Structure-Based Understanding of Binding Affinity and Mode of Estrogen Receptor α Agonists and Antagonists

<div><p>The flexible hydrophobic ligand binding pocket (LBP) of estrogen receptor α (ERα) allows the binding of a wide variety of endocrine disruptors. Upon ligand binding, the LBP reshapes around the contours of the ligand and stabilizes the complex by complementary hydrophobic interactions and specific hydrogen bonds with the ligand. Here we present a framework for quantitative analysis of the steric and electronic features of the human ERα-ligand complex using three dimensional (3D) protein-ligand interaction description combined with 3D-QSAR approach. An empirical hydrophobicity density field is applied to account for hydrophobic contacts of ligand within the LBP. The obtained 3D-QSAR model revealed that hydrophobic contacts primarily determine binding affinity and govern binding mode with hydrogen bonds. Several residues of the LBP appear to be quite flexible and adopt a spectrum of conformations in various ERα-ligand complexes, in particular His524. The 3D-QSAR was combined with molecular docking based on three receptor conformations to accommodate receptor flexibility. The model indicates that the dynamic character of the LBP allows accommodation and stable binding of structurally diverse ligands, and proper representation of the protein flexibility is critical for reasonable description of binding of the ligands. Our results provide a quantitative and mechanistic understanding of binding affinity and mode of <i>ER</i>α agonists and antagonists that may be applicable to other nuclear receptors.</p></div

Classification of His524 conformations (PDB IDs: 2YJA, 4IVY, and 4IWC).

<p>Closed (A) and moved back (B) conformations which stabilize protein-ligand complex through hydrogen bond and hydrophobic interaction, respectively. Open conformation (C) that provides an expanded binding pocket for ligands longer than 13 Å. Ligands are colored in orange with hydrogen, oxygen, nitrogen, sulfur, and fluorine atoms in white, red, blue, yellow, and green, respectively.</p

Advancing Fifth Percentile Hazard Concentration Estimation Using Toxicity-Normalized Species Sensitivity Distributions

The species sensitivity distribution (SSD) is an internationally accepted approach to hazard estimation using the probability distribution of toxicity values that is representative of the sensitivity of a group of species to a chemical. Application of SSDs in ecological risk assessment has been limited by insufficient taxonomic diversity of species to estimate a statistically robust fifth percentile hazard concentration (HC5). We used the toxicity-normalized SSD (SSDn) approach, (Lambert, F. N.; Raimondo, S.; Barron, M. G. Environ. Sci. Technol.2022,56, 8278–8289), modified to include all possible normalizing species, to estimate HC5 values for acute toxicity data for groups of carbamate and organophosphorous insecticides. We computed mean and variance of single chemical HC5 values for each chemical using leave-one-out (LOO) variance estimation and compared them to SSDn and conventionally estimated HC5 values. SSDn-estimated HC5 values showed low uncertainty and high accuracy compared to single-chemical SSDs when including all possible combinations of normalizing species within the chemical-taxa grouping (carbamate-all species, carbamate-fish, organophosphate-fish, and organophosphate-invertebrate). The SSDn approach is recommended for estimating HC5 values for compounds with insufficient species diversity for HC5 computation or high uncertainty in estimated single-chemical HC5 values. Furthermore, the LOO variance approach provides SSD practitioners with a simple computational method to estimate confidence intervals around an HC5 estimate that is nearly identical to the conventionally estimated HC5

Prediction of RBA of 17β-estradiol derivatives based on protein-ligand complex structures from crystal structure modification (blue bars) or molecular docking (red bars).

<p>RBA is described by contributions from hydrophobic contacts (rectangle bars) and hydrogen bonds (hexagon bars). Black dots represent the experimental log RBA.</p

Hydrophobic contacts (log <i>P</i>_<i>C</i>) of n-alkyl group vs log RBA residual of n-alkyl 4-phenol (gray circles) and n-alkyl paraben (black filled circles).

<p>Hydrophobic contacts (log <i>P</i>_<i>C</i>) of n-alkyl group vs log RBA residual of n-alkyl 4-phenol (gray circles) and n-alkyl paraben (black filled circles).</p

Summary of pharmacophore, fingerprint, and QSAR model parameters.

<p>Summary of pharmacophore, fingerprint, and QSAR model parameters.</p

Advancing Fifth Percentile Hazard Concentration Estimation Using Toxicity-Normalized Species Sensitivity Distributions

The species sensitivity distribution (SSD) is an internationally accepted approach to hazard estimation using the probability distribution of toxicity values that is representative of the sensitivity of a group of species to a chemical. Application of SSDs in ecological risk assessment has been limited by insufficient taxonomic diversity of species to estimate a statistically robust fifth percentile hazard concentration (HC5). We used the toxicity-normalized SSD (SSDn) approach, (Lambert, F. N.; Raimondo, S.; Barron, M. G. Environ. Sci. Technol.2022,56, 8278–8289), modified to include all possible normalizing species, to estimate HC5 values for acute toxicity data for groups of carbamate and organophosphorous insecticides. We computed mean and variance of single chemical HC5 values for each chemical using leave-one-out (LOO) variance estimation and compared them to SSDn and conventionally estimated HC5 values. SSDn-estimated HC5 values showed low uncertainty and high accuracy compared to single-chemical SSDs when including all possible combinations of normalizing species within the chemical-taxa grouping (carbamate-all species, carbamate-fish, organophosphate-fish, and organophosphate-invertebrate). The SSDn approach is recommended for estimating HC5 values for compounds with insufficient species diversity for HC5 computation or high uncertainty in estimated single-chemical HC5 values. Furthermore, the LOO variance approach provides SSD practitioners with a simple computational method to estimate confidence intervals around an HC5 estimate that is nearly identical to the conventionally estimated HC5

Description of hydrophobic interactions on the contact surface.

<p>Hydrophobic and hydrophilic grid points are represented by blue and red circles, respectively, on the SAS of the ligand. Hydrophobic grid points within the <i>van der Waals</i> surface of hydrophobic residues are marked by filled blue circles.</p

Advancing Fifth Percentile Hazard Concentration Estimation Using Toxicity-Normalized Species Sensitivity Distributions

The species sensitivity distribution (SSD) is an internationally accepted approach to hazard estimation using the probability distribution of toxicity values that is representative of the sensitivity of a group of species to a chemical. Application of SSDs in ecological risk assessment has been limited by insufficient taxonomic diversity of species to estimate a statistically robust fifth percentile hazard concentration (HC5). We used the toxicity-normalized SSD (SSDn) approach, (Lambert, F. N.; Raimondo, S.; Barron, M. G. Environ. Sci. Technol.2022,56, 8278–8289), modified to include all possible normalizing species, to estimate HC5 values for acute toxicity data for groups of carbamate and organophosphorous insecticides. We computed mean and variance of single chemical HC5 values for each chemical using leave-one-out (LOO) variance estimation and compared them to SSDn and conventionally estimated HC5 values. SSDn-estimated HC5 values showed low uncertainty and high accuracy compared to single-chemical SSDs when including all possible combinations of normalizing species within the chemical-taxa grouping (carbamate-all species, carbamate-fish, organophosphate-fish, and organophosphate-invertebrate). The SSDn approach is recommended for estimating HC5 values for compounds with insufficient species diversity for HC5 computation or high uncertainty in estimated single-chemical HC5 values. Furthermore, the LOO variance approach provides SSD practitioners with a simple computational method to estimate confidence intervals around an HC5 estimate that is nearly identical to the conventionally estimated HC5

A Practical Probabilistic Graphical Modeling Tool for Weighing Ecological Risk-Based Evidence

<p>Past weight-of-evidence frameworks for adverse ecological effects have provided soft-scoring procedures for judgments based on the quality and measured attributes of evidence. Here, we provide a flexible probabilistic structure for weighing and integrating lines of evidence for ecological risk determinations. Probabilistic approaches can provide both a quantitative weighing of lines of evidence and methods for evaluating risk and uncertainty. The current modeling structure was developed for propagating uncertainties in measured endpoints and their influence on the plausibility of adverse effects. To illustrate the approach, we apply the model framework to the sediment quality triad using example lines of evidence for sediment chemistry measurements, bioassay results, and in situ infauna diversity of benthic communities using a simplified hypothetical case study. We then combine the three lines evidence and evaluate sensitivity to the input parameters, and show how uncertainties are propagated and how additional information can be incorporated to rapidly update the probability of impacts. The developed network model can be expanded to accommodate additional lines of evidence, variables and states of importance, and different types of uncertainties in the lines of evidence including spatial and temporal as well as measurement errors.</p