IDPs and their complexes in GPCR and nuclear receptor signaling

Identification of Optimal Machine Learning Algorithms and Molecular Fingerprints for Explainable Toxicity Prediction Models Using ToxCast/Tox21 Bioassay Data

Recent studies have primarily focused on introducing novel frameworks to enhance the predictive power of toxicity prediction models by refining molecular representation methods and algorithms. However, these methods are inherently complex and often pose challenges in understanding and explaining, leading to barriers in their regulatory adoption and validation. Therefore, it is necessary to select the optimal model, considering not only model performance but also interpretability. This study aimed to identify the optimal combination of molecular fingerprints (pattern-based versus algorithm-based) and machine learning algorithms (simple versus complex) for developing explainable toxicity prediction models through an comprehensive investigation of the ToxCast/Tox21 bioassay data set. For 1092 ToxCast/Tox21 assays, five molecular fingerprints (MACCS, Morgan, RDKit, Layered, and Patterned) and six algorithms (MLP, GBT, Random Forest, kNN, Logistic Regression, and Naïve Bayes) were used to train the models. Results showed that 35 models revealed acceptable performance (F1 score or accuracy is 0.8 or higher). Among the combinations, either MACCS or Morgan, paired with Random Forest, demonstrated robust performance compared with other molecular fingerprints and algorithms. MACCS and Random Forest are valuable, even when prioritizing interpretability. Consequently, the MACCS-Random Forest combination model based on four assays, targeting G protein-coupled receptor and kinase, were identified and they can be used to discern specific structural features or patterns in chemical compounds, offering explainable insights into toxicity-related chemical structures. This study indicates the importance of not disregarding the utilization of simple models when assessing both predictivity and interpretability within the context of chemical feature-based Tox21 data analysis.

Structural Insights into the Intrinsically Disordered GPCR C-Terminal Region, Major Actor in Arrestin-GPCR Interaction

Arrestin-dependent pathways are a central component of G protein-coupled receptor (GPCRs) signaling. However, the molecular processes regulating arrestin binding are to be further illuminated, in particular with regard to the structural impact of GPCR C-terminal disordered regions. Here, we used an integrated biophysical strategy to describe the basal conformations of the C-terminal domains of three class A GPCRs, the vasopressin V2 receptor (V2R), the growth hormone secretagogue or ghrelin receptor type 1a (GHSR) and the β2-adernergic receptor (β2AR). By doing so, we revealed the presence of transient secondary structures in these regions that are potentially involved in the interaction with arrestin. These secondary structure elements differ from those described in the literature in interaction with arrestin. This suggests a mechanism where the secondary structure conformational preferences in the C-terminal regions of GPCRs could be a central feature for optimizing arrestins recognition.

Ligands and DNA in the allosteric control of retinoid receptors function

Retinoids are a family of compounds that include both vitamin A (all-trans retinol) and its naturally occurring metabolites such as retinoic acids (e.g. all-trans retinoic acid) as well as synthetic analogs. They are critically involved in the regulation of a wide variety of essential biological processes, such as embryogenesis and organogenesis, apoptosis, reproduction, vision, and the growth and differentiation of normal and neoplastic cells in vertebrates. The ability of these small molecules to control the expression of several hundred genes through binding to nuclear ligand-dependent transcription factors accounts for most of their functions. Three retinoic acid receptor (RARα,β,γ) and three retinoid X receptor (RXRα,β,γ) subtypes form a variety of RXR-RAR heterodimers that have been shown to mediate the pleiotropic effects of retinoids through the recruitment of high-molecular weight co-regulatory complexes to response-element DNA sequences found in the promoter region of their target genes. Hence, heterodimeric retinoid receptors are multidomain entities that respond to various incoming signals, such as ligand and DNA binding, by allosteric structural alterations which are the basis of further signal propagation. Here, we provide an overview of the current state of knowledge with regard to the structural mechanisms by which retinoids and DNA response elements act as allosteric effectors that may combine to finely tune RXR-RAR heterodimers activity.

Structural Insights into the Interaction of the Intrinsically Disordered Co-activator TIF2 with Retinoic Acid Receptor Heterodimer (RXR/RAR)

Retinoic acid receptors (RARs) and retinoid X receptors (RXRs) form heterodimers that activate target gene transcription by recruiting co-activator complexes in response to ligand binding. The nuclear receptor (NR) co-activator TIF2 mediates this recruitment by interacting with the ligand-binding domain (LBD) of NRs trough the nuclear receptor interaction domain (TIF2_NRID) containing three highly conserved α-helical LxxLL motifs (NR-boxes). The precise binding mode of this domain to RXR/RAR is not clear due to the disordered nature of TIF2. Here we present the structural characterization of TIF2_NRID by integrating several experimental (NMR, SAXS, Far-UV CD, SEC-MALS) and computational data. Collectively, the data are in agreement with a largely disordered protein with partially structured regions, including the NR-boxes and their flanking regions, which are evolutionary conserved. NMR and X-ray crystallographic data on TIF2_NRID in complex with RXR/RAR reveal a multisite binding of the three NR-boxes as well as an active role of their flanking regions in the interaction.