Time-resolved NMR: extracting the topology of complex enzyme networks

Abstract

The use of nondestructive NMR spectroscopy for enzymatic studies offers unique opportunities to identify nearly all enzymatic byproducts and detect unstable short-lived products or intermediates at the molecular level; however, numerous challenges must be overcome before it can become a widely used tool. The biosynthesis of acetyl-coenzyme A (acetyl-CoA) by acetyl-CoA synthetase is used here as a case study for the development of an analytical NMR-based time-course assay platform. We describe an algorithm to deconvolve superimposed spectra into spectra for individual molecules, and further develop a model to simulate the acetyl-CoA synthetase enzyme reaction network using the data derived from time-course NMR. Simulation shows indirectly that synthesis of acetyl-CoA is mediated via an enzyme-bound intermediate (possibly acetyl-AMP) and is accompanied by a nonproductive loss from an intermediate. The ability to predict enzyme function based on partial knowledge of the enzymatic pathway topology is also discussed.

Figure 1

Real-time monitoring of ACS forward reaction by ¹H NMR. The NMR-based assays, consisting of ATP (1.2 mM), acetate (1.0 mM), CoA (2.0 mM), and ACS (12.5 μg), were carried out at 37°C in a 600 mHz spectrometer. The first spectrum was obtained ∼2 min after the addition of enzyme, and subsequently 299 spectra were collected over a 70 min period. Representative spectra in the time course are shown. Labeled abbreviations: Ade (adenosine), Rib (ribose), ACP (acetate-cysteamine-pantothenate). (A) A portion of the ¹H-observed, time-resolved spectra showing ACS conversion of CoA to AcCoA (diagnostic peaks of the protons belonging to the carbons of ACP regions at 2.2–3.2 ppm). (B) A portion of the spectra showing ACS conversion of ATP to AMP as AcCoA is synthesized (diagnostic peaks of the protons belonging to the carbons of the adenosine base of AMP or ATP at 8.4–8.7 ppm). (C) A portion of the spectra showing ACS conversion of ATP to AMP as AcCoA is synthesized (diagnostic peaks of the protons belonging to the carbons of the ribose region of ATP at 4.2–4.6 ppm). (D) A portion of the spectra showing the ribose regions (6.19–6.32 ppm) where peaks were not resolvable due to overlapping signals from different molecular species.

Figure 2

Determination of peak location. (A) A diagnostic peak (CoA-CP-H10″) from the time-series spectrum of the ACS forward reaction. The peak was used to find the best fit between the standard and the NMR time-resolved diagnostic peak. (B) A library of representative shifted diagnostic peaks (CoA-CP-H10″) from a premeasured CoA standard. For clarity, in this panel only a subset of the full library of shifted peaks is presented. The full library contains one shifted peak per grid point. (C) Estimation of peak location using NMD. A single large spike (denoted with an X) was picked out as the best fit of the location of the diagnostic peak. This optimization procedure was then repeated 100 times using random initial conditions to determine the probability distribution of alignment locations (C, inset).

Figure 3

Choosing a location to perform peak fitting. (A) Detail of one of the diagnostic windows. (B) Diagnostic peak (i.e., relatively isolated peaks that are easy to distinguish from preacquired spectral standards) windows (indicated by vertical bars) used for peak location determination (using NMD) and an initial peak fitting analysis. Blue, AcCoA; red, acetate; black, AMP; green, ATP; magenta, CoA; dotted blue line, time-averaged spectrum from experiment. (C) A larger set of windows used for MPSF. Vertical bars demarcate the spectral windows used. (D) Detail of a window in which multiple peaks overlap. Once the exact location of the spectral standards has been determined, the overlapping spectra in this window are used to perform MPSF.

Figure 4

MPSF. (A) Average spectrum from experimental data. Spectral windows used in data fitting are denoted by vertical black lines. (B) Experimental data fit with spectral standards for the initial reactants. The top traces in this panel are experimental data (gray). The bottom traces show the actual standards at the fit amplitudes (red, acetate; green, ATP; and magenta, CoA). (C) Experimental data fit with the product AMP (black). The upper part shows the experimental data (gray) and the combined fit of ATP, acetate, and CoA standards (dark green). Arrows point to the peaks that are unaccounted for when spectra of ATP, acetate, and CoA are used to fit the experimental data. The lower part shows the actual AMP standard at the fit amplitude. (D) Experimental data fit with another product AcCoA (blue). The upper part shows the experimental data (gray) and the combined fit of ATP, acetate, CoA, and AMP standards (dark green). Arrows point to the peaks that were left when spectra of ATP, acetate, AMP, and CoA were used to fit the experimental data. The lower part shows the actual AcCoA standard at the fit amplitude. (E) Experimental data (gray) fit with AcCoA, acetate, AMP, ATP, and CoA standards (dark green).

Figure 5

Improved S/N ratio of concentrations of metabolites obtained by least-squares peak fitting. (Blue line) time course generated by PIBS using NMR operation software Vnmrj (Palo Alto, CA). (Red/black lines) MPSF using selected windows (Fig. 3C). The black line denotes the mean, and red lines denote 2-σ error bars (see Experimental Procedures for the error bar determination method). Fitting peaks in multiple windows by MPSF gives an order of magnitude improvement in concentration estimation.

Figure 6

Network reconstruction of the ACS-catalyzed reaction using the MPSF-processed data set. (A) Schematic representation of models 1–4 for the ACS-catalyzed reaction. For clarity, only forward reaction steps are shown, but all corresponding backward reaction steps (with arrow directions reversed) are included in each model, except for the backward step of ATP→ADP, which is treated as irreversible. E, enzyme; Q, acetyl-AMP. (B) Time course of concentrations of AMP and ATP show how the ENS results fit the MPSF-processed experimental data for different models. Dots represents the MPSF experimental data, and solid lines represent ENS results by the ensemble mean (middle line) and uncertainty bands with two ensemble standard deviations around the mean (upper and lower lines). (C) The χ²-values of ENS MC random walk process versus the MC sweep number for the MPSF-processed data set with 6000 MC sweeps, starting from a random rate coefficient (random θ) MC initialization. One of 20 repetitions (restarts) of this MC process is shown in C.