A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e. , in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Switchgrass (Panicum virgatum) is a bioenergy model crop valued for its energy efficiency and drought tolerance. The related monocot species rice (Oryza sativa) and maize (Zea mays) deploy species-specific, specialized metabolites as core stress defenses. By contrast, specialized chemical defenses in switchgrass are largely unknown.To investigate specialized metabolic drought responses in switchgrass, we integrated tissuespecific transcriptome and metabolite analyses of the genotypes Alamo and Cave-in-Rock that feature different drought tolerance.The more drought-susceptible Cave-in-Rock featured an earlier onset of transcriptomic changes and significantly more differentially expressed genes in response to drought compared to Alamo. Specialized pathways showed moderate differential expression compared to pronounced transcriptomic alterations in carbohydrate and amino acid metabolism. However, diterpenoid-biosynthetic genes showed drought-inducible expression in Alamo roots, contrasting largely unaltered triterpenoid and phenylpropanoid pathways. Metabolomic analyses identified common and genotype-specific flavonoids and terpenoids. Consistent with transcriptomic alterations, several root diterpenoids showed significant drought-induced accumulation, whereas triterpenoid abundance remained predominantly unchanged. Structural analysis verified select drought-responsive diterpenoids as oxygenated furanoditerpenoids.Drought-dependent transcriptome and metabolite profiles provide the foundation to understand the molecular mechanisms underlying switchgrass drought responses. Accumulation of specialized root diterpenoids and corresponding pathway transcripts supports a role in drought stress tolerance.

The effects of including (a) implicit solvent in geometry optimizations, (b) conformationally flexible molecules in test sets, and (c) empirical dispersion D3(BJ) on scaling factors for predicting 1 H and 13 C NMR chemical shifts were explored. Scaling factors with optimizations performed in the gas phase and with a Polarizable Continuum Model (PCM) solvent model were obtained for 12 organic solvents, including 2,2,2-trifluroethanol and chlorobenzene, for which scaling factors have been developed for the first time. Scaling factors for aromatic solvents were split into primary and secondary scaling factors to account for CH-π effects. Including empirical dispersion D3(BJ) did not lead to significant improvement. K E Y W O R D S 1 H, 13 C, computational NMR, density functional theory, NMR

A barley diterpene synthase (HvKSL4) was found to produce (14S)-cleistantha-8,12-diene (1). Formation of the nearly planar cyclohexa-1,4-diene configuration leaves the ring poised for aromatization, but necessitates a deceptively complicated series of rearrangements steered through a complex energetic landscape, as elucidated here through quantum chemical calculations and labeling studies.

Panowamycins are a group of isochromanbased natural products first isolated from Streptomyces sp. K07-0010 in 2012 by Satoshi Ōmura and co-workers that exhibit modest anti-trypanosomal activity. Herein we demonstrate the first syntheses of these natural products and their epimers. Stereoselective dirhodiumcatalyzed CÀ H insertion reactions with a donor/donor carbene construct the substituted isochroman core in the key bond-forming step. The syntheses are completed without the use of protecting groups and feature a latestage Wacker oxidation. Incongruent NMR spectra between natural and synthetic samples revealed the structural misassignment of panowamycin A and veramycin F. Computational NMR studies suggested panowamycin A to be an alternate diastereomer, which was confirmed by synthesizing this isomer. Concurrent with this work, in 2021 Mahmud and co-workers came to the same conclusion with an updated NMR analysis of panowamycin A. In a divergent, asymmetric sequence, we report the synthesis of panowamycin A, panowamycin B, TM-135, and veramycin F.