Simulation of 13C nuclear magnetic resonance spectra of lignin compounds using principal component analysis and artificial neural networks

Thermal fluctuation and conformational effects on NMR parameters in β-O-4 lignin dimers from QM/MM and machine-learning approaches

Advanced solid-state and liquid-state nuclear magnetic resonance (NMR) approaches have enabled high throughput information about functional groups and types of bonding in a variety of lignin fragments from degradation processes and laboratory synthesis. The use of quantum chemical (QM) methods may provide detailed insight into the relationships between NMR parameters and specific lignin conformations and their dynamics, whereas a rapid prediction of NMR properties could be achieved by combining QM with machine-learning (ML) approaches. In this study, we present the effect of conformations of β-O-4 linked lignin guaiacyl dimers on ¹³C and ¹H chemical shifts while considering the thermal fluctuations of the guaiacyl dimers in water, ethanol and acetonitrile, as well as their binary 75 wt% aqueous solutions. Molecular dynamics and QM/MM simulations were used to describe the dynamics of guaiacyl dimers. The isotropic shielding of the majority of the carbon nuclei was found to be less sensitive toward a specific conformation than that of the hydrogen nuclei. The largest ¹H downfield shifts of 4-6 ppm were established in the hydroxy groups and the rings in the presence of organic solvent components. The Gradient Boosting Regressor model has been trained on 60% of the chemical environments in the dynamics trajectories with the NMR isotropic shielding (σ_iso), computed with density-functional theory, for lignin atoms. The high efficiency of this machine-learning model in predicting the remaining 40% σ_iso(¹³C) and σ_iso(¹H) values was established.

Contributions to Lignomics: Stochastic Generation of Oligomeric Lignin Structures for Interpretation of MALDI-FT-ICR-MS Results

The lack of standards to identify oligomeric molecules is a challenge for the analysis of complex organic mixtures. High-resolution mass spectrometry-specifically, Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS)-offers new opportunities for analysis of oligomers with the assignment of formulae (C_x H_y O_z ) to detected peaks. However, matching a specific structure to a given formula remains a challenge due to the inability of FT-ICR MS to distinguish between isomers. Additional separation techniques and other analyses (e.g., NMR spectroscopy) coupled with comparison of results to those from pure compounds is one route for assignment of MS peaks. Unfortunately, this strategy may be impractical for complete analysis of complex, heterogeneous samples. In this study we use computational stochastic generation of lignin oligomers to generate a molecular library for supporting the assignment of potential candidate structures to compounds detected during FT-ICR MS analysis. This approach may also be feasible for other macromolecules beyond lignin.

Physiological and Metabolic Responses of a Novel Dunaliella salina Strain to Myo-inositol1

Dunaliella salina is well known for its ability to accumulate large amounts of β-carotene. Myo-inositol (MI) enhances the biomass production of D. salina, but the underlying mechanisms were unclear. The present study showed that the concentration of exogenous MI decreased gradually and reached a constant level at the 4th day of cultivation. MI enhanced the contents of total colored carotenoids and the activity of photosystem II. Metabolic profiles were significantly changed after the addition of exogenous MI, as revealed by multivariate statistical analysis. The metabolites could be categorized into four groups based on the relative levels in different samples. Exogenous MI increased the levels of most detected sugars, amino acids, and total saturated and unsaturated fatty acids. Based on the physiological and metabolic analyses, a hypothetical growth-promoting model that MI promotes the growth of D. salina TG by increasing the levels of key metabolites and possibly enhancing photosynthesis, was proposed. This study provides valuable information for understanding the growth-promoting mechanisms of MI in D. salina from the metabolic perspective.

A tocotrienol series with an oxidative terminal prenyl unit from Garcinia amplexicaulis

Ten tocotrienol derivatives, i.e., amplexichromanols (1-10), were isolated from stem bark of Garcinia amplexicaulis Vieill. ex Pierre collected in Caledonia. The structures of the compounds 1-5 were determined to be chromanol derivatives substituted by a polyprenyl chain oxidized in terminal position. The remaining compounds 6-10 are the corresponding dimeric derivatives. Eleven known compounds, including xanthones, tocotrienol derivatives, triterpenes and phenolic compounds, were also isolated. Their structures were mainly determined using one and two-dimensional NMR and mass spectroscopy analysis. The compounds and some amplexichromanol molecules formerly isolated from G. amplexicaulis exhibited significant antioxidant activity against lipid peroxidation and in the ORAC assay.

Chemical constituents of the stems of Celastrus rugosus

Two new sesquiterpene pyridine alkaloids rugosusines A and B (1 and 2), and thirty-one known compounds were isolated from the stems of Celastrus rugosus. The structures of new compounds were elucidated by detailed spectroscopic analysis, including HR-ESI-MS and 2D NMR spectroscopic data. All the compounds were isolated from this plant for the first time. The cytotoxicities of these compounds were tested against SKOV3 and MGC-803 cell lines by CCK-8 method.

Prediction of 13C chemical shifts in methoxyflavonol derivatives using MIA-QSPR

The (13)C chemical shifts of 19 methoxyflavonol derivatives have been modeled through using a structure-based quantitative structure-property relationship approach, which is based on the treatment of 2D images. In MIA-QSPR (multivariate image analysis applied to quantitative-structure-property relationships), descriptors correlating with dependent variables are pixels (binaries) of 2D chemical structures; variant pixels in the structures (substituents) account for the explained variance in the chemical shifts. Thus, a predictive model may be built from the regression between descriptors and experimental data. The MIA-QSPR approach coupled to partial least squares (PLS) regression built for the series of flavonols revealed that the predictive ability of MIA descriptors is comparable, or even superior for the fused rings moiety, when compared to the well-known Gauge Included Atomic Orbital (GIAO) procedure for (13)C chemical shifts calculations.

Neural networks in analytical chemistry

This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP derivatives and activity parameters (pIC50) of heparanase inhibitors. The use of a genetic algorithm-kernel partial least squares algorithm combined with an artificial neural network (GA-KPLS-ANN) is described for predicting the activities of a series of aromatic sulfonamides. The retention behavior of terpenes and volatile organic compounds and predicting the response surface of different detection systems are presented as typical applications of ANNs in chromatographic area. The use of ANNs is explored in electrophoresis with emphasizes on its application on peptide mapping. Simulation of the electropherogram of glucagons and horse cytochrome C is described as peptide models. This chapter also focuses on discussing the role of ANNs in the simulation of mass and 13C-NMR spectra for noncyclic alkenes and alkanes and lignin and xanthones, respectively.

Application of artificial neural networks for predicting the aqueous acidity of various phenols using QSAR

Artificial neural networks (ANNs) have been successfully trained to model and predict the acidity constants (pK(a)) of 128 various phenols with diverse chemical structures using a quantitative structure-activity relationship. An ANN with 6-14-1 architecture was generated using six molecular descriptors that appear in the multi-parameter linear regression (MLR) model. The polarizability term (pi (I)), most positive charge of acidic hydrogen atom (q+), molecular weight (MW), most negative charge of the phenolic oxygen atom (q-), the hydrogen-bond accepting ability (epsilon(B)) and partial-charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pK(a). It was found that a properly selected and trained neural network with 106 phenols could represent the dependence of the acidity constant on molecular descriptors fairly well. For evaluation of the predictive power of the ANN, an optimized network was used to predict the pK(a)s of 22 compounds in the prediction set, which were not used in the optimization procedure. A squared correlation coefficient (R2) and root mean square error (RMSE) of 0.8950 and 0.5621 for the prediction set by the MLR model should be compared with the values of 0.99996 and 0.0114 by the ANN model. These improvements are due to the fact that the pK(a) of phenols shows non-linear correlations with the molecular descriptors. [Figure: see text].