posted on 2023-12-29, 18:33authored byNick Rigel, Da-Wei Li, Rafael Brüschweiler
Despite rapid progress
in metabolomics research, a major bottleneck
is the large number of metabolites whose chemical structures are unknown
or whose spectra have not been deposited in metabolomics databases.
Nuclear magnetic resonance (NMR) spectroscopy has a long history of
elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing
the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds
(e.g., metabolites from the Human Metabolome Database (HMDB)) and
compare them with chemical shifts of the unknown. However, accurate
prediction of NMR chemical shifts in aqueous solution is challenging
due to limitations of experimental chemical shift libraries and the
high computational cost of quantum chemical methods. To improve NMR
prediction accuracy and applicability, an empirical prediction strategy
is introduced here to provide an accurately predicted chemical shift
for organic molecules and metabolites within seconds. Unique features
of COLMARppm include (i) the training library exclusively consisting
of high quality NMR spectra measured under standard conditions in
aqueous solution, (ii) utilization of NMR motif information, and (iii)
leveraging of the improved prediction accuracy for the automated assignment
of experimental chemical shifts for candidate structures. COLMARppm
is demonstrated in terms of accuracy and speed for a set of 20 compounds
taken from the HMDB for chemical shift prediction and resonance assignment.
COLMARppm is applicable to a wide range of small molecules and can
be directly incorporated into metabolomics workflows.