Based on atomistic molecular dynamics simulations and machine learning approaches we unveil the binding mechanism of a cyclic-RGD-ligand activated NP (PEGylated-TiO 2 ) to its receptor protein (integrin-α V β 3 ) overexpressed in many tumor cells.
G protein-coupled receptors (GPCRs) are prominent drug targets responsible for extracellular-to-intracellular signal transduction. GPCRs can form functional dimers that have been poorly characterized so far. Here, we show the dimerization mechanism of the chemokine receptors CCR5 and CXCR4 by means of an advanced free-energy technique named coarse-grained metadynamics. Our results reproduce binding events between the GPCRs occurring in the minute timescale, revealing a symmetric and an asymmetric dimeric structure for each of the three investigated systems, CCR5/CCR5, CXCR4/CXCR4, and CCR5/CXCR4. The transmembrane helices TM4-TM5 and TM6-TM7 are the preferred binding interfaces for CCR5 and CXCR4, respectively. The identified dimeric states differ in the access to the binding sites of the ligand and G protein, indicating that dimerization may represent a fine allosteric mechanism to regulate receptor activity. Our study offers structural basis for the design of ligands able to modulate the formation of CCR5 and CXCR4 dimers and in turn their activity, with therapeutic potential against HIV, cancer, and immune-inflammatory diseases.
Abstract The central role of eukaryotic translation initiation factor 4E (eIF4E) in controlling mRNA translation has been clearly assessed in the last decades. eIF4E function is essential for numerous physiological processes, such as protein synthesis, cellular growth and differentiation; dysregulation of its activity has been linked to ageing, cancer onset and progression and neurodevelopmental disorders, such as autism spectrum disorder (ASD) and Fragile X Syndrome (FXS). The interaction between eIF4E and the eukaryotic initiation factor 4G (eIF4G) is crucial for the assembly of the translational machinery, the initial step of mRNA translation. A well-characterized group of proteins, named 4E-binding proteins (4E-BPs), inhibits the eIF4E–eIF4G interaction by competing for the same binding site on the eIF4E surface. 4E-BPs and eIF4G share a single canonical motif for the interaction with a conserved hydrophobic patch of eIF4E. However, a second non-canonical and not conserved binding motif was recently detected for eIF4G and several 4E-BPs. Here, we review the structural features of the interaction between eIF4E and its molecular partners eIF4G and 4E-BPs, focusing on the implications of the recent structural and biochemical evidence for the development of new therapeutic strategies. The design of novel eIF4E-targeting molecules that inhibit translation might provide new avenues for the treatment of several conditions.
Abstract Fast, reliable and point-of-care systems to detect the SARS-CoV-2 infection are crucial to contain viral spreading and to adopt timely clinical treatments. Many of the rapid detection tests currently in use are based on antibodies that bind viral proteins 1 . However, newly appearing virus variants accumulate mutations in their RNA sequence and produce proteins, such as Spike, that may show reduced binding affinity to these diagnostic antibodies, resulting in less reliable tests and in the need for continuous update of the sensing systems 2 . Here we propose a graphene field-effect transistor (gFET) biosensor which exploits the key interaction between the Spike protein and the human ACE2 receptor. This interaction is one of the determinants of host infections and indeed recently evolved Spike variants were shown to increase affinity for ACE2 receptor 3 . Through extensive computational analyses we show that a chimeric ACE2-Fc construct mimics the ACE2 dimer, normally present on host cells membranes, better than its soluble truncated form. We demonstrate that ACE2-Fc functionalized gFET is effective for in vitro detection of Spike and outperforms the same chip functionalized with either a diagnostic antibody or the soluble ACE2. Our sensor is implemented in a portable, wireless, point-of-care device and successfully detected both alpha and gamma virus variants in patient’s clinical samples. As incomplete immunization, due to vaccine roll-out, may offer new selective grounds for antibody-escaping virus variants 4 , our biosensor opens to a class of highly sensitive, rapid and variant-robust SARS-CoV-2 detection systems.
The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that mediates the biological and toxicological effects of structurally diverse chemicals, including halogenated aromatic hydrocarbons. In this work, we investigate the effects of the binding of the AhR prototypical ligand, TCDD, on the stability of the AhR:ARNT complex, as well as the mechanisms by which ligand-induced perturbations propagate to the DNA recognition site responsible for gene transcription. To this aim, a reliable structural model of the overall quaternary structure of the AhR:ARNT:DRE complex is proposed, based on homology modelling. The model shows very good agreement with a previous one and is supported by experimental evidence. Moreover, molecular dynamics simulations are performed to compare the dynamic behaviour of the AhR:ARNT heterodimer in the presence or absence of the TCDD. Analysis of the simulations, performed by an unsupervised machine learning method, shows that TCDD binding to the AhR PASB domain influences the stability of several inter-domain interactions, in particular at the PASA-PASB interface. The inter-domain communication network suggests a mechanism by which TCDD binding allosterically stabilizes the interactions at the DNA recognition site. These findings may have implications for the comprehension of the different toxic outcomes of AhR ligands and drug design.
NURA (NUclear Receptor Activity) dataset collects curated information on small molecules that modulate nuclear receptors (NRs), to be intended for both pharmacological and toxicological applications. NURA contains bioactivity annotations for 15,206 molecules and 11 selected NRs, and it was obtained by integrating and curating data from toxicological and pharmacological databases (i.e., Tox21, ChEMBL, NR-DBIND and BindingDB). NURA dataset is a useful tool to bridge the gap between toxicology- and medicinal-chemistry-related databases, as it is enriched in terms of number of molecules, structural diversity and covered atomic scaffolds compared to the single sources. To the best of our knowledge, NURA dataset is the most exhaustive collection of small molecules annotated for their modulation of the chosen nuclear receptors. NURA dataset is intended to support decision-making in pharmacology and toxicology, as well as to contribute to data-driven applications, such as machine learning. The data curation pipeline can be downloaded free of charge at the following URL: https://michem.unimib.it/download/data/nura/.
Two data files are provided: "Nura_v1.0.0.csv", dataset containing activity labels for each molecule (rows, identified by a unique ID and the canonical SMILES string) and each nuclear receptor endpoint (columns). "Nura_v1.0.0_details", containing information on the individual records used to generate the dataset. Additional details on the content and curation pipeline can be found in the uploaded, non peer-reviewed, preprint ("NURApreprint.pdf").
Nanoparticle functionalization is a modern strategy in nanotechnology to build up devices for several applications. Modeling fully decorated metal oxide nanoparticles of realistic size (few nanometers) in an aqueous environment is a challenging task. In this work, we present a case study relevant for solar-light exploitation and for biomedical applications, i.e., a dopamine-functionalized TiO2 nanoparticle (1700 atoms) in bulk water, for which we have performed an extensive comparative investigation with both MM and QM/MM approaches of the structural properties and of the conformational dynamics. We have used a combined multiscale protocol for a more efficient exploration of the complex conformational space. On the basis of the results of this study and of some QM and experimental data, we have defined strengths and limitations of the existing force field parameters. Our findings will be useful for an improved modeling and simulation of many other similar hybrid bioinorganic nanosystems in an aqueous environment that are pivotal in a broad range of nanotechnological applications.
Abstract The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor involved in the regulation of many patho-physiological processes. Among these, immune system modulation, as well as regulation of skin homeostasis and inflammation, make it a promising target for psoriasis therapy. Tapinarof, an AhR agonist recently approved for psoriasis treatment, exerts its action through antioxidant, anti-inflammatory and barrier-restoring effects. In this study, we employed a computational drug-discovery approach to identify novel AhR modulators with psoriasis therapeutic potential. We performed a multi-step similarity-based screening in PubChem. Application of molecular docking led to the identification of diverse chemical scaffolds with high docking scores and potential AhR activity, some of which belong to chemical classes with known pharmacological relevance. Notably, several identified compounds suggest a possible interplay between AhR signaling and sirtuin modulation, highlighting a previously unexplored avenue in psoriasis therapy. Our findings underscore the potential of computational approaches in accelerating the discovery of novel AhR-targeting agents and provide a foundation for further experimental validation.
