In silico exploration of hydroxylated polychlorinated biphenyls as estrogen receptor β ligands by 3D-QSAR, molecular docking and molecular dynamics simulations

Discovery of novel bromodomain-containing protein 4 (BRD4-BD1) inhibitors combined with 3d-QSAR, molecular docking and molecular dynamics in silico

Bromine-containing domain protein 4 (BRD4) plays a crucial role in regulating transcription and genome stability. Selective inhibitors of BRD4-BD1 can specifically target specific bromine domains to affect cell proliferation, apoptosis, and differentiation. In this work, 43 selective benzoazepinone BRD4-BD1 inhibitors were studied using molecular simulations and three-dimensional quantitative conformation relationships (3D-QSAR). A reliable 3D-QSAR model was established based on COMFA (Q2 = 0.532, R2 = 0.981) and COMSIA (S + E + H (Q2 = 0.536, R2 = 0.979) two different analysis methods. Through 3D-QSAR model prediction and quantum chemical analysis, 15 small molecules with stronger inhibitory activity than the template compounds were constructed, and 5 new compounds with higher predictive activity and binding affinity were screened by molecular docking and ADMET methods. According to the molecular dynamics simulation, the key residues that can interact with BRD4-BD1 protein and molecular docking results are consistent, including ASN140, MET132, GLN85, MET105, ASN135 and TYR97. From the MD trajectory, we calculated and analyzed RMSD, RMSF, free binding energy, FECM, DCCM and PCA, the loop region formed by amino acids VAL45∼PRO62 showed α-helix, β-folding and clustering towards the active center with the extension of simulation time. Further optimization of the structure of active candidate compounds A6, A11, A14, and A15 will provide the necessary theoretical basis for the synthesis and activity evaluation of novel BRD4-BD1 inhibitors.Communicated by Ramaswamy H. Sarma.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.