Discovery of PHGDH inhibitors by virtual screening and preliminary structure-activity relationship study.

Phosphoglycerate dehydrogenase (PHGDH) is abnormally expressed in numerous malignant tumor cells and catalyzes the first step of serine biosynthesis, thus becoming a key drug target for antitumor treatment. In this study, compound B2 bearing a benzene-1,3-diamine scaffold was identified by structure-based virtual screening as a novel PHGDH inhibitor with moderate enzymatic activity. The structure-activity relationship study led to the discovery of compound C25 possessing improved enzymatic inhibitory activity and potent inhibitory activity on the proliferation of cells overexpressing PHGDH. The enzyme kinetic assay confirmed that C25 inhibited PHGDH in a nicotinamide adenine dinucleotide (NAD+) competitive manner. Molecular docking and mutagenesis experiment on PHGDH collectively revealed the binding site and key interaction residues of C25 in the PHGDH catalytic site. Taken together, this study provides information on the structural diversity for a further development of potent PHGDH inhibitors.

A retrospective overview of PHGDH and its inhibitors for regulating cancer metabolism.

Emerging evidence suggests that cancer metabolism is closely associated to the serine biosynthesis pathway (SSP), in which glycolytic intermediate 3-phosphoglycerate is converted to serine through a three-step enzymatic transformation. As the rate-limiting enzyme in the first step of SSP, phosphoglycerate dehydrogenase (PHGDH) is overexpressed in various diseases, especially in cancer. Genetic knockdown or silencing of PHGDH exhibits obvious anti-tumor response both in vitro and in vivo, demonstrating that PHGDH is a promising drug target for cancer therapy. So far, several types of PHGDH inhibitors have been identified as a significant and newly emerging option for anticancer treatment. Herein, this comprehensive review summarizes the recent achievements of PHGDH, especially its critical role in cancer and the development of PHGDH inhibitors in drug discovery.

One-Pot Preparation of Tricyclo[5.2.2.04,9]undecanes via Cu-Catalyzed Asymmetric Carboboration of Cyclohexadienone-Tethered Allenes.

The Cu-catalyzed asymmetric carboboration of cyclohexadienone-tethered allenes has been achieved through regioselective ß-borylation of the allenes and subsequent conjugate addition to cyclohexadienones, affording cis-bicyclic frameworks with acceptable yields and high to excellent enantioselectivities. Further conjugate borylation of the carboboration products proved to be a favorable kinetic resolution process, which improved the overall enantioselectivity. Finally, one-pot preparation of highly enantioenriched tricyclo[5.2.2.04,9]undecanes was developed from the cyclohexadienone-tethered allenes through ß-borylation/1,4-addition and subsequent tandem oxidation/intramolecular aldol reaction.

Identification of novel STAT3 inhibitors bearing 2-acetyl-7-phenylamino benzofuran scaffold for antitumour study.

Signal transducer and activator of transcription 3 (STAT3) is identified as a promising target for multiple cancer therapy and attracts widespread concern. Herein, we reported the discovery of a series of 2-acetyl-7-phenylamino benzofuran derivatives as STAT3 inhibitors using scaffold fusion strategy. Further structure activity relationship study led to the discovery of compound C6, which displayed the most potent anti-proliferation activities against MDA-MB-468 cells (IC50 = 0.16 µM). Western blot assay demonstrated that C6 inhibited the activation of STAT3 (Tyr705) without influencing the phosphorylation of STAT1 (Tyr701). Further mechanistic studies indicated that C6 caused a notable G2/M cycle-arresting and early apoptosis in a concentration-dependent manner in MDA-MB-468 cells. Finally, molecular modelling study elucidated the binding mode of C6 in STAT3 SH2 domain.

Design, synthesis and biological evaluation of novel potent STAT3 inhibitors based on BBI608 for cancer therapy.

Persistently activated signal transducer and activator of transcription 3 (STAT3) plays an important role in the development of multiple cancers, and therefore is a potential therapeutic target for cancer prevention. Herein, we report the rational design, synthesis, and biological evaluation of novel potent STAT3 inhibitors based on BBI608. Among them, compound A11 exhibited the most potent in vitro tumor cell growth inhibitory activities toward MDA-MB-231, MDA-MB-468 and HepG2 cells with IC50 values as low as 0.67 ± 0.02 µM, 0.77 ± 0.01 µM and 1.24 ± 0.16 µM, respectively. Fluorescence polarization (FP) assay validated the binding of compound A11 in STAT3 SH2 domain with the IC50 value of 5.18 µM. Further mechanistic studies indicated that A11 inhibited the activation of STAT3 (Y705), and thus reduced the expression of STAT3 downstream genes CyclinD1 and C-Myc. Simultaneously, it induced cancer cell S phase arrest and apoptosis in a concentration-dependent manner. An additional in vivo study revealed that A11 suppressed the MDA-MB-231 xenograft tumor growth in mice at the dose of 10 mg/kg (i.p.) without obvious body-weight loss. Finally, molecular docking study further elucidated the binding mode of A11 in STAT3 SH2 domain.

Molecular modeling studies of 1,2,4-triazine derivatives as novel h-DAAO inhibitors by 3D-QSAR, docking and dynamics simulations.

Human d-amino acid oxidase (h-DAAO) can effectively act on d-serine, which has been actively explored as a novel therapeutic target for treating schizophrenia. In this study, 37 h-DAAO inhibitors based on a 6-hydroxy-1,2,4-triazine-3,5(2H,4H)-dione scaffold were obtained to construct the optimal comparative molecular field analysis (CoMFA, q 2 = 0.613, r 2 = 0.966) and comparative molecular similarity index analysis (CoMSIA, q 2 = 0.669, r 2 = 0.985) models. The results indicate that the models have good predictability and strong stability. Furthermore, contour maps of the three-dimensional quantitative structure-activity relationship (3D-QSAR) revealed the relationships between the structural features and inhibitory activity. A total of nine new h-DAAO inhibitors were designed, which exhibited good predicted pIC50 values. Through molecular docking and molecular dynamics simulation, four essential residues (i.e., Gly313, Arg283, Tyr224 and Tyr228) were considered to interact with the inhibitor. The hydrogen bonds produced by the triazine structure with protein and the hydrophobic interactions with the residues (i.e., Leu51, His217, Gln53 and Leu215) play an important role in the stability of the inhibitor at the binding site of the protein. Additionally, the compounds D1, D3 and D8, with higher predicted activities, were selected by ADME and bioavailability prediction. The present study could offer a reliable theoretical basis for future structural optimisation, design and synthesis of effective antipsychotics.

Predicting chemical toxicity effects based on chemical-chemical interactions.

Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1(st) order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.

Using WPNNA classifier in ubiquitination site prediction based on hybrid features.

Ubiquitination, a reversible protein post-translational modification (PTM), occurs when an amide bond is formed between ubiquitin (a small protein) and the targeted protein. It involves in a wide variety of cellular processes and is associated with various diseases such as Alzheimer's disease. In order to understand ubiquitination at the molecular level, it is important to identify the ubiquitination site by which the ubiquitin binds to. Since experimental methods to determine ubiquitination sites are both expensive and time-consuming, it is necessary to develop in-silico methods to predict ubiquitination sites based on merely the sequential information of the target protein. In this paper, we apply a new classifier called weighted passive nearest neighbor algorithm (WPNNA) to predict the ubiquitination sites. WPNNA was demonstrated to be insensitive to the varied datum densities between different classes. A hybrid of features, including PSSM conservation scores, amino acid factors and disorder scores, are employed to code the protein fragments centered on the possible ubiquitination sites. The Mathew's correlation coefficient (MCC) of our predictor on a training dataset is 0.169 with sensitivity of 31.6% and specificity of 82.9%, and on an independent test dataset is 0.403 with sensitivity of 64.3% and specificity of 75.7%. We compare our predictor with that of a recent published paper which also made predictions on the same datasets. Our predictor achieves much better sensitivities on both datasets than the paper and achieves much better MCC than the paper on the independent test dataset, indicating that the predictor based on WPNNA is as least a good complement to the current state of art in ubiquitination site prediction.