune recognition, and elucidating the basic structural elements that provide it. Correct identification of these elements would allow for select immunogens to obtain antibodies with either wide specificity to different representatives of a given chemical class (for class-specific immunoassays), or narrow specificity to a unique compound (mono-specific immunoassays). Fluoroquinolones (FQs; antibiotic contaminants of animal-derived foods) are of particular interest for such research. We studied the structural basis of immune recognition of FQs by antibodies against ciprofloxacin (CIP) and clinafloxacin (CLI) as the immunizing hapten. CIP and CLI possess the same cyclopropyl substituents at the N1 position, while their substituents at C7 and C8 are different. Anti-CIP antibodies were specific to 22 of 24 FQs, while anti-CLI antibodies were specific to 11 of 26 FQs. The molecular size was critical for the binding between the FQs and the anti-CIP antibody. The presence of the cyclopropyl ring at the N1 position was important for the recognition between fluoroquinolones and the anti-CLI antibody. The anti-CIP quantitative structure⁻activity relationship (QSAR) model was well-equipped to predict the test set (pred_R² = 0.944). The statistical parameters of the anti-CLI model were also high (R² = 0.885, q² = 0.864). Thus, the obtained QSAR models yielded sufficient correlation coefficients, internal stability, and predictive ability. This work broadens our knowledge of the molecular mechanisms of FQs' interaction with antibodies, and it will contribute to the further development of antibiotic immunoassays.
Keyphrases
  • structural basis
  • structure activity relationship
  • healthcare
  • pseudomonas aeruginosa
  • molecular docking
  • high resolution
  • molecular dynamics simulations
  • sensitive detection