The Interleukin-6 (IL-6) family of cytokines regulates inflammation and plays important roles in numerous biochemical pathways. Typically, cytokine levels are measured using enzyme-linked immunosorbent assay (ELISA) or western blot. However, these techniques usually require substantial processing time, cost, machinery, and specialist training. Understanding the fundamental molecular recognition mechanism of cytokines with synthetic substrates is key to developing new biomedical technologies such as assays, sensors, and therapeutics that overcome the above limitations. Herein, we use the corona phase molecular recognition (CoPhMoRe) approach to engineer new carbon nanotube constructs and study their binding to the inflammatory cytokines: IL-6, interleukin-11 (IL-11), ciliary neurotrophic factor (CNTF), and leukemia inhibitory factor (LIF). Library screening identified two polymer-based CoPhMoRe constructs consisting of single-walled carbon nanotubes complexed with p(AA68-rand-BA16-rand-CD16) polymer (MK2) or p(SS80-rand-BS20) polymer (P14) corona phases. The resulting dissociation constants (KD) were 8.38 ng/mL and 16.7 μg/mL, respectively, compared to that of the natural IL-6 receptor at ∼0.32 ng/mL. In addition, the MK2 constructs showed a nonmonotonic response function upon binding with IL-6. Comparative binding experiments suggest that both constructs appear to recognize the axially aligned α-helical structures present in the Interleukin-6 family. The findings from this study elucidate how nanoparticle interfaces, such as those produced by CoPhMoRe, can be designed to lock onto specific protein features. We find that the α-helical structure of the IL-6 family of cytokines can enable facile molecular recognition, opening the door to new types of label-free, low-cost sensing technologies.
Bibliographical noteKAUST Repository Item: Exported on 2023-05-29
Acknowledged KAUST grant number(s): OSR-2015 Sensors 2707
Acknowledgements: The authors are grateful for financial support from Amgen Inc. This research was also partially supported by the King Abdullah University of Science & Technology (OSR-2015 Sensors 2707) and the NSF Biosensing Program, under Award Number 2124194. M.P is grateful for the support of the Samsung scholarship. X.J. acknowledges support from Mathworks Inc. through the Mathworks Engineering Fellowship. The authors also acknowledge H. Lee, A. Zeng, G. Xue, J. Schacherl, S. Gibson, and L. Vega at Amgen for their constructive comments, guidance, and support.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.