Bootstrap Aggregation for Model Selection in the Model-free Formalism.

Area of Science:

• Biophysics
• Structural Biology
• Computational Chemistry

Background:

• Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for studying biological macromolecule dynamics.
• Accurate theoretical models for nuclear spin relaxation are essential for robust NMR data analysis.
• Current methods often rely on selecting model-free spectral density functions using bias-corrected fitness tests.

Purpose of the Study:

• To apply advances in statistical model selection theory, specifically bootstrap aggregation (bagging), to NMR ¹⁵N spin relaxation data.
• To develop a multimodel inference solution for the model-free selection problem in NMR data analysis.
• To enhance the accuracy of inferences about biological macromolecule dynamics.

Main Methods:

• Utilized bootstrap aggregation (bagging) for statistical model selection.
• Applied the bagging approach to ¹⁵N spin relaxation data.
• Tested the method on datasets recorded at four different static magnetic fields.

Main Results:

• Developed a multimodel inference solution for selecting appropriate model-free spectral density functions.
• Demonstrated the effectiveness of the bagging approach in analyzing NMR relaxation data.
• Successfully applied the method to the bZip domain of the S. cerevisiae transcription factor GCN4.

Conclusions:

• Bootstrap aggregation offers a robust statistical framework for model selection in NMR relaxation studies.
• This multimodel inference approach improves the reliability of dynamic inferences for biological macromolecules.
• The method provides a powerful tool for analyzing complex NMR datasets across various magnetic field strengths.