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Enhanced Molecular Recognition between Nucleobases and Guanine-5′-monophosphate-disodium (GMP) by Surfactant Aggregates in Aqueous Solution

Liu, Zhang, Wang, Dong, Cao, Meiwen, Han, Yuchun, Xu, Hai, Wang, Yilin
ACS Applied Materials & Interfaces 2015 v.7 no.27 pp. 15078-15087
DNA, adenine, ammonium, aqueous solutions, cytosine, guanine, guanosine monophosphate, hydrogen bonding, hydrophobicity, micelles, surfactants, uracil
Only specific base pairs on DNA can bind with each other through hydrogen bonds, which is called the Watson–Crick (W/C) pairing rule. However, without the constraint of DNA chains, the nucleobases in bulk aqueous solution usually do not follow the W/C pairing rule anymore because of the strong competitive effect of water and the multi-interaction edges of nucleobases. The present work applied surfactant aggregates noncovalently functionalized by nucleotide to enhance the recognition between nucleobases without DNA chains in aqueous solution, and it revealed the effects of their self-assembling ability and morphologies on the recognition. The cationic ammonium monomeric, dimeric, and trimeric surfactants DTAB, 12–3–12, and 12–3–12–3–12 were chosen. The surfactants with guanine-5′-monophosphate-disodium (GMP) form micelles, vesicles, and fingerprint-like and plate-like aggregates bearing the hydrogen-bonding sites of GMP, respectively. The binding parameters of these aggregates with adenine (A), uracil (U), guanine (G), and cytosine(C) indicate that the surfactants can promote W/C recognitions in aqueous solution when they form vesicles (GMP/DTAB) or plate-like aggregates (GMP/12–3–12) with proper molecular packing compactness, which not only provide hydrophobic environments but also shield non-W/C recognition edges. However, the GMP/12–3–12 micelles with loose molecular packing, the GMP/12–3–12 fingerprint-like aggregates where the hydrogen bond sites of GMP are occupied by itself, and the GMP/12–3–12–3–12 vesicles with too strong self-assembling ability cannot promote W/C recognition. This work provides insight into how to design self-assemblies with the performance of enhanced molecule recognition.