scholarly journals Identification of Novel Inhibitors against Coactivator Associated Arginine Methyltransferase 1 Based on Virtual Screening and Biological Assays

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Fei Ye ◽  
Weiyao Zhang ◽  
Wenchao Lu ◽  
Yiqian Xie ◽  
Hao Jiang ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecule ◽  
Protein Interactions ◽  
Binding Mode ◽  
Protein Protein Interactions ◽  
Arginine Methyltransferase ◽  
Docking Analysis ◽  
Biological Assays ◽  
Biochemical Assays ◽  
Methyltransferase 1

Overexpression of coactivator associated arginine methyltransferase 1 (CARM1), a protein arginine N-methyltransferase (PRMT) family enzyme, is associated with various diseases including cancers. Consequently, the development of small-molecule inhibitors targeting PRMTs has significant value for both research and therapeutic purposes. In this study, together with structure-based virtual screening with biochemical assays, two compounds DC_C11 and DC_C66 were identified as novel inhibitors of CARM1. Cellular studies revealed that the two inhibitors are cell membrane permeable and effectively blocked proliferation of cancer cells including HELA, K562, and MCF7. We further predicted the binding mode of these inhibitors through molecular docking analysis, which indicated that the inhibitors competitively occupied the binding site of the substrate and destroyed the protein-protein interactions between CARM1 and its substrates. Overall, this study has shed light on the development of small-molecule CARM1 inhibitors with novel scaffolds.

Immunosuppressive Small Molecule Discovered by Structure-Based Virtual Screening for Inhibitors of Protein-Protein Interactions

2011 ◽  
Vol 51 (1) ◽  
pp. 258-261 ◽  
Author(s):  
Tim Geppert ◽  
Stefanie Bauer ◽  
Jan A. Hiss ◽  
Elea Conrad ◽  
Michael Reutlinger ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecule ◽  
Protein Interactions ◽  
Protein Protein Interactions

Virtual screening and experimental validation reveal novel small-molecule inhibitors of 14-3-3 protein–protein interactions

2013 ◽  
Vol 49 (76) ◽  
pp. 8468 ◽  
Author(s):  
Philipp Thiel ◽  
Lars Röglin ◽  
Nicole Meissner ◽  
Sven Hennig ◽  
Oliver Kohlbacher ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Small Molecule ◽  
Protein Interactions ◽  
Experimental Validation ◽  
Small Molecule Inhibitors ◽  
Protein Protein Interactions

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Small Molecule Inhibitors ◽  
Computational Techniques ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Computational Approaches ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Therapeutic Measures

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.

Polyamines: Naturally occurring small molecule modulators of electrostatic protein–protein interactions

2010 ◽  
Vol 104 (2) ◽  
pp. 118-125 ◽  
Author(s):  
Anja Berwanger ◽  
Susanne Eyrisch ◽  
Inge Schuster ◽  
Volkhard Helms ◽  
Rita Bernhardt
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Naturally Occurring ◽  
Small Molecule Modulators

scholarly journals Crystal structure and functional implication of bacterial STING

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Tzu-Ping Ko ◽  
Yu-Chuan Wang ◽  
Chia-Shin Yang ◽  
Mei-Hui Hou ◽  
Chao-Jung Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Driving Force ◽  
Binding Mode ◽  
Innate Immune ◽  
Ligand Specificity ◽  
Protein Protein Interactions ◽  
Recognition Mechanism ◽  
Cyclic Dinucleotides ◽  
Phage Propagation ◽  
Complex Crystal

AbstractMammalian innate immune sensor STING (STimulator of INterferon Gene) was recently found to originate from bacteria. During phage infection, bacterial STING sense c-di-GMP generated by the CD-NTase (cGAS/DncV-like nucleotidyltransferase) encoded in the same operon and signal suicide commitment as a defense strategy that restricts phage propagation. However, the precise binding mode of c-di-GMP to bacterial STING and the specific recognition mechanism are still elusive. Here, we determine two complex crystal structures of bacterial STING/c-di-GMP, which provide a clear picture of how c-di-GMP is distinguished from other cyclic dinucleotides. The protein-protein interactions further reveal the driving force behind filament formation of bacterial STING. Finally, we group the bacterial STING into two classes based on the conserved motif in β-strand lid, which dictate their ligand specificity and oligomerization mechanism, and propose an evolution-based model that describes the transition from c-di-GMP-dependent signaling in bacteria to 2’3’-cGAMP-dependent signaling in eukaryotes.

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