Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.

The umami taste is one of the five basic taste modalities normally linked to the protein content in food. The implementation of fast and cost-effective tools for the prediction of the umami taste of a molecule remains extremely interesting to understand the molecular basis of this taste and to effectively rationalise the production and consumption of specific foods and ingredients. However, the only examples of umami predictors available in the literature rely on the amino acid sequence of the analysed peptides, limiting the applicability of the models. In the present study, we developed a novel ML-based algorithm, named VirtuousUmami, able to predict the umami taste of a query compound starting from its SMILES representation, thus opening up the possibility of potentially using such a model on any database through a standard and more general molecular description. Herein, we have tested our model on five databases related to foods or natural compounds. The proposed tool will pave the way toward the rationalisation of the molecular features underlying the umami taste and toward the design of specific peptide-inspired compounds with specific taste properties.

Significance: TAT-RasGAP_317–326can lyse cancer cells in a manner distinct from known programmed cell death pathways through its ability to target specific plasma membrane lipids. The killing properties of this peptide may therefore be difficult for cancer cells to alleviate through resistance-building alterations within known regulated cell death pathways. TAT-RasGAP_317–326and compounds with similar anticancer activities can potentially complement existing anticancer therapies based on the use of genotoxins or radiation that induce apoptosis.

The pursuit for effective strategies inhibiting the amyloidogenic process in neurodegenerative disorders, such as Alzheimer’s disease (AD), remains one of the main unsolved issues, and only a few drugs have demonstrated to delay the degeneration of the cognitive system. Moreover, most therapies induce severe side effects and are not effective at all stages of the illness. The need to find novel and reliable drugs appears therefore of primary importance. In this context, natural compounds have shown interesting beneficial effects on the onset and progression of neurodegenerative diseases, exhibiting a great inhibitory activity on the formation of amyloid aggregates and proving to be effective in many preclinical and clinical studies. However, their inhibitory mechanism is still unclear. In this work, ensemble docking and molecular dynamics simulations on S-shaped Aβ₄₂ fibrils have been carried out to evaluate the influence of several natural compounds on amyloid conformational behaviour. A deep understanding of the interaction mechanisms between natural compounds and Aβ aggregates may play a key role to pave the way for design, discovery and optimization strategies toward an efficient destabilization of toxic amyloid assemblies.

Dendrimer nanocarriers are unique hyper-branched polymers with biomolecule-like properties, representing a promising prospect as a nucleic acid delivery system. The design of effective dendrimer-based gene carriers requires considering several parameters, such as carrier morphology, size, molecular weight, surface chemistry, and flexibility/rigidity. In detail, the rational design of the dendrimer surface chemistry has been ascertained to play a crucial role on the efficiency of interaction with nucleic acids. Within this framework, advances in the field of organic chemistry have allowed us to design dendrimers with even small difference in the chemical structure of their surface terminals. In this study, we have selected two different cationic phosphorus dendrimers of generation 3 functionalized, respectively, with pyrrolidinium (DP) and morpholinium (DM) surface groups, which have demonstrated promising potential for short interfering RNA (siRNA) delivery. Despite DP and DM differing only for one atom in their chemical structure, in vitro and in vivo experiments have highlighted several differences between them in terms of siRNA complexation properties. In this context, we have employed coarse-grained molecular dynamics simulation techniques to shed light on the supramolecular characteristics of dendrimer–siRNA complexation, the so-called dendriplex formations. Our data provide important information on self-assembly dynamics driven by surface chemistry and competition mechanisms.