Proton Affinity and Conformational Integrity of a 24-Atom Triazine Macrocycle across Physiologically Relevant pH.
Alexander J MenkeZachary P JacobusLiam E ClatonOnofrio AnnunziataRiccardo CapelliGiovanni M PavanEric E SimanekPublished in: The Journal of organic chemistry (2024)
For 24-atom triazine macrocycles, protonation of the heterocycle leads to a rigid, folded structure presenting a network of hydrogen bonds. These molecules derive from dynamic covalent chemistry wherein triazine monomers bearing a protected hydrazine group and acetal tethered by the amino acid dimerize quantitatively in an acidic solution. Here, lysine is used, and the product is a tetracation. The primary amines of the lysine side chains do not interfere with quantitative yields of the desired bis(hydrazone) at concentrations of 5-125 mg/mL. Mathematical modeling of data derived from titration experiments of the macrocycle reveals that the p K a values of the protonated triazines are 5.6 and 6.7. Changes in chemical shifts of resonances in the 1 H NMR spectra corroborate these values and further support assignment of the protonation sites. The p K a values of the lysine side chains are consistent with expectation. Upon deprotonation, the macrocycle enjoys greater conformational freedom as evident from the broadening of resonances in the 1 H and 13 C NMR spectra indicative of dynamic motion on the NMR time scale and the appearance of additional conformations at room temperature. While well-tempered metadynamics suggests only a modest difference in accessible conformational footprints of the protonated and deprotonated macrocycles, the shift in conformation(s) supports the stabilizing role that the protons adopt in the hydrogen-bonded network.
Keyphrases
- molecular dynamics
- amino acid
- room temperature
- density functional theory
- solid state
- high resolution
- molecular dynamics simulations
- ionic liquid
- magnetic resonance
- single molecule
- solid phase extraction
- electron transfer
- electronic health record
- case report
- mass spectrometry
- high speed
- machine learning
- big data
- visible light
- capillary electrophoresis
- artificial intelligence
- data analysis