Discovery of a 2,4-Diamino-7-aminoalkoxyquinazoline as a Potent and Selective Inhibitor of Histone Lysine Methyltransferase G9a

SAR exploration of the 2,4-diamino-6,7-dimethoxyquinazoline template led to the discovery of 8 (UNC0224) as a potent and selective G9a inhibitor. A high resolution X-ray crystal structure of the G9a-8 complex, the first co-crystal structure of G9a with a small molecule inhibitor, was obtained. The co-crystal structure validated our binding hypothesis and will enable structure-based design of novel inhibitors. 8 is a useful tool for investigating the biology of G9a and its roles in chromatin remodeling

Protein Lysine Methyltransferase G9a Inhibitors: Design, Synthesis, and Structure Activity Relationships of 2,4-Diamino-7-aminoalkoxy-quinazolines.

Protein lysine methyltransferase G9a, which catalyzes methylation of lysine 9 of histone H3 (H3K9) and lysine 373 (K373) of p53, is over expressed in human cancers. Genetic knockdown of G9a inhibits cancer cell growth and the di-methylation of p53 K373 results in the inactivation of p53. Initial SAR exploration of the 2,4-diamino-6,7-dimethoxyquinazoline template represented by 3a (BIX01294), a selective small molecule inhibitor of G9a and GLP, led to the discovery of 10 (UNC0224) as a potent G9a inhibitor with excellent selectivity. A high resolution X-ray crystal structure of the G9a-10 complex, the first co-crystal structure of G9a with a small molecule inhibitor, was obtained. Based on the structural insights revealed by this co-crystal structure, optimization of the 7-dimethylaminopropoxy side chain of 10 resulted in the discovery of 29 (UNC0321) (Morrison Ki = 63 pM), which is the first G9a inhibitor with picomolar potency and the most potent G9a inhibitor to date

A chemical probe selectively inhibits G9a and GLP methyltransferase activity in cells

Protein lysine methyltransferases G9a and GLP modulate the transcriptional repression of a variety of genes via dimethylation of Lys9 on histone H3 (H3K9me2) as well as dimethylation of non-histone targets. Here we report the discovery of UNC0638, an inhibitor of G9a and GLP with excellent potency and selectivity over a wide range of epigenetic and non-epigenetic targets. UNC0638 treatment of a variety of cell lines resulted in lower global H3K9me2 levels, equivalent to levels observed for small hairpin RNA knockdown of G9a and GLP with the functional potency of UNC0638 being well separated from its toxicity. UNC0638 markedly reduced the clonogenicity of MCF7 cells, reduced the abundance of H3K9me2 marks at promoters of known G9a-regulated endogenous genes and disproportionately affected several genomic loci encoding microRNAs. In mouse embryonic stem cells, UNC0638 reactivated G9a-silenced genes and a retroviral reporter gene in a concentration-dependent manner without promoting differentiation

A single active catalytic site is sufficient to promote transport in P-glycoprotein

P-glycoprotein (Pgp) is an ABC transporter responsible for the ATP-dependent efflux of chemotherapeutic compounds from multidrug resistant cancer cells. Better understanding of the molecular mechanism of Pgp-mediated transport could promote rational drug design to circumvent multidrug resistance. By measuring drug binding affinity and reactivity to a conformation-sensitive antibody we show here that nucleotide binding drives Pgp from a high to a low substrate-affinity state and this switch coincides with the flip from the inward- to the outward-facing conformation. Furthermore, the outward-facing conformation survives ATP hydrolysis: the post-hydrolytic complex is stabilized by vanadate, and the slow recovery from this state requires two functional catalytic sites. The catalytically inactive double Walker A mutant is stabilized in a high substrate affinity inward-open conformation, but mutants with one intact catalytic center preserve their ability to hydrolyze ATP and to promote drug transport, suggesting that the two catalytic sites are randomly recruited for ATP hydrolysis

Means of transferring the emotional state of the characters in the early prose by Ivan Melezh and their equivalents in the Russian translation

The article deals with the transformations used totranslate the stories by Ivan Melezh into the Russian language. Translators often do not care enough to save the author's images, metaphors, emotional characteristics of different characters, destroying and omitting them, which leads to a distortion of the figurative structure of the original text.У артыкуле разглядаюцца перакладчыцкія трансфармацыі пры перадачы на рускую мову апавяданняў Івана Мележа. Перакладчыкі часта недастаткова ашчадна абыходзяцца з аўтарскімі вобразамі, метафарамі, эмацыйнай характарыстыкай персанажаў, разбураючы і апускаючы іх, што вядзе да скажэння вобразнай структуры арыгінальнага тэксту

Demographical Changes of Student Subgroups in MOOCs: Towards Predicting At-Risk Students

Past studies have shown that student engagement in Massive Open Online Courses (MOOCs) could be used to identify at-risk students (students with drop-out tendency). Some studies have further considered student diversity by looking into subgroup behavior. Yet, most of them lack consideration of students’ behavioral changes along the course. Towards bridging the gap, this study clusters students based on both their interaction with the system and their characteristics and explores how their cluster membership changes along the course. The result shows that students’ cluster membership changes significantly in the first half of the course and stabilized in the second half of the course. Our findings provide insight into how students may be engaged in learning on MOOC platforms and suggest the improvement of identifying at-risk students based on their temporal data

Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students

Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex “big data” from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research, looking mainly into overall behavior, may have missed patterns related to student diversity. Using a large dataset from a MOOC offered by FutureLearn, we delve into a new way of investigating hidden patterns through both machine learning and statistical modelling. In this paper, we report on clustering analysis of student activities and comparative analysis on both behavioral patterns and demographical patterns between student subpopulations in the MOOC. Our approach allows for a deeper understanding of how MOOC students behave and achieve. Our findings may be used to design adaptive strategies towards an enhanced MOOC experience