Analysis of Complex Reacting Mixtures by Time-Resolved 2D NMR

Nuclear magnetic resonance (NMR) spectroscopy is a versatile tool for chemical analysis. Besides the most straightforward application to study a stable sample containing a single compound, NMR has been also used for the analysis of mixtures. In particular, the analyzed mixtures can undergo changes caused by chemical reactions. The multidimensional NMR techniques are especially effective in a case of samples containing many components. Unfortunately, they are usually too lengthy to be applied in time-resolved experiments performed to study mentioned changes in a series of spectral “snapshots.” Recently, time-resolved nonuniform sampling (NUS) has been proposed as a straightforward solution to the problem. In this paper, we discuss the features of time-resolved NUS and give practical recommendations regarding the temporal resolution and use of the time pseudodimension to resolve the components. The theoretical considerations are exemplified by the application in challenging cases of fermenting samples of wheat flour and milk

Comparison of LDA performance with TSAR amino-acid recognition procedure.

The results were shown for α-synuclein spin sytems. (PDF)</p

Classification accuracy for LDA using H^N, N, CO, C<i>α</i>, C<i>β</i>, H<i>α</i> and H<i>β</i> chemical shifts.

We performed leave-one-out cross-validation with NMR data from the 17 proteins, downloaded from the BMRB. Amino acid distributions are shown as percentages of each type present in each protein.</p

C_<i>β</i>/H_<i>β</i> plane showing the chemical shifts of 17 unfolded proteins from the BMRB (shown in different colors) and 2D projections of higher-dimensional spectra of <i>α</i>-synuclein (shown in black).

Cβ/Hβ plane showing the chemical shifts of 17 unfolded proteins from the BMRB (shown in different colors) and 2D projections of higher-dimensional spectra of α-synuclein (shown in black).</p

LDA classification of proteins in the training set.

The results of LDA for the proteins from the training data obtained in the same way as Fig 7 (subset (iii)). (PDF)</p

Fig 1 -

The sequential assignment workflow: A) peak picking for all spectra; B) forming spin systems; C) finding sequential links (nuclei whose chemical shifts are used to find the connections between adjacent amino acid residues are marked with different colors); D) forming chains (the numbers of consecutive spin systems are given in boxes; the label “pre” denotes the residue preceding the formed chain for which some chemical shifts are known; E) amino acid recognition based on characteristic chemical shifts; F) chain mapping onto the protein sequence.</p

LDA classification of proteins in the training set.

(PDF)</p

Confusion charts for the proposed chemical shift sets.

The charts result from performing leave-one-out cross-validation of the 17 proteins from the BMRB used for training. Diagonal elements (in blue) represent correct classifications, and non-diagonal elements (in red) represent incorrect classifications. The values listed to the right of each chart are the sum, for all 17 proteins used, of residues of a given type that were classified. The charts are “row normalized”, that is to say, each row shows the distribution of how true amino acid types were classified. Weighted mean accuracy (weighted by the number of residues in the proteins) was calculated for each case: A) subset (i), 66.94%; B) subset (ii), 88.67%; and C) subset (iii), 89.51%.</p

LDA classification of proteins in the training set.

The results for all 17 proteins from the BMRB that compose the training set are shown, demonstrating the efficiency and accuracy of the LDA approach. (PDF)</p

Comparison of the chain mapping procedure for <i>α</i>-synuclein with and without LDA analysis.

LDA was performed using subset (iii), but in some residues, certain chemical shifts were missing and a reduced dataset was used. The numbers of the spin systems forming the chains are shown in square boxes. The label “pre” stands for the amino acid residue preceding the formed chain. The arrows labeled “LDA” point to the results of the LDA analysis, and the size of the one-letter codes corresponds to the probability as determined by LDA. The arrows labeled “aa-seq filtering” point at the results after amino-acid sequence filtering, reducing the number of possibilities. The arrows pointing left point to unambiguous manual identification of glycine, alanine, serine and threonine (“X” is used for all other amino acid types). All chains for which identification is consistent are shown on both sides, and the correct chain is marked in green. Panels A)-E) correspond to chain-mapping tasks of increasing difficulty (see Section Application 1: Mapping spin-system chains for details).</p