MC-Fit: using Monte-Carlo methods to get accurate confidence limits on enzyme parameters.
A program is described for estimating enzymatic parameters from experimental data using Apple Macintosh computers. MC-Fit uses iterative least-square fitting and Monte-Carlo sampling to get accurate estimates of the confidence limits. This approach is more robust than the conventional covariance matrix estimation, especially in cases where experimental data is partially lacking or when the standard error on individual measurements is large. This happens quite often when analysing the properties of variant enzymes obtained by mutagenesis, as these can have severely impaired activities and reduced affinities for their substrates.
Dardel
F
F
Laboratoire de Biochimie, Unité de Recherche Associée n240 du CNRS, Palaiseau, France.
D016001:Confidence Intervals; D004798:Enzymes / Q000235:genetics / Q000378:metabolism*; D004926:Escherichia coli / Q000201:enzymology / Q000235:genetics; D007700:Kinetics; D016018:Least-Squares Analysis; D008432:Mathematical Computing; D008718:Methionine-tRNA Ligase / Q000378:metabolism; D008838:Microcomputers; D009010:Monte Carlo Method; D016296:Mutagenesis / Q000502:physiology; D012984:Software; D014584:User-Computer Interface
D004798:Enzymes; D008718:Methionine-tRNA Ligase