In vitro and in silico prediction of antibacterial interaction between essential oils via graph embedding approach.

Essential oils contain a variety of volatile metabolites, and are expected to be utilized in wide fields such as antimicrobials, insect repellents and herbicides. However, it is difficult to foresee the effect of oil combinations because hundreds of compounds can be involved in synergistic and antagonistic interactions. In this research, it was developed and evaluated a machine learning method to classify types of (synergistic/antagonistic/no) antibacterial interaction between essential oils. Graph embedding was employed to capture structural features of the interaction network from literature data, and was found to improve in silico predicting performances to classify synergistic interactions. Furthermore, in vitro antibacterial assay against a standard strain of Staphylococcus aureus revealed that four essential oil pairs (Origanum compactum-Trachyspermum ammi, Cymbopogon citratus-Thujopsis dolabrata, Cinnamomum verum-Cymbopogon citratus and Trachyspermum ammi-Zingiber officinale) exhibited synergistic interaction as predicted. These results indicate that graph embedding approach can efficiently find synergistic interactions between antibacterial essential oils.

Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space.

Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils from Lindera triloba and Cinnamomum sieboldii against Staphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts.

Subcritical Methanol Extraction of the Stone of Japanese Apricot Prunus mume Sieb. et Zucc.

The pits of Japanese apricot, Prunus mume Sieb. et Zucc., which are composed of stones, husks, kernels, and seeds, are unused by-products of the processing industry in Japan. The processing of Japanese apricot fruits generates huge amounts of waste pits, which are disposed of in landfills or, to a lesser extent, burned to form charcoal. Mume stones mainly consist of cellulose, hemicellulose, and lignin. Herein, we attempted to solubilize the wood-like carapace (stone) encasing the pit by subcritical fluid extraction with the aim of extracting useful chemicals. The characteristics of the main phenolic constituents were elucidated by liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) analyses. The degrees of solubility for various treatments (190 °C; 3 h) were determined as follows: subcritical water (54.9%), subcritical 50% methanol (65.5%), subcritical 90% methanol (37.6%), subcritical methanol (23.6%), and subcritical isopropyl alcohol (14.4%). Syringaldehyde, sinapyl alcohol, coniferyl alcohol methyl ether, sinapyl alcohol methyl ether, 5-(hydroxymethyl)-2-furfural, and furfural were present in the subcritical 90% methanol extract. Coniferyl and sinapyl alcohols (monolignols) are source materials for the biosynthesis of lignin, and syringaldehyde occur in trace amounts in wood. Our current findings provide a solubilization method that allows the main phenolic constituents of the pits to be extracted under mild conditions. This technique for obtaining subcritical extracts shows great potential for further applications.