Complete thermodynamic characterization of the multiple protonation equilibria of the aminoglycoside antibiotic paromomycin:: A calorimetric and natural abundance 15N NMR study

The binding of aminoglycoside antibiotics to a broad range of macromolecular targets is coupled to protonation of one or more of the amino groups that typify this class of drugs. Determining how and to what extent this linkage influences the energetics of the aminoglycoside-macromolecule binding reaction requires a detailed understanding of the thermodynamics associated with the protonation equilibria of the aminoglycoside amino groups. In recognition of this need, a calorimetric- and NMR-based approach for obtaining the requisite thermodynamic information is presented using paromomycin as the model aminoglycoside. Temperature- and pH-dependent 15 NNMR studies provide pK(a) values for the five paromomycin amino groups, as well as the temperature dependence of these pKa values. These studies also indicate that the observed pKa values associated with the free base form of paromomycin are lower in magnitude than the corresponding values associated with the sulfate salt form of the drug. This difference in pKa is due to drug interactions with the sulfate counterions at the high drug concentrations (>= 812 mM) used in the N-15 NMR studies. Isothermal titration calorimetry studies conducted at drug concentrations <= 45 mu M reveal that the extent of paromomycin protonation linked to the binding of the drug to its pharmacologically relevant target, the 16 S rRNA A-site, is consistent with the pKa values of the free base and not the sulfate salt form of the drug. Temperature- and pH-dependent isothermal titration calorimetry studies yield exothermic enthalpy changes ( Delta H) for protonation of the five paromomycin amino groups, as well as positive heat capacity changes ( Delta C-p) for three of the five amino groups. Regarded as a whole, the results presented here represent an important first step toward establishing a thermodynamic database that can be used to predict how aminoglycoside-macromolecule binding energetics will be influenced by conditions such as temperature, pH, and ionic strength. Such a predictive capability is a critical component of any drug design strategy.