Current trends on seaweeds: Looking at chemical composition, phytopharmacology, and cosmetic applications

Seaweeds have received huge interest in recent years given their promising potentialities. Their antioxidant, anti-inflammatory, antitumor, hypolipemic, and anticoagulant effects are among the most renowned and studied bioactivities so far, and these effects have been increasingly associated with their content and richness in both primary and secondary metabolites. Although primary metabolites have a pivotal importance such as their content in polysaccharides (fucoidans, agars, carragenans, ulvans, alginates, and laminarin), recent data have shown that the content in some secondary metabolites largely determines the effective bioactive potential of seaweeds. Among these secondary metabolites, phenolic compounds feature prominently. The present review provides the most remarkable insights into seaweed research, specifically addressing its chemical composition, phytopharmacology, and cosmetic applications.We would like to thank the University of Aveiro and FCT/MCT for their financial support for the QOPNA Research Unit (FCT UID/QUI/00062/2019) and the cE3c Center (UID/BIA/00329/2013) through national founds, and where applicable, co-financed by the FEDER within the PT2020 Partnership Agreement. Martins N. would like to thank the Portuguese Foundation for Science and Technology (FCT-Portugal) for the strategic project ref. UID/BIM/04293/2013 and "NORTE2020-Programa Operacional Regional do Norte" (NORTE-01-0145-FEDER-000012). Acknowledgments: We would like to thank the University of Aveiro and FCT/MCT for their financial support for the QOPNA Research Unit (FCT UID/QUI/00062/2019) and the cE3c Center (UID/BIA/00329/2013) through national founds, and where applicable, co-financed by the FEDER within the PT2020 Partnership Agreement. Martins N. would like to thank the Portuguese Foundation for Science and Technology (FCT–Portugal) for the strategic project ref. UID/BIM/04293/2013 and “NORTE2020—Programa Operacional Regional do Norte” (NORTE-01-0145-FEDER-000012)

An automated framework for NMR chemical shift calculations of small organic molecules

When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties—specifically NMR chemical shifts in this manuscript—via the open source, high-performance computational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model. The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries