Dataset Information

In Silico Modeling of Spirolides and Gymnodimines: Determination of S Configuration at Butenolide Ring Carbon C-4.

ABSTRACT: Only few naturally occurring cyclic imines have been fully structurally elucidated or synthesized to date. The configuration at the C-4 carbon plays a pivotal role in the neurotoxicity of many of these metabolites, for example, gymnodomines (GYMs) and spirolides (SPXs). However, the stereochemistry at this position is not accessible by nuclear Overhauser effect-nuclear magnetic resonance spectroscopy (NOE-NMR) due to unconstrained rotation of the single carbon bond between C-4 and C-5. Consequently, the relative configuration of GYMs and SPXs at C-4 and its role in protein binding remains elusive. Here, we determined the stereochemical configuration at carbon C-4 in the butenolide ring of spirolide- and gymnodimine-phycotoxins by comparison of measured 13C NMR shifts with values obtained in silico using force field, semiempirical and density functional theory methods. This comparison demonstrated that modeled data support S configuration at C-4 for all studied SPXs and GYMs, suggesting a biosynthetically conserved relative configuration at carbon C-4 among these toxins.

SUBMITTER: Zurhelle C

PROVIDER: S-EPMC7692061 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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In Silico Modeling of Spirolides and Gymnodimines: Determination of <i>S</i> Configuration at Butenolide Ring Carbon C-4.

Zurhelle Christian C Harder Tilmann T Tillmann Urban U Tebben Jan J

Toxins 20201029 11

Only few naturally occurring cyclic imines have been fully structurally elucidated or synthesized to date. The configuration at the C-4 carbon plays a pivotal role in the neurotoxicity of many of these metabolites, for example, gymnodomines (GYMs) and spirolides (SPXs). However, the stereochemistry at this position is not accessible by nuclear Overhauser effect-nuclear magnetic resonance spectroscopy (NOE-NMR) due to unconstrained rotation of the single carbon bond between C-4 and C-5. Consequen ...[more]

PMID: 33138275

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Project description:Constraint-based models use steady-state mass balances to define a solution space of flux configurations, which can be narrowed down by measuring as many fluxes as possible. Due to loops and redundant pathways, this process typically yields multiple alternative solutions. To address this ambiguity, flux sampling can estimate the probability distribution of each flux, or a flux configuration can be singled out by further minimizing the sum of fluxes according to the assumption that cellular metabolism favors states where enzyme-related costs are economized. However, flux sampling is susceptible to artifacts introduced by thermodynamically infeasible cycles and is it not clear if the economy of fluxes assumption (EFA) is universally valid. Here, we formulated a constraint-based approach, MaxEnt, based on the principle of maximum entropy, which in this context states that if more than one flux configuration is consistent with a set of experimentally measured fluxes, then the one with the minimum amount of unwarranted assumptions corresponds to the best estimation of the non-observed fluxes. We compared MaxEnt predictions to Escherichia coli and Saccharomyces cerevisiae publicly available flux data. We found that the mean square error (MSE) between experimental and predicted fluxes by MaxEnt and EFA-based methods are three orders of magnitude lower than the median of 1,350,000 MSE values obtained using flux sampling. However, only MaxEnt and flux sampling correctly predicted flux through E. coli's glyoxylate cycle, whereas EFA-based methods, in general, predict no flux cycles. We also tested MaxEnt predictions at increasing levels of overflow metabolism. We found that MaxEnt accuracy is not affected by overflow metabolism levels, whereas the EFA-based methods show a decreasing performance. These results suggest that MaxEnt is less sensitive than flux sampling to artifacts introduced by thermodynamically infeasible cycles and that its predictions are less susceptible to overfitting than EFA-based methods.

Dataset Information

In Silico Modeling of Spirolides and Gymnodimines: Determination of S Configuration at Butenolide Ring Carbon C-4.

Publications

In Silico Modeling of Spirolides and Gymnodimines: Determination of <i>S</i> Configuration at Butenolide Ring Carbon C-4.

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