A nonlinear regression procedure for fitting estimates of an association constant and saturation shifts to NMR titration experiments under fast-exchange conditions is described. The method assigns weights to each observation by propagating measurement errors through the fitted model. A series of Monte Carlo studies simulating a variety of possible experimental conditions has shown this method to be significantly superior to other methods commonly in use.
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Barrans et al. (1994) studied this question.
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