Abstract
Abstract. The ability to make robust inferences about the dynamics of biological
macromolecules using NMR spectroscopy depends heavily on the application
of appropriate theoretical models for nuclear spin relaxation. Data
analysis for NMR laboratory-frame relaxation experiments typically
involves selecting one of several model-free spectral density functions
using a bias-corrected fitness test. Here, advances in statistical model selection theory, termed bootstrap aggregation or bagging, are applied
to 15N spin relaxation data, developing a multimodel inference solution to the model-free selection problem. The approach is illustrated using data sets recorded at four static magnetic fields for the bZip domain of the S. cerevisiae transcription factor GCN4.
Funder
National Institutes of Health
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