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This book provides a valuable information source for olfaction and taste which includes a comprehensive and timely overview of the current state of knowledge of use for olfaction and taste machines. This book presents original, latest research in the field, with an emphasis on the recent development of human interfacing. It covers the full range of artificial chemical senses including olfaction and taste, from basic through to advanced level. Timely project in that mobile robots, olfactory displays and odour recorders are currently under research, driven by commercial demand.

Sweetness in food delivers a delightful sensory experience, underscoring the crucial role of sweeteners in the food industry. However, the widespread use of sweeteners has sparked health concerns. This underscores the importance of developing and screening natural, health-conscious sweeteners. Our study represents a groundbreaking venture into the discovery of such sweeteners derived from egg and soy proteins. Employing virtual hydrolysis as a novel technique, our research entailed a comprehensive screening process that evaluated biological activity, solubility, and toxicity of the derived compounds. We harnessed cutting-edge machine learning methodologies, specifically the latest graph neural network models, for predicting the sweetness of molecules. Subsequent refinements were made through molecular docking screenings and molecular dynamics simulations. This meticulous research approach culminated in the identification of three promising sweet peptides: DCY(Asp-Cys-Tyr), GGR(Gly-Gly-Arg), and IGR(Ile-Gly-Arg). Their binding affinity with T1R2/T1R3 was lower than −15 kcal/mol. Using an electronic tongue, we verified the taste profiles of these peptides, with IGR emerging as the most favorable in terms of taste with a sweetness value of 19.29 and bitterness value of 1.71. This study not only reveals the potential of these natural peptides as healthier alternatives to traditional sweeteners in food applications but also demonstrates the successful synergy of computational predictions and experimental validations in the realm of flavor science.

Lactisole, an inhibitor of the human sweet taste receptor, has a 2-phenoxypropionic acid skeleton and has been shown to interact with the transmembrane domain of the T1R3 subunit (T1R3-TMD) of the receptor. Another inhibitor, 2,4-DP, which shares the same molecular skeleton as lactisole, was confirmed to be approximately 10-fold more potent in its inhibitory activity than lactisole; however the structural basis of their inhibitory mechanisms against the receptor remains to be elucidated. Crystal structures of the TMD of metabotropic glutamate receptors, which along with T1Rs are categorized as class C G-protein coupled receptors, have recently been reported and made it possible to create an accurate structural model for T1R3-TMD. In this study, the detailed structural mechanism underlying sweet taste inhibition was characterized by comparing the action of lactisole on T1R3-TMD with that of 2,4-DP. We first performed a series of experiments using cultured cells expressing the sweet taste receptor with mutations and examined the interactions with these inhibitors. Based on the results, we next performed docking simulations and then applied molecular dynamics-based energy minimization. Our analyses clearly revealed that the (S)-isomers of both lactisole and 2,4-DP, interacted with the same seven residues in T1R3-TMD and that the inhibitory potencies of those inhibitors were mainly due to stabilizing interactions mediated via their carboxyl groups in the vertical dimension of the ligand pocket of T1R3-TMD. In addition, 2,4-DP engaged in a hydrophobic interaction mediated by its o-Cl group, and this interaction may be chiefly responsible for the higher inhibitory potency of 2,4-DP.

Ritonavir is a protease inhibitor used in combination with other antiretroviral drugs to treat HIV, especially in children. It enhances the effectiveness of these drugs by inhibiting the cytochrome P450-3A4 enzyme, thereby increasing their bioavailability. Ritonavir is also being investigated for cancer treatment due to its mechanism of action. However, its intense bitterness, particularly in liquid formulations, can be intolerable for some children. This bitterness is attributed to its activation of bitter taste receptors, including TAS2R14 (also named T2R14), as demonstrated in our previous study. In this study, we utilized molecular modeling, site-directed mutagenesis, and cell-based calcium mobilization assays to characterize the key residues involved in TAS2R14 activation by ritonavir. Eight critical residues for ritonavir interacting with the receptor were discovered. The results indicate two potential binding sites for ritonavir in TAS2R14 receptor, including orthosteric and allosteric sites. These findings can be useful for developing bitter blockers targeting TAS2R14 to eliminate or reduce the bitter taste of ritonavir.

Pirfenidone, an antifibrotic agent, has been shown to be effective in the treatment of idiopathic pulmonary fibrosis (IPF). However, the exact mechanism of action and clinical efficacy require further investigation and validation. This study commenced by identifying pathogenic genes associated with IPF through the GeneCards database. Potential targets of pirfenidone were subsequently screened through PubChem and Swiss TargetPrediction, and overlapping targets were identified through Venn diagram analysis. Enrichment analysis of potential target genes was performed to identify the key biological processes and pathways involved in the action of pirfenidone. The main target genes were subsequently identified through the GSE10667 and GSE110147 datasets. The affinity of PDE1A to pirfenidone was predicted by molecular docking and MicroScale Thermophoresis (MST). Finally, the expression and antifibrotic effects of pirfenidone on PDE1A were validated through data from the GSE226249 dataset. PDE1A, identified by GeneCards and Swiss TargetPrediction, was found to be an important mediator of the antifibrotic effect of pirfenidone. The enrichment analysis revealed biological processes such as cyclic nucleotide-mediated signaling and cAMP-mediated signaling. KEGG pathway analysis further linked pirfenidone activity to pathways involved in calcium signaling, taste transduction, morphine dependence, renin secretion and purine metabolism. Molecular docking, molecular dynamics (MD) simulations and MST results revealed a strong binding affinity between pirfenidone and PDE1A. MD simulations showed the stability of the complex. It was observed that the RMSD analysis of the complex stabilized between 0.6 to 0.8 nm throughout the simulation, however RMSF showed minimal fluctuation. Data from the GSE226249 dataset confirmed that upregulation of PDE1A promotes fibrosis, whereas pirfenidone downregulates PDE1A, thereby exerting its antifibrotic effect. The inhibition of IPF progression by pirfenidone is mediated by PDE1A, providing insights into its therapeutic mechanism.

Excessive sugar intake remains a major health challenge, motivating the development of safe and effective alternatives. Thaumatin, a natural high-intensity sweet protein, elicits sweetness through activation of the sweet taste receptor (T1R2/T1R3), yet its molecular recognition mechanism remains understudied. An integrated computational strategy combining comparative modeling, protein–protein docking, and 500 ns molecular dynamics simulations (triplicates) was employed to elucidate the thaumatin–receptor binding. Structural modeling identified the closed conformation of the Venus flytrap domain (VFT) as optimal for ligand engagement. Modeling revealed a stable binding interface characterized by electrostatic complementarity and van der Waals interactions, characterized by interfacial contacts of receptors and hydrogen bonding networks. Residue-level energy decomposition highlighted key residues (W418 and E422 of T1R2; S59 of T1R3) and thaumatin residues (K67, R82, and K137) that contribute substantially to complex stabilization, consistent with experimentally reported sweetness determinants. These findings provide molecular-level insight into sweet protein recognition and establish a structural framework for rational engineering of protein-based sweeteners with enhanced potency and selectivity.

Most sensory systems are remarkable in their temporal precision, reflected in such phrases as \"a flash of light\" or \"a twig snap\". Yet taste is complicated by the transport processes of stimuli through the papilla matrix to reach taste receptors, processes that are poorly understood. We computationally modeled the surface of the human tongue as a microfiber porous medium and found that time-concentration profiles within the papilla zone rise with significant delay that well match experimental ratings of perceived taste intensity to a range of sweet and salty stimuli for both rapid pulses and longer sip-and-hold exposures. Diffusivity of these taste stimuli, determined mostly by molecular size, correlates greatly with time and slope to reach peak intensity: smaller molecular size may lead to quicker taste perception. Our study demonstrates the novelty of modeling the human tongue as a porous material to drastically simplify computational approaches and that peripheral transport processes may significantly affect the temporal profile of taste perception, at least to sweet and salty compounds.

The first commercial fleets of Robo-Taxis will be on the road soon. Today important efforts are made to anticipate future Robo-Taxi services. Fleet size is one of the key parameters considered in the planning phase of service design and configuration. Based on multi-agent approaches, the fleet size can be explored using dynamic demand response simulations. Time and cost are the most common variables considered in such simulation approaches. However, personal taste variation can affect the demand and consequently the required fleet size. In this paper, we explore the impact of user trust and willingness-to-use on the Robo-Taxi fleet size. This research is based upon simulating the transportation system of the Rouen-Normandie metropolitan area in France using MATSim, a multi-agent activity-based simulator. A local survey is made in order to explore the variation of user trust and their willingness-to-use future Robo-Taxis according to the sociodemographic attributes. Integrating survey data in the model shows the significant importance of traveler trust and willingness-to-use varying the Robo-Taxi use and the required fleet size.

We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements.

Digital environments have become important contexts in which consumers form sensory expectations and evaluate food quality prior to consumption. Drawing on the elaboration likelihood model and attribution theory, this study develops a theoretically grounded process model to explain how visual and textual cues in online presentations of natural foods shape food-related cognition. Specifically, we propose that perceived naturalness serves as an initial perceptual input that can trigger cognitive engagement through multiple mechanisms: directly, via credibility as a validation mechanism, via taste inference as an experiential simulation, and through a sequential chain in which credibility enables taste inference that subsequently sustains elaboration. A 2 (platform type: content-oriented vs. transaction-oriented) × 2 (image scene: lifestyle-oriented vs. nature-oriented) × 2 (text framing: consumption-oriented vs. production-oriented) between-subjects experiment (N = 320) was conducted. Partial least squares structural equation modeling was employed to test direct and indirect effects; multi-group analysis examined boundary conditions across experimental contexts; and necessary condition analysis identified minimum required levels of predictors for high engagement states. The results indicate that perceived naturalness has a significant direct effect on cognitive engagement, as well as indirect effects through credibility and taste inference independently and in sequence. The indirect pathway is more pronounced in content-oriented environments, particularly when nature-oriented images and consumption-oriented text are used. Taste inference emerged as the strongest necessary condition for high cognitive engagement, followed by credibility; perceived naturalness showed a weaker but significant necessity effect. These findings demonstrate how visual and textual cues jointly guide anticipatory sensory processing and cognitive engagement in digital food contexts, offering both theoretical contributions to cue-based processing research and practical implications for the design of online presentations of natural foods.