scholarly journals Discovery of Small-Molecule Antagonists of the PWWP Domain of NSD2

2020 ◽  
Author(s):  
Renato Ferreira de Freitas ◽  
Yanli Liu ◽  
Magdalena M. Szewczyk ◽  
Naimee Mehta ◽  
Fengling Li ◽  
...  

ABSTRACTIncreased activity of the lysine methyltrans-ferase NSD2 driven by translocation and activating mutations is associated with multiple myeloma and acute lymphoblastic leukemia, but no NSD2-targeting chemical probe has been reported to date. Here, we present the first antagonists that block the protein-protein interaction between the N-terminal PWWP domain of NSD2 and H3K36me2. Using virtual screening and experimental validation, we identified the small-molecule antagonist 3f, which binds to the NSD2-PWWP1 domain with a Kd of 3.4 μM and abrogates histone H3K36me2 binding in cells. This study establishes an alternative approach to targeting NSD2 and provides a small-molecule antagonist that can be further optimized into a chemical probe to better understand the cellular function of this protein.

2020 ◽  
Author(s):  
Carrow Wells ◽  
David Drewry ◽  
Julie E. Pickett ◽  
Alison D. Axtman

Building upon a wealth of published knowledge surrounding the pyrazolopyrimidine scaffold, we designed a small library around the most selective small molecule CK2 inhibitors reported. Through extensive evaluation of this library we identified inhibitor 24 (SGC-CK2-1) as a potent, selective, and cell-active CK2 chemical probe. Remarkably, despite years of research pointing to CK2 as a key driver in cancer, our probe did not elicit an antiproliferative phenotype in cell lines tested. While many publications have attempted tocharacterize CK2 function, CK2 biology is complex and a high-quality chemical tool like SGC-CK2-1 will aid in connecting CK2 functions to phenotypes.


2011 ◽  
Vol 16 (8) ◽  
pp. 869-877 ◽  
Author(s):  
Duncan I. Mackie ◽  
David L. Roman

In this study, the authors used AlphaScreen technology to develop a high-throughput screening method for interrogating small-molecule libraries for inhibitors of the Gαo–RGS17 interaction. RGS17 is implicated in the growth, proliferation, metastasis, and the migration of prostate and lung cancers. RGS17 is upregulated in lung and prostate tumors up to a 13-fold increase over patient-matched normal tissues. Studies show RGS17 knockdown inhibits colony formation and decreases tumorigenesis in nude mice. The screen in this study uses a measurement of the Gαo–RGS17 protein–protein interaction, with an excellent Z score exceeding 0.73, a signal-to-noise ratio >70, and a screening time of 1100 compounds per hour. The authors screened the NCI Diversity Set II and determined 35 initial hits, of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC50 <10 µM in dose–response experiments. Four exhibited IC50 values <6 µM while inhibiting the Gαo–RGS17 interaction >50% when compared to a biotinylated glutathione-S-transferase control. This report describes the first high-throughput screen for RGS17 inhibitors, as well as a novel paradigm adaptable to many other RGS proteins, which are emerging as attractive drug targets for modulating G-protein-coupled receptor signaling.


2011 ◽  
Vol 47 (26) ◽  
pp. 7512 ◽  
Author(s):  
Amanda L. Garner ◽  
Kim D. Janda

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.


2015 ◽  
Vol 30 (6) ◽  
pp. 1487-1494 ◽  
Author(s):  
Magdalena Dudek ◽  
Monika Marcinkowska ◽  
Adam Bucki ◽  
Adrian Olczyk ◽  
Marcin Kołaczkowski

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