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Enzyme kinetics, Protein folding, Mathematics Statistics, Regression, Data filter, Michaelis-Menten, HIV protease, Autocorrelation function, Runs-of-signs test
Abstract:
Experimental data from continuous enzyme assays or protein folding experiments often contain hundreds, or even thousands, of densely spaced data points. When the sampling interval is extremely short, the experimental data points might not be statistically independent. The resulting neighborhood correlation invalidates important theoretical assumptions of nonlinear regression analysis. As a consequence, certain goodness-of-fit criteria, such as the runs-of-signs test and the autocorrelation function, might indicate a systematic lack of fit even if the experiment does agree very well with the underlying theoretical model. A solution to this problem is to analyze only a subset of the residuals of fit, such that any excessive neighborhood correlation is eliminated. Substrate kinetics of the HIV protease and the unfolding kinetics of UMP/CMP kinase, a globular protein from Dictyostelium discoideum, serve as two illustrative examples. A suitable data-reduction algorithm has been incorporated into software DYNAFIT [P. Kuzmic, Anal. Biochem. 237 (1996) 260-273], freely available to all academic researchers from http://www.biokin.com.
