NUS for ASAP-HSQC experiments

Non Uniform Sampling (NUS) speeds the collection of multidimensional NMR spectra by measuring only a fraction of the data and predicting what was omitted. The quality of the reconstructed data depends on many factors and what works well for one experiment many not work well for others. The last two posts examined how NUS affects COSY and HMBC experiments. This post examines if NUS impacts the ASAP-HSQC experiment more than the traditional HSQC.

NUS for COSY experiments

Non Uniform Sampling (NUS) reduces the time taken to acquire multi-dimensional NMR spectra by predicting a fraction of the normal data instead of measuring it. The most commonly used algorithm for reconstructing the missing data requires the collected data to be properly phased in the indirect dimension. For this reason I have not recommended using NUS with HMBC and gCOSY experiments. However, last month's post showed that unphaseable HMBC experiments cope with NUS just as well as the phaseable LR-HSQMBC. In this post I compare the unphaseable gCOSY experiment with the phaseable CLIP-COSY to see how they are impacted by NUS

NUS for HMBC and LR-HSQMBC experiments

Non Uniform Sampling (NUS) of multidimensional NMR data can greatly reduce the time taken to record a spectrum by recording only a subset of the normal data. A variety of algorithms are available to reconstruct the omitted data based on the data that was recorded. The most commonly used algorithm is Iterative Soft Thresholding (IST). Most implementations of the IST algorithm rely on the peaks in the detected dimension being phased correctly and positive. For most modern experiments this is not a problem, but in the HMBC experiment it is not possible to phase the peaks. For this reason I have not recommended using NUS with HMBCs. The LR-HSQMBC experiment, however, can be phased and I recommend using NUS with it. In this post, spectra recorded with different levels of NUS were recorded to determine how NUS affects HMBC and LR-HSQMBC experiments.

Comparing NUS schedules on different compounds

Non uniform sampling (NUS) speeds NMR experiments by sampling a fraction of the data traditionally collected and predicting the rest. The data to be collected is defined by a sampling schedule and many different sampling schedules can be defined for a given level of sampling. A previous post showed that different schedules produce different results. If the best performing schedule for one compound also performs well for other compounds then that schedule could be defined as the default to give the best possible performance. To determine if this is true I compared NUS spectra obtained using the same sampling schedules on three different compounds, strychnine, ethyl benzene and cholesteryl acetate.

Comparing NUS reconstruction software

Non uniform sampling (NUS) reduces the time taken to record multidimensional NMR spectra by collecting a fraction of the usual data points and predicting the rest. There are many algorithms to predict the skipped data points and even multiple implementations of the same algorithm. Here I compare two implementations of the Iterative Soft Threshold (IST) algorithm, one provided by nmrPipe and the other by SMILE.

Comparing NUS sampling schedules

Non uniform sampling (NUS) allows multi-dimensional NMR data to be collected in a quarter or less of the traditional time. This is done by collecting only a fraction of the usual data and predicting the missing points. The data that is collected is defined by a sampling schedule and many different ways of creating these sampling schedules have been published. Several papers have reported that not all sampling schedules, even those created in the same way, produce the same quality spectra. Here, the spectra produced by different sampling schedules are compared and methods for assessing the quality of NUS spectra trialed.

Non uniform sampling

Non uniform sampling (NUS) is a method for collecting a subset of the indirect points typically acquired, thereby reducing experiment time. As little as 10% of an indirect dimension can be collected without reducing spectral quality. NUS offers significant time savings, particularly for higher dimensionality experiments.