NMR spectroscopy is a powerful method in structural and functional analysis of macromolecules and has become particularly prevalent in studies of protein structure, function and dynamics. Unique to NMR spectroscopy is the relatively low constraints on sample preparation and the high level of control of sample conditions. Proteins can be studied in a wide range of buffer conditions, e.g. different pHs and variable temperatures, allowing studies of proteins under conditions that are closer to their native environment compared to other structural methods such as X-ray crystallography and electron microscopy. The key disadvantage of NMR is the relatively low sensitivity of the method, requiring either concentrated samples or very lengthy data-acquisition times. Thus, proteins that are unstable or can only be studied in dilute solutions are often considered practically unfeasible for NMR studies. Here, we describe a general method, where non-uniform sampling (NUS) allows for signal averaging to be monitored in an iterative manner, enabling efficient use of spectrometer time, ultimately leading to savings in costs associated with instrument and isotope-labelled protein use. The method requires preparation of multiple aliquots of the protein sample that are flash-frozen and thawed just before acquisition of a short NMR experiments carried out while the protein is stable (12 h in the presented case). Non-uniform sampling enables sufficient resolution to be acquired for each short experiment. Identical NMR datasets are acquired and sensitivity is monitored after each co-added spectrum is reconstructed. The procedure is repeated until sufficient signal-to-noise is obtained. We discuss how maximum entropy reconstruction is used to process the data, and propose a variation on the previously described method of automated parameter selection. We conclude that combining NUS with iterative co-addition is a general approach, and particularly powerful when applied to unstable proteins.

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http://dx.doi.org/10.1007/s10858-017-0091-zDOI Listing

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