Molecular basis of cooperativity in pH-triggered supramolecular self-assembly

Yang Li, Tian Zhao, Chensu Wang, Zhiqiang Lin, Gang Huang, Baran D. Sumer and Jinming Gao ()
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Yang Li: Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center
Tian Zhao: Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center
Chensu Wang: Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center
Zhiqiang Lin: Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center
Gang Huang: Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center
Baran D. Sumer: University of Texas Southwestern Medical Center
Jinming Gao: Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center

Nature Communications, 2016, vol. 7, issue 1, 1-11

Abstract: Abstract Supramolecular self-assembly offers a powerful strategy to produce high-performance, stimuli-responsive nanomaterials. However, lack of molecular understanding of stimulated responses frequently hampers our ability to rationally design nanomaterials with sharp responses. Here we elucidated the molecular pathway of pH-triggered supramolecular self-assembly of a series of ultra-pH sensitive (UPS) block copolymers. Hydrophobic micellization drove divergent proton distribution in either highly protonated unimer or neutral micelle states along the majority of the titration coordinate unlike conventional small molecular or polymeric bases. This all-or-nothing two-state solution is a hallmark of positive cooperativity. Integrated modelling and experimental validation yielded a Hill coefficient of 51 in pH cooperativity for a representative UPS block copolymer, by far the largest reported in the literature. These data suggest hydrophobic micellization and resulting positive cooperativity offer a versatile strategy to convert responsive nanomaterials into binary on/off switchable systems for chemical and biological sensing, as demonstrated in an additional anion sensing model.

Date: 2016
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DOI: 10.1038/ncomms13214

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