High-Throughput to Identify Inhibitors of SSB-Protein Interactions

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.

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High-Throughput Mammalian Two-Hybrid Screening for Protein–Protein Interactions Using Transfected Cell Arrays (CAPPIA)

Protein Microarray for Disease Analysis - Methods in Molecular Biology ◽

10.1007/978-1-61779-043-0_11 ◽

2011 ◽

pp. 165-183 ◽

Cited By ~ 2

Author(s):

Andrea Fiebitz ◽

Dominique Vanhecke

Keyword(s):

High Throughput ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Cell Arrays ◽

Two Hybrid ◽

Hybrid Screening

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Fluorescence strategies for high-throughput quantification of protein interactions

Nucleic Acids Research ◽

10.1093/nar/gkr1045 ◽

2011 ◽

Vol 40 (5) ◽

pp. e33-e33 ◽

Cited By ~ 45

Author(s):

Aaron R. Hieb ◽

Sheena D'Arcy ◽

Michael A. Kramer ◽

Alison E. White ◽

Karolin Luger

Keyword(s):

High Throughput ◽

Protein Interactions

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An atlas of protein-protein interactions across mammalian tissues

10.1101/351247 ◽

2018 ◽

Cited By ~ 5

Author(s):

Michael A. Skinnider ◽

Nichollas E. Scott ◽

Anna Prudova ◽

Nikolay Stoynov ◽

R. Greg Stacey ◽

...

Keyword(s):

High Throughput ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Cellular Mechanisms ◽

Stable Isotope Labelling ◽

Proteomic Approach ◽

Mouse Tissues ◽

Mammalian Tissues ◽

Genetically Manipulated

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.

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EndoBind detects endogenous protein-protein interactions in real time

Communications Biology ◽

10.1038/s42003-021-02600-5 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Anke Bill ◽

Sheryll Espinola ◽

Daniel Guthy ◽

Jacob R. Haling ◽

Mylene Lanter ◽

...

Keyword(s):

Real Time ◽

High Throughput ◽

Protein Interactions ◽

Continuous Monitoring ◽

Protein Protein Interactions ◽

Dimer Formation ◽

Endogenous Protein ◽

Extended Duration ◽

Substrate Addition

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.

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A Genome-Wide Arrayed cDNA Screen to Identify Functional Modulators of α7 Nicotinic Acetylcholine Receptors

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/1087057116676086 ◽

2016 ◽

Vol 22 (2) ◽

pp. 155-165 ◽

Cited By ~ 2

Author(s):

Elizabeth B. Rex ◽

Nikhil Shukla ◽

Shenyan Gu ◽

David Bredt ◽

Daniel DiSepio

Keyword(s):

Cdna Library ◽

High Throughput ◽

Protein Interactions ◽

Nicotinic Acetylcholine Receptors ◽

Calcium Flux ◽

Functional Assay ◽

Α7 Nicotinic Acetylcholine Receptor ◽

Nicotinic Acetylcholine ◽

Genome Wide ◽

A Genome

Cellular signaling is in part regulated by the composition and subcellular localization of a series of protein interactions that collectively form a signaling complex. Using the α7 nicotinic acetylcholine receptor (α7nAChR) as a proof-of-concept target, we developed a platform to identify functional modulators (or auxiliary proteins) of α7nAChR signaling. The Broad cDNA library was transiently cotransfected with α7nAChR cDNA in HEK293T cells in a high-throughput fashion. Using this approach in combination with a functional assay, we identified positive modulators of α7nAChR activity. We identified known positive modulators/auxiliary proteins present in the cDNA library that regulate α7nAChR signaling, in addition to identifying novel modulators of α7nAChR signaling. These included NACHO, SPDYE11, TCF4, and ZC3H12A, all of which increased PNU-120596-mediated nicotine-dependent calcium flux. Importantly, these auxiliary proteins did not modulate GluR1(o)-mediated Ca flux. To elucidate a possible mechanism of action, we employed an α7nAChR-HA surface staining assay. NACHO enhanced α7nAChR surface expression; however, the mechanism responsible for the SPDYE11-, TCF4-, and ZC3H12A-dependent modulation of α7nAChR has yet to be defined. This report describes the development and validation of a high-throughput, genome-wide cDNA screening platform coupled to FLIPR functional assays in order to identify functional modulators of α7nAChR signaling.

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INFERRING PROTEIN-PROTEIN INTERACTIONS FROM MESSENGER RNA EXPRESSION PROFILES WITH SVM

Journal of Biological System ◽

10.1142/s0218339005001525 ◽

2005 ◽

Vol 13 (03) ◽

pp. 287-298 ◽

Cited By ~ 1

Author(s):

JUN CAI ◽

YING HUANG ◽

LIANG JI ◽

YANDA LI

Keyword(s):

High Throughput ◽

Protein Interactions ◽

Messenger Rna ◽

Expression Profiles ◽

Support Vector ◽

Svm Classifier ◽

Good Prediction ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

High Throughput Experiments

In post-genomic biology, researchers in the field of proteome focus their attention on the networks of protein interactions that control the lives of cells and organisms. Protein-protein interactions play a useful role in dynamic cellular machinery. In this paper, we developed a method to infer protein-protein interactions based on the theory of support vector machine (SVM). For a given pair of proteins, a new strategy of calculating cross-correlation function of mRNA expression profiles was used to encode SVM vectors. We compared the performance with other methods of inferring protein-protein interaction. Results suggested that, through five-fold cross validation, our SVM model achieved a good prediction. It enables us to show that expression profiles in transcription level can be used to distinguish physical or functional interactions of proteins as well as sequence contents. Lastly, we applied our SVM classifier to evaluate data quality of interaction data sets from four high-throughput experiments. The results show that high-throughput experiments sacrifice some accuracy in determination of interactions because of limitation of experiment technologies.

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