Theoretically exploring selective-binding mechanisms of BRD4 through integrative computational approaches

Discovery of novel BRD4-BD2 inhibitors via in silico approaches: QSAR techniques, molecular docking, and molecular dynamics simulations

Bromodomain-containing protein 4(BRD4) plays an important role in the occurrence and development of various malignant tumors, which has attracted the attention of scientific research institutions and pharmaceutical companies. The structural modification of most currently available BRD4 inhibitors is relatively simple, but the drug effectiveness is limited. Research has found that the inhibition of BD1 may promote the differentiation of oligodendrocyte progenitor cell; however, the inhibition of BD2 will not cause this outcome. Therefore, newly potential drugs which target BRD4-BD2 need further research. Herein, we initially built QSAR models out of 49 compounds using HQSAR, CoMFA, CoMSIA, and Topomer CoMFA technology. All of the models have shown suitable reliabilities (q2 = 0.778, 0.533, 0.640, 0.702, respectively) and predictive abilities (r2pred = 0.716, 0.6289, 0.6153, 0.7968, respectively) for BRD4-BD2 inhibitors. On the basis of QSAR results and the search of the R-group in the topomer search module, we designed 20 new compounds with high activity that showed appropriate docking score and suitable ADMET. Docking studies and MD simulation were carried out to reveal the amino acid residues (Asn351, Cys347, Tyr350, Pro293, and Asp299) at the active site of BRD4-BD2. Free energy calculations and free energy landscapes verified the stable binding results and indicated stable conformations of the complexes. These theoretical studies provide guidance and theoretical basis for designing and developing novel BRD4-BD2 inhibitors.

Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations

BRD9 and TAF1(2) have been regarded as significant targets of drug design for clinically treating acute myeloid leukemia, malignancies, and inflammatory diseases. In this study, multiple short molecular dynamics simulations combined with the molecular mechanics generalized Born surface area method were employed to investigate the binding selectivity of three ligands, 67B, 67C, and 69G, to BRD9/TAF1(2) with IC50 values of 230/59 nM, 1400/46 nM, and 160/410 nM, respectively. The computed binding free energies from the MM-GBSA method displayed good correlations with that provided by the experimental data. The results indicate that the enthalpic contributions played a critical factor in the selectivity recognition of inhibitors toward BRD9 and TAF1(2), indicating that 67B and 67C could more favorably bind to TAF1(2) than BRD9, while 69G had better selectivity toward BRD9 over TAF1(2). In addition, the residue-based free energy decomposition approach was adopted to calculate the inhibitor–residue interaction spectrum, and the results determined the gatekeeper (Y106 in BRD9 and Y1589 in TAF1(2)) and lipophilic shelf (G43, F44, and F45 in BRD9 and W1526, P1527, and F1528 in TAF1(2)), which could be identified as hotspots for designing efficient selective inhibitors toward BRD9 and TAF1(2). This work is also expected to provide significant theoretical guidance and insightful molecular mechanisms for the rational designs of efficient selective inhibitors targeting BRD9 and TAF1(2).

Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors

A novel decision-making procedure is proposed here for the first time to identify active/inactive and selective/non-selective dual inhibitors using consensus approaches and pools of k-nearest neighbours (kNN) classifications instead of individual models. Dual BRD4/PLK1 inhibition with adequate selectivity is a potential therapeutic strategy for targeting tumour cells in high-risk patients. We report the unique way to identify both active and selective dual BRD4/PLK1 inhibitors using consensus and kNN strategies together with two sources of receptor-based and ligand-based information which are the ranked binding energies of residues and important molecular features, respectively. The results of consensus approaches were compared with the results of individual kNN models. The chemical space similarity was measured using three different distance functions to increase the reliability. All activity and selectivity classification models were validated using cross-validation and y-randomization tests. The outcomes show that consensus approaches can increase the reliability and accuracy of active/inactive or selective/non-selective detections up to 90%. Consensus approaches also reached more balanced values of sensitivity and specificity compared to the individual kNN models because of the compensation in the integration of diverse sources of information.

Computational studies with flavonoids and terpenoids as BRPF1 inhibitors: in silico biological activity prediction, molecular docking, molecular dynamics simulations, MM/PBSA calculations

The BRPF1 protein is encoded by the BRPF1 gene. In addition, the BRPF1 gene is known to be upregulated in leukaemia. Recent studies have shown that it is also overexpressed in hepatocellular carcinoma (HCC) as well. Therefore, BRPF1 is a significant target for anti-cancer drug development studies, especially on HCC. 40 terpenoids and flavonoids were chosen because of their anticancer properties given in the literature. In this study, the biological activity of molecules was also investigated with in silico structure-activity relationship analysis. In addition, interactions between a series of terpenoids and flavonoids and the BRPF1 protein were investigated by molecular docking and molecular dynamics simulations. The energy change caused by the interactions of BRPF1 with different compounds was also evaluated by MM/PBSA calculations. It has been revealed that compound 5 (-9.2 kcal/mol), a kind of secoclerodane type diterpenoid, has a higher affinity both compared to other flavonoids and terpenoids, and 9F9 (-7.9 kcal/mol), a selective BRPF1 inhibitor. The study presented in this article demonstrates that compound 5, as a natural product, could form a chemical scaffold for the development of selective BRPF1 bromodomain inhibitors.