Computational screening of umami tastants using deep learning.

Umami, a fundamental human taste modality, refers to the savory flavors in meats and broths, often associated with monosodium glutamate and protein richness. With limited knowledge of umami molecules, the food industry seeks efficient approaches for identifying novel tastants. In this study, we have devised a virtual screening pipeline for identifying highly potent umami tastants from large molecular databases.

Classification of tastants: A deep learning based approach.

Predicting the taste of molecules is of critical importance in the food and beverages, flavor, and pharmaceutical industries for the design and screening of new tastants. In this work, we have built deep learning models to classify sweet, bitter, and umami molecules- the three basic tastes whose sensation is mediated by G protein-coupled receptors. An extensive dataset containing 1466 bitter, 1764 sweet, and 238 umami tastants was curated from existing literature.

Phase Behavior of Pure PSPC and PEGylated Multicomponent Lipid and Their Interaction with Paclitaxel: An All-Atom MD Study.

Exploring structural behavior of pure 1-palmitoyl-2-stearoyl--glycero-3-phosphocholine (PSPC) and multicomponent PSPC and 1,2-distearoyl--glycero-3-phosphoethanolamine-[amino(poly(ethylene glycol))-2000] (DSPE-PEG) membranes and their interaction with pharmaceutically important drugs carry huge importance in drug delivery. Using all-atom molecular dynamics (MD) simulations, we investigated the phase behavior of pure and PEGylated membranes at the temperature range of 280-360 K. We observe a gel-to-liquid crystalline phase transition for pure PSPC between 320 and 330 K, and in the case of multicomponent membranes, at 320 K, a coexistence of order-disorder phases is observed, which gradually transform to a complete liquid crystalline to gel phase between 320 and 310 K.