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Endocrine-disrupting chemicals (EDCs) can inadvertently interact with 12 classic nuclear receptors (NRs) that disrupt the endocrine system and cause adverse effects. There is no widely accepted understanding about what structural features make thousands of EDCs able to activate different NRs as well as how these structural features exert their functions and induce different outcomes at the cellular level. This paper applies the hierarchical characteristic fragment methodology and high-throughput screening molecular docking to comprehensively explore the structural and functional features of EDCs for the 12 NRs based on more than 7000 chemicals from curated datasets. EDCs share three levels of key fragments. The primary and secondary fragments are associated with the binding of EDCs to four groups of receptors: steroidal nuclear receptors (SNRs, including androgen, estrogen, glucocorticoid, mineralocorticoid, and progesterone), retinoic acid receptors, thyroid hormone receptors, and vitamin D receptors. The tertiary fragments determine the activity type by interacting with two key locations in the ligand-binding domains of NRs (N-H5-H3-C and N-H7-H11-C for SNRs and N-H5-H5'-H2'-H3-C and N-H6'-H11-C for non-SNRs). The resulting compiled structural fragments of EDCs together with elucidated compound NR binding modes provide a framework for understanding the interactions between EDCs and NRs, facilitating faster and more accurate screening of EDCs for multiple NRs in the future.

Bisphenols (BPs) have the potential to interfere with the androgen receptor (AR). However, in silico screening and substitute design were difficult because little was known about the mechanisms by ...

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.

Endocrine disrupting chemicals (EDCs) are of great concern given their potential influence on the endocrine system. In silico methods for the evaluation of EDCs have been widely recognized. However, subcellular molecular mechanisms of action, such as ligand-receptor interactions, receptor conformational switch and protein-protein interactions, are needed for the development of mechanism-based in silico models. Here, molecular mechanisms of action for steroid hormone receptors (SHRs), the important targets of EDCs, are systematically reviewed. Ligand binding and ligand-receptor interactions are required for SHR activation, and facilitate the nuclear translocation and the dimerization of SHRs. Coregulator recruitment results from conformational switch of SHR, which regulates the transcription and results in either an agonistic or an antagonistic effect. EDCs potentially interfere with SHRs by influencing ligand-receptor interactions, nuclear translocation, dimerization and coregulator recruitment. These new findings shed light on the development of mechanism-based computational models for the evaluation of EDCs.