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Exploring the Chemical Space of Macro- and Micro-Algae Using Comparative Metabolomics.

ABSTRACT: With more than 156,000 described species, eukaryotic algae (both macro- and micro-algae) are a rich source of biological diversity, however their chemical diversity remains largely unexplored. Specialised metabolites with promising biological activities have been widely reported for seaweeds, and more recently extracts from microalgae have exhibited activity in anticancer, antimicrobial, and antioxidant screens. However, we are still missing critical information on the distinction of chemical profiles between macro- and microalgae, as well as the chemical space these metabolites cover. This study has used an untargeted comparative metabolomics approach to explore the chemical diversity of seven seaweeds and 36 microalgal strains. A total of 1390 liquid chromatography-mass spectrometry (LC-MS) features were detected, representing small organic algal metabolites, with no overlap between the seaweeds and microalgae. An in-depth analysis of four Dunaliella tertiolecta strains shows that environmental factors may play a larger role than phylogeny when classifying their metabolomic profiles.

SUBMITTER: Hughes AH

PROVIDER: S-EPMC7913273 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The fight against the emergence of mutant influenza strains has led to the screening of an increasing number of compounds for inhibitory activity against influenza neuraminidase. This study explores the chemical space of neuraminidase inhibitors (NAIs), which provides an opportunity to obtain further molecular insights regarding the underlying basis of their bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the literature. Molecular and quantum chemical descriptors were obtained from low-energy conformational structures geometrically optimized at the PM6 level. The bioactivities of NAIs were classified as active or inactive according to their half maximum inhibitory concentration (IC50) value in which IC50 < 1µM and ≥ 10µM were defined as active and inactive compounds, respectively. Interpretable decision rules were derived from a quantitative structure-activity relationship (QSAR) model established using a set of substructure descriptors via decision tree analysis. Univariate analysis, feature importance analysis from decision tree modeling and molecular scaffold analysis were performed on both data sets for discriminating important structural features amongst active and inactive NAIs. Good predictive performance was achieved as deduced from accuracy and Matthews correlation coefficient values in excess of 81% and 0.58, respectively, for both influenza A and B NAIs. Furthermore, molecular docking was employed to investigate the binding modes and their moiety preferences of active NAIs against both influenza A and B neuraminidases. Moreover, novel NAIs with robust binding fitness towards influenza A and B neuraminidase were generated via combinatorial library enumeration and their binding fitness was on par or better than FDA-approved drugs. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.

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Exploring the Chemical Space of Macro- and Micro-Algae Using Comparative Metabolomics.

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