Eric Adriano Zizzi

Predicting the ligand–receptor dissociation rate constant (koff) remains to this day a key challenge in drug discovery, despite its well-established relevance to residence time and in vivo drug efficacy, since it depends on the dynamic interaction between the ligand and the receptor site that cannot be immediately reconstructed from equilibrium conditions alone. In this work, we developed a transformer-based machine-learning framework able to predict the koff value relying solely on the protein amino acid sequence and ligand SMILES string, without requiring explicit 3D protein–ligand complex structures. Evaluated on a chemically and biologically diverse dataset, our model achieved competitive performance in cross-validation and on a held-out test set, while also showing robust behavior on the most represented protein families. Our results indicate that promising koff prediction performance can be obtained with a structure-independent representation, by exploiting pretrained protein and molecular language representations as a scalable strategy for kinetics-aware drug discovery.

The mechanical architecture of microtubules (MTs) is crucial for modulating their functions within cells; however, the effect of varying the number of protofilaments (PFs) on the propagation of mechanical signals remains largely unexplored. Nevertheless, MTs assembled in vitro exhibit diverse PF numbers depending on the specific tubulin composition, stabilizing agents and cellular context, suggesting a regulated architectural adaptation. Here, we performed a multiscale computational study integrating molecular dynamics, dynamical network analysis and elastic network modelling to investigate the influence of the MT architecture on structural communication and mechanics. Our results highlight that an increase in PF number alters tubulin–tubulin contact patterns, reshapes lateral surface hydrophobicity and modulates the dynamics of a specific unstructured region known as the M-loop. Remarkably, we identified a correlation between the PF number, vibrational path length and bending stiffness, revealing that MTs with larger architectures propagate mechanical information less efficiently, but offer increased structural support. These findings suggest that MT architecture may serve as a design parameter influencing the propagation of mechanical signals across scales. Moreover, they may contribute to the emerging field of neuromechanobiology, where MTs are considered potential integrators of mechanical and informational processes within neurons.

Formation of complexes between triazine-carbosilane dendrons and rose bengal is based on electrostatic interactions.

Volatile anesthetics (VAs) are medicinal chemistry compounds commonly used to enable surgical procedures for patients who undergo painful treatments and can be partially or fully sedated, remaining in an unconscious state during the operation. The specific molecular mechanism of anesthesia is still an open issue, but scientific evidence supports the hypothesis of the involvement of both putative hydrophobic cavities in membrane receptors as binding pockets and interactions between anesthetics and cytoplasmic proteins. Previous studies demonstrated the binding of VAs to tubulin. Since actin is the other major component of the cytoskeleton, this study involves an investigation of its interactions with four major anesthetics: halothane, isoflurane, sevoflurane, and desflurane. Molecular docking was implemented using the Molecular Operating Environment (MOE) software (version 2022.02) and applied to a G-actin monomer, extrapolating the relative binding affinities and root-mean-square deviation (RMSD) values. A comparison with the F-actin was also made to assess if the generally accepted idea about the enhanced F-to-G-actin transformation during anesthesia is warranted. Overall, our results confirm the solvent-like behavior of anesthetics, as evidenced by Van der Waals interactions as well as the relevant hydrogen bonds formed in the case of isoflurane and sevoflurane. Also, a comparison of the interactions of anesthetics with tubulin was made. Finally, the short- and long-term effects of anesthetics are discussed for their possible impact on the occurrence of mental disorders.

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.

Understanding the intricate interplay between structural features and signal-processing events is crucial for unravelling the mechanisms of biomolecular systems. G protein-coupled receptors (GPCRs), a pervasive protein family in humans, serve a wide spectrum of vital functions. TAS2Rs, a subfamily of GPCRs, play a primary role in recognizing bitter molecules and triggering events leading to the perception of bitterness, a crucial defence mechanism against spoiled or poisonous food. Beyond taste, TAS2Rs function is associated with many diseases as they are expressed in several extra-oral tissues. Since the precise mechanism of TAS2R activation is poorly understood, this work aims to characterize the mechanisms underlying the signal transduction on the recently experimentally solved human TAS2R46 bitter taste receptor using molecular dynamics simulations coupled with network-based analysis. The results show that the allosteric activation of the receptor is associated with more correlated dynamics of the receptor and the formation of an interaction between two helices which mainly convey the signal transferring from the extracellular to the intracellular region. By elucidating the hallmarks of the allosteric network of TAS2R46 under varying conditions (ligand-bound, ligand-free, and transition states), this study has enabled the identification of the unique functional mechanisms of this receptor, thereby establishing a foundation for a more profound characterisation of this intriguing class of receptors. Highlights: The dynamics of TAS2R46 in different states were studied through molecular dynamics and network analysis. The presence of the bitter agonist increases intra-protein correlations. TM3 and TM6 helices mediate the allosteric network in Holo TAS2R46. The rotation of Y241 ^6.48 residue is pivotal in the allosteric network for the Holo TAS2R46.

Nanocapsules that collapse in response to guanosine triphosphate (GTP) have the potential as drug carriers for efficiently curing diseases caused by cancer and RNA viruses because GTP is present at high levels in such diseased cells and tissues. However, known GTP-responsive carriers also respond to adenosine triphosphate (ATP), which is abundant in normal cells as well. Here, we report the elaborate reconstitution of microtubule into a nanocapsule that selectively responds to GTP. When the tubulin monomer from microtubule is incubated at 37 °C with a mixture of GTP (17 mol%) and nonhydrolysable GTP* (83 mol%), a tubulin nanosheet forms. Upon addition of photoreactive molecular glue to the resulting dispersion, the nanosheet is transformed into a nanocapsule. Cell death results when a doxorubicin-containing nanocapsule, after photochemically crosslinked for properly stabilizing its shell, is taken up into cancer cells that overexpress GTP.

Some clinical studies have indicated activity of ivermectin, a macrocyclic lactone, against COVID-19, but a biological mechanism initially proposed for this anti-viral effect is not applicable at physiological concentrations. This in silico investigation explores potential modes of action of ivermectin and 14 related compounds, by which the infectivity and morbidity of the SARS-CoV-2 virus may be limited. Binding affinity computations were performed for these agents on several docking sites each for models of (1) the spike glycoprotein of the virus, (2) the CD147 receptor, which has been identified as a secondary attachment point for the virus, and (3) the alpha-7 nicotinic acetylcholine receptor (α7nAChr), an indicated point of viral penetration of neuronal tissue as well as an activation site for the cholinergic anti-inflammatory pathway controlled by the vagus nerve. Binding affinities were calculated for these multiple docking sites and binding modes of each compound. Our results indicate the high affinity of ivermectin, and even higher affinities for some of the other compounds evaluated, for all three of these molecular targets. These results suggest biological mechanisms by which ivermectin may limit the infectivity and morbidity of the SARS-CoV-2 virus and stimulate an α7nAChr-mediated anti-inflammatory pathway that could limit cytokine production by immune cells.