Recent studies suggest that the fast timescale motion of methyl-bearing side chains may play an important role in mediating protein activity. These motions have been shown to encapsulate the residual conformational entropy of the folded state that can potentially contribute to the energetics of protein function. Here, we provide an overview of how to characterize these motions using nuclear magnetic resonance (NMR) spin relaxation methods. The strengths and limitations of several techniques are highlighted in order to assist with experimental design. Particular emphasis is placed on the practical aspects of sample preparation, data collection, data fitting, and statistical analysis. Additionally, discussion of the recently refined “entropy meter” is presented and its use in converting NMR observables to conformational entropy is illustrated. Taken together, these methods should yield new insights into the complex interplay between structure and dynamics in protein function.

Assignment of resonances of nuclear magnetic resonance (NMR) spectra to specific atoms within a protein remains a labor-intensive and challenging task. Automation of the assignment process often remains a bottleneck in the exploitation of solution NMR spectroscopy for the study of protein structure-dynamics-function relationships. We present an approach to the assignment of backbone triple resonance spectra of proteins. A Bayesian statistical analysis of predicted and observed chemical shifts is used in conjunction with inter-spin connectivities provided by triple resonance spectroscopy to calculate a pseudo-energy potential that drives a simulated annealing search for the most optimal set of resonance assignments. Termed Bayesian Assisted Assignments by Simulated Annealing (BARASA), a C++ program implementation is tested against systems ranging in size to over 450 amino acids including examples of intrinsically disordered proteins. BARASA is fast, robust, accommodates incomplete and incorrect information, and outperforms current algorithms – especially in cases of sparse data and is sufficiently fast to allow for real-time evaluation during data acquisition.

The comprehensive assignment of individual resonances of the nuclear magnetic resonance spectrum of a protein to specific atoms remains a labor-intensive and often debilitating task -especially for proteins larger than 30 kDa. Recently, there have been tremendous advances in our empirical knowledge of the relationship between the structural context of a nuclear spin and its observed resonance frequency. Indeed, the expansion in the database of determined high-resolution protein structures and recent advances in structure prediction provide an enormous resource in this respect. Robust automation of the resonance assignment process nevertheless often remains a bottleneck in the exploitation of solution NMR spectroscopy for the study of protein structure-dynamics-function relationships. Here we present a new approach for the assignment of backbone triple resonance spectra of proteins. A Bayesian statistical analysis of predicted and observed chemical shifts is used to provide a pseudo-energy potential to drive the search for the most optimal set of resonance assignments. This approach has been implemented in the C++ program Bayllagio and tested against protein systems ranging in size to over 450 amino acids. Bayllagio makes almost no errors, accommodates incomplete information, is sufficiently fast to allow for real-time evaluation of data acquisition, and greatly outperforms currently employed deterministic algorithms.