# Reducing bias in the analysis of solution-state NMR data with dynamics detectors

## TL;DR

This paper adapts the detector-based analysis approach from solid-state NMR to solution-state NMR, effectively reducing bias in characterizing molecular motions and separating different types of motions in proteins.

## Contribution

It introduces a detector-based method for solution NMR that accounts for overall tumbling, internal motions, and chemical exchange, improving motion characterization accuracy.

## Key findings

- Detectors effectively describe internal protein motions in solution NMR.
- The method separates overall tumbling from internal motions.
- Comparison shows advantages over traditional model-free analysis.

## Abstract

Nuclear magnetic resonance (NMR) is sensitive to dynamics on a wide range of correlation times. Recently, we have shown that analysis of relaxation rates via fitting to a correlation function with a small number of exponential terms could yield a biased characterization of molecular motion in solid-state NMR, due to limited sensitivity of experimental data to certain ranges of correlation times. We introduced an alternative approach based on 'detectors' in solid-state NMR, for which detector responses characterize motion for a range of correlation times, and reduce potential bias resulting from the use of simple models for the motional correlation functions. Here, we show that similar bias can occur in the analysis of solution-state NMR relaxation data. We have thus adapted the detector approach to solution-state NMR, specifically separating overall tumbling motion from internal motions and accounting for contributions of chemical exchange to transverse relaxation. We demonstrate that internal protein motions can be described with detectors when the overall motion and the internal motions are statistically independent. We illustrate the detector analysis on ubiquitin with typical relaxation data sets recorded at a single or at multiple high magnetic fields, and compare with results of model-free analysis. We also compare our methodology to LeMaster's method of dynamics analysis.

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Source: https://tomesphere.com/paper/1812.01890