bioRxivBiophysics
Seedless: On-the-fly pulse calculation for NMR experiments
A. J. Baldwin, J. Jones, C. Buchanan, G. Karunanithy, V. Casablancas-Antras, A. Poh, B. G. Davis
Abstract
All NMR experiments require sequences of RF pulses to manipulate nuclear spins. Signal is lost due to non-uniform excitation of nuclear spins resonating at different energies (chemical shifts) and inhomogeneity in the RF actually generated by hardware over the sample volume. Here, we present Seedless, a tool to calculate NMR pulses that compensate for these effects to enhance control of magnetisation and boost signal. The calculations take only a few seconds using an optimised GRadient Ascent Pulse Engineering (GRAPE) implementation, allowing pulses to be generated on-the-fly, optimised for individual samples and spectrometers. Each calculated pulse requires bands of chemical shift to be identified, over which one of 4 transforms will be performed, selected from a set that covers all commonly used applications: a universal rotation (e.g. 90{degrees} about the x axis), including as a special case an identity operation (return spins in the same state as they started), state-to-state (e.g. Z->Y), an XYcite (Z->XY plane), or a novel type, a suppression, that leaves spins minimally perturbed at all times during the pulse. Using imaging experiments we demonstrate our pulses effectively increase the size of the coil volume and effectively increase signal-to-noise in all experiments. We illustrate the approach by devising ultra-broadband pulses (300 ppm excitation pulse for 19F 1D spectra), a 15N HSQC with 58% increased S/N (950 MHz spectrometer + cryoprobe), triple resonance biomolecular NMR experiments such as HNCACO with 55% increased S/N (600 MHz spectrometer + RT probe), and a highly efficient pulse sequence for water suppression. The 8 optimised pulse sequences presented required 54 bespoke pulses all calculated on-the-fly. Seedless provides a means to enhance sensitivity in all pulse sequences in a manner that can be tailored to all samples/hardware being used.
- research focus: benchmarking
- research focus: software / tool
- approach: deep learning
- approach: statistical model
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