Rapid transformation of nanobodies affinity based on AlphaFold2's high-accuracy predictions and interaction analysis for enrofloxacin detection in coastal fish

Abstract

High-affinity antibodies are crucial in biosensors, disease diagnostics, therapeutic drug development, and immunological analysis, making the enhancement of antibody affinity a key research focus within the field. Computer-aided design is recognized as a time-saving and labor-efficient method for nanobodies in vitro affinity maturation. Compared to experimental mutagenesis techniques, it is advantageous due to the elimination of the need for laborious library construction and screening processes. However, these approaches are constrained by structural prediction since inaccuracy in structure could readily result in maturation failures. Herein, a novel nanobodies modification method for in vitro affinity maturation, utilizing the high accuracy prediction of AlphaFold2, was employed to rapidly transform a low affinity nanobody against enrofloxacin (ENR) into one with high affinity. The molecular docking results revealed a 1.5- to 2.5-fold increase in the number of noncovalent interactions of modified nanobodies, accompanied by a reduction in binding free energy ranging from 14.1 to 62.6%. The evaluation results from ELISA and BLI indicated that the affinity of the modified nanobodies had been enhanced by 6.2–91.6 times compared to the template nanobody. Furthermore, the modified nanobodies were employed for the detection of ENR-spiked coastal fish samples. In summary, this research proposed a nanobodies modification method from a new perspective, endowing its great application potential in biosensors, food safety, and environmental monitoring.