Influence of dexamethasone on the interaction between glucocorticoid receptor and SOX9: A molecular dynamics study.

The glucocorticoid receptor (GR) is a nuclear receptor that controls critical biological processes by regulating the transcription of specific genes. GR transcriptional activity is modulated by a series of ligands and coenzymes, where a ligand can act as an agonist or antagonist. GR agonists, such as the glucocorticoids dexamethasone (DEX) and prednisolone, are widely prescribed to patients with inflammatory and autoimmune diseases. DEX is also used to induce osteogenic differentiation in vitro. Recently, it has been highlighted that DEX induces changes in the osteogenic differentiation of human mesenchymal stromal cells by downregulating the transcription factor SRY-box transcription factor 9 (SOX9) and upregulating the peroxisome proliferator-activated receptor γ (PPARG). SOX9 is fundamental in the control of chondrogenesis, but also in osteogenesis by acting as a dominant-negative of RUNX2. Many processes remain to be clarified during cell fate determination, such as the interplay between the key transcription factors. The main objective pursued by this work is to shed light on the interaction between GR and SOX9 in the presence and absence of DEX at an atomic level of resolution using molecular dynamics simulations. The outcome of this research could help the understanding of possible molecular interactions between GR and SOX9 and their role in the determination of cell fate. The results highlight the key residues at the interface between GR and SOX9 involved in the complexation process and shed light on the mechanism through which DEX modulates GR-SOX9 binding and exerts its biological activity.

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.

Characterization of the Interaction of Polymeric Micelles with siRNA: A Combined Experimental and Molecular Dynamics Study.

The simulation of large molecular systems remains a daunting challenge, which justifies the exploration of novel methodologies to keep computers as an ideal companion tool for everyday laboratory work. Whole micelles, bigger than 20 nm in size, formed by the self-assembly of hundreds of copolymers containing more than 50 repeating units, have until now rarely been simulated, due to a lack of computational power. Therefore, a flexible amphiphilic triblock copolymer (mPEG45-α-PLL10-PLA25) containing a total of 80 repeating units, has been emulated and synthesized to embody compactified nanoconstructs of over 900 assembled copolymers, sized between 80 and 100 nm, for siRNA complexing purposes. In this study, the tailored triblock copolymers containing a controlled number of amino groups, were used as a support model to address the binding behavior of STAT3-siRNA, in the formation of micelleplexes. Since increasingly complex drug delivery systems require an ever more optimized physicochemical characterization, a converging description has been implemented by a combination of experimentation and computational simulations. The computational data were advantageous in allowing for the assumption of an optimal N/P ratio favoring both conformational rigidifications of STAT3-siRNA with low competitive phenomena at the binding sites of the micellar carriers. These calculations were consistent with the experimental data showing that an N/P ratio of 1.5 resulted in a sufficient amount of complexed STAT3-siRNA with an electrical potential at the slipping plane of the nanopharmaceuticals, close to the charge neutralization.

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.

TAT-RasGAP317-326 kills cells by targeting inner-leaflet-enriched phospholipids.

TAT-RasGAP317-326 is a cell-penetrating peptide-based construct with anticancer and antimicrobial activities. This peptide kills a subset of cancer cells in a manner that does not involve known programmed cell death pathways. Here we have elucidated the mode of action allowing TAT-RasGAP317-326 to kill cells. This peptide binds and disrupts artificial membranes containing lipids typically enriched in the inner leaflet of the plasma membrane, such as phosphatidylinositol-bisphosphate (PIP2) and phosphatidylserine (PS). Decreasing the amounts of PIP2 in cells renders them more resistant to TAT-RasGAP317-326, while reducing the ability of cells to repair their plasma membrane makes them more sensitive to the peptide. The W317A TAT-RasGAP317-326 point mutant, known to have impaired killing activities, has reduced abilities to bind and permeabilize PIP2- and PS-containing membranes and to translocate through biomembranes, presumably because of a higher propensity to adopt an α-helical state. This work shows that TAT-RasGAP317-326 kills cells via a form of necrosis that relies on the physical disruption of the plasma membrane once the peptide targets specific phospholipids found on the cytosolic side of the plasma membrane.

Highlighting the effect of amyloid beta assemblies on the mechanical properties and conformational stability of cell membrane.

Alzheimer disease (AD) is the most common cause of dementia, characterized by a progressive decline in cognitive function due to the abnormal aggregation and deposition of Amyloid beta (Aß) fibrils in the brain of patients. In this context, the molecular mechanisms of protein misfolding and aggregation that are known to induce significant biophysical alterations in cells, including destabilization of plasma membranes, remain partially unclear. Physical interaction between the Aß assemblies and the membrane leads to the disruption of the cell membrane in multiple ways including, surface carpeting, generation of transmembrane channels and detergent-like membrane dissolution. Understanding the impact of amyloidogenic protein in different stages of aggregation with the plasma membrane, plays a crucial role to fully elucidate the pathological mechanisms of AD. Within this framework, computer simulations represent a powerful tool able to shed lights on the interactions governing the structural influence of Aß proteins on biological membrane. In this study, molecular dynamics (MD) simulations have been performed in order to characterize how POPC bilayer conformational and mechanical properties are affected by the interaction with Aß11-42 peptide, oligomer and fibril.

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.

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.