Machine learning approaches to predict TAS2R receptors for bitterants.

Bitter taste involves the detection of diverse chemical compounds by a family of G protein-coupled receptors, known as taste receptor type 2 (TAS2R). It is often linked to toxins and harmful compounds and in particular bitter taste receptors participate in the regulation of glucose homeostasis, modulation of immune and inflammatory responses, and may have implications for various diseases. Human TAS2Rs are characterized by their polymorphism and differ in localization and function. Different receptors can activate various signaling pathways depending on the tissue and the ligand. However, in vitro screening of possible TAS2R ligands is costly and time-consuming. For this reason, in silico methods to predict bitterant-TAS2R interactions could be powerful tools to help in the selection of ligands and targets for experimental studies and improve our knowledge of bitter receptor roles. Machine learning (ML) is a branch of artificial intelligence that applies algorithms to large datasets to learn from patterns and make predictions. In recent years, there has been a record of numerous taste classifiers in literature, especially on bitter/non-bitter or bitter/sweet classification. However, only a few of them exploit ML to predict which TAS2R receptors could be targeted by bitter molecules. Indeed, the shortage and incompleteness of data on receptor-ligand associations in literature make this task non-trivial. In this work, we provide an overview of the state of the art dealing with this specific investigation, focusing on three ML-based models, namely BitterX (2016), BitterSweet (2019) and BitterMatch (2022). This review aims to establish the foundation for future research endeavours focused on addressing the limitations and drawbacks of existing models.

VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites.

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.

Mechanical communication within the microtubule through network-based analysis of tubulin dynamics.

The identification of the mechanisms underlying the transfer of mechanical vibrations in protein complexes is crucial to understand how these super-assemblies are stabilized to perform specific functions within the cell. In this context, the study of the structural communication and the propagation of mechanical stimuli within the microtubule (MT) is important given the pivotal role of the latter in cell viability. In this study, we employed molecular modelling and the dynamical network analysis approaches to analyse the MT. The results highlight that ß -tubulin drives the transfer of mechanical information between protofilaments (PFs), which is altered at the seam due to a different interaction pattern. Moreover, while the key residues involved in the structural communication along the PF are generally conserved, a higher diversity was observed for amino acids mediating the lateral communication. Taken together, these results might explain why MTs with different PF numbers are formed in different organisms or with different ß -tubulin isotypes.

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.

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.

Informed classification of sweeteners/bitterants compounds via explainable machine learning.

Perception of taste is an emergent phenomenon arising from complex molecular interactions between chemical compounds and specific taste receptors. Among all the taste perceptions, the dichotomy of sweet and bitter tastes has been the subject of several machine learning studies for classification purposes. While previous studies have provided accurate sweeteners/bitterants classifiers, there is ample scope to enhance these models by enriching the understanding of the molecular basis of bitter-sweet tastes. Towards these goals, our study focuses on the development and testing of several machine learning strategies coupled with the novel SHapley Additive exPlanations (SHAP) for a rational sweetness/bitterness classification. This allows the identification of the chemical descriptors of interest by allowing a more informed approach toward the rational design and screening of sweeteners/bitterants. To support future research in this field, we make all datasets and machine learning models publicly available and present an easy-to-use code for bitter-sweet taste prediction.

A survey on computational taste predictors.

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s00217-022-04044-5.

Noncovalent Interactions with PAMAM and PPI Dendrimers Promote the Cellular Uptake and Photodynamic Activity of Rose Bengal: The Role of the Dendrimer Structure.

Rose bengal is an anionic dye considered as a potential photosensitizer for anticancer photodynamic therapy. The clinical utility of rose bengal is hampered by its short half-life, limited transmembrane transport, aggregation, and self-quenching; consequently, efficient drug carriers that overcome these obstacles are urgently required. In this study, we performed multilevel in vitro and in silico characterization of interactions between rose bengal and cationic poly(amidoamine) (PAMAM) and poly(propyleneimine) (PPI) dendrimers of the third and fourth generation and assessed the ability of the resultant complexes to modulate the photosensitizing properties of the drug. We focused on explaining the molecular basis of this phenomenon and proved that the generation- and structure-dependent binding of the dye by the dendrimers increases the cellular uptake and production of singlet oxygen and intracellular reactive oxygen species, leading to an increase in phototoxicity. We conclude that the application of dendrimer carriers could enable the design of efficient photodynamic therapies based on rose bengal.

The Impact of Natural Compounds on S-Shaped Aß42 Fibril: From Molecular Docking to Biophysical Characterization.

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 Aß42 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 Aß aggregates may play a key role to pave the way for design, discovery and optimization strategies toward an efficient destabilization of toxic amyloid assemblies.

In silico Investigations of the Mode of Action of Novel Colchicine Derivatives Targeting ß-Tubulin Isotypes: A Search for a Selective and Specific ß-III Tubulin Ligand.

The cardinal role of microtubules in cell mitosis makes them interesting drug targets for many pharmacological treatments, including those against cancer. Moreover, different expression patterns between cell types for several tubulin isotypes represent a great opportunity to improve the selectivity and specificity of the employed drugs and to design novel compounds with higher activity only on cells of interest. In this context, tubulin isotype ßIII represents an excellent target for anti-tumoral therapies since it is overexpressed in most cancer cells and correlated with drug resistance. Colchicine is a well-known antimitotic agent, which is able to bind the tubulin dimer and to halt the mitotic process. However, it shows high toxicity also on normal cells and it is not specific for isotype ßIII. In this context, the search for colchicine derivatives is a matter of great importance in cancer research. In this study, homology modeling techniques, molecular docking, and molecular dynamics simulations have been employed to characterize the interaction between 55 new promising colchicine derivatives and tubulin isotype ßIII. These compounds were screened and ranked based on their binding affinity and conformational stability in the colchicine binding site of tubulin ßIII. Results from this study point the attention on an amide of 4-chlorine thiocolchicine. This colchicine-derivative is characterized by a unique mode of interaction with tubulin, compared to all other compounds considered, which is primarily characterized by the involvement of the α-T5 loop, a key player in the colchicine binding site. Information provided by the present study may be particularly important in the rational design of colchicine-derivatives targeting drug resistant cancer phenotypes.

Molecular and Coarse-Grained Modeling to Characterize and Optimize Dendrimer-Based Nanocarriers for Short Interfering RNA Delivery.

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.