Presentation
Expanding the Scope of High-Throughput NMR: Handling Peculiar 2D Peaks in Automated Structure Verification
Dimitris Argyropoulos, NMR Business Manager; ACD/Labs
Dimitris Argyropoulos, Sergey Golotvin, Uliana Bortnik
ACD/Labs, Toronto, ON, Canada
The synergy of modern NMR instruments, automated sample handling, and data interpretation through Automated Structure Verification (ASV) has ushered in an era of unparalleled high-throughput NMR capabilities. These automated systems are typically configured with standardized acquisition parameters1 to cater to a diverse array of samples efficiently. Nevertheless, despite scientists’ best efforts to optimize these parameters, instances arise where these parameters yield spectra with peaks deviating from their anticipated positions or phase.
For instance, employing an HSQC experiment with an F1 width set to 0-160 ppm for samples containing aldehyde or SiCH3 groups may lead to peaks appearing at the periphery of the F1 window, if not entirely beyond it and aliased over.2 Additionally, modern adiabatic HSQC-DEPT pulse sequences, designed to enhance experiment sensitivity,3 may yield inaccurate results in ASV systems for samples with cyclopropyl or acetylene protons. Peaks corresponding to these groups may manifest with the opposite phase in the resultant spectra. (Figure 1)
Figure 1. (left) A fully aliased Si-CH3 group HSQC peak appearing at (-0.07,168) ppm. (right) The region of cyclopropyl peaks in an HSQC spectrum (1.38–1.55,15.5 ppm). The peaks appear with the wrong (positive) phase compared to the strong peak of a methyl at (0.83,13.5) ppm. The peak at (0.99,13.7) ppm is another methyl from an impurity.
Discrepancies between expected and observed peaks can introduce significant disruptions to high-throughput workflows, forcing chemists to manually review datasets and, in some cases, re-record spectra with parameters more suitable for that sample. However, by providing the ASV system prior knowledge of the proposed structure, and training it to recognize peaks with unexpected presentations, these disruptions can be avoided.
This poster examines such an automated solution that seamlessly addresses this challenge, demonstrating its effectiveness in high-throughput laboratories. A range of examples showcasing each case will be presented, underscoring the robustness and reliability of the proposed approach.
1. Golotvin, S.S., Vodopianov, E., Pol, R., Lefebvre, B.A., Williams, A.J., Rutkowske, R.D., Spitzer, T.D. (2007). Magn. Reson. Chem., 45, 803-813.
2. See for example Claridge, T., “High Resolution NMR Techniques in Organic Chemistry”, Elsevier, 3rd Edition, 2016.
3. Boyer, R. D., Johnson, R., Krishnamurthy, K. (2003). J. Mag. Res. 165, 253-259.