scholarly journals Application of a novel in silico high-throughput screen to identify selective inhibitors for protein–protein interactions

2010 ◽  
Vol 20 (18) ◽  
pp. 5411-5413 ◽  
Author(s):  
Catherine Joce ◽  
Joshua A. Stahl ◽  
Mitesh Shridhar ◽  
Mark R. Hutchinson ◽  
Linda R. Watkins ◽  
...  
Author(s):  
Alexander Goncearenco ◽  
Minghui Li ◽  
Franco L. Simonetti ◽  
Benjamin A. Shoemaker ◽  
Anna R. Panchenko

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106413 ◽  
Author(s):  
Sunita Yadav ◽  
Smita Gupta ◽  
Chandrabose Selvaraj ◽  
Pawan Kumar Doharey ◽  
Anita Verma ◽  
...  

2004 ◽  
Vol 5 (5) ◽  
pp. 382-402 ◽  
Author(s):  
Michael Cornell ◽  
Norman W. Paton ◽  
Stephen G. Oliver

Global studies of protein–protein interactions are crucial to both elucidating gene function and producing an integrated view of the workings of living cells. High-throughput studies of the yeast interactome have been performed using both genetic and biochemical screens. Despite their size, the overlap between these experimental datasets is very limited. This could be due to each approach sampling only a small fraction of the total interactome. Alternatively, a large proportion of the data from these screens may represent false-positive interactions. We have used the Genome Information Management System (GIMS) to integrate interactome datasets with transcriptome and protein annotation data and have found significant evidence that the proportion of false-positive results is high. Not all high-throughput datasets are similarly contaminated, and the tandem affinity purification (TAP) approach appears to yield a high proportion of reliable interactions for which corroborating evidence is available. From our integrative analyses, we have generated a set of verified interactome data for yeast.


2018 ◽  
Author(s):  
Michael A. Skinnider ◽  
Nichollas E. Scott ◽  
Anna Prudova ◽  
Nikolay Stoynov ◽  
R. Greg Stacey ◽  
...  

SummaryCellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the complete network of biologically relevant protein-protein interactions, the interactome, has therefore been a central objective of high-throughput biology. Yet, because widely used methods for high-throughput interaction discovery rely on heterologous expression or genetically manipulated cell lines, the dynamics of protein interactions across physiological contexts are poorly understood. Here, we use a quantitative proteomic approach combining protein correlation profiling with stable isotope labelling of mammals (PCP SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide the first proteome-scale survey of interactome dynamics across mammalian tissues, revealing over 27,000 unique interactions with an accuracy comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewiring of protein interactions across tissues is widespread, and is poorly predicted by gene expression or coexpression. Rewired proteins are tightly regulated by multiple cellular mechanisms and implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Anke Bill ◽  
Sheryll Espinola ◽  
Daniel Guthy ◽  
Jacob R. Haling ◽  
Mylene Lanter ◽  
...  

AbstractWe present two high-throughput compatible methods to detect the interaction of ectopically expressed (RT-Bind) or endogenously tagged (EndoBind) proteins of interest. Both approaches provide temporal evaluation of dimer formation over an extended duration. Using examples of the Nrf2-KEAP1 and the CRAF-KRAS-G12V interaction, we demonstrate that our method allows for the detection of signal for more than 2 days after substrate addition, allowing for continuous monitoring of endogenous protein-protein interactions in real time.


PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e112082 ◽  
Author(s):  
Stefania Correale ◽  
Ivan de Paola ◽  
Carmine Marco Morgillo ◽  
Antonella Federico ◽  
Laura Zaccaro ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document