scholarly journals Meeting Review: Bioinformatics and Medicine – From Molecules to Humans, Virtual and Real

2002 ◽  
Vol 3 (3) ◽  
pp. 270-276
Author(s):  
Roslin Russell
Keyword(s):  
Protein Structure ◽  
Modeling And Simulation ◽  
High Throughput ◽  
Protein Interactions ◽  
Life Sciences ◽  
Systems Modeling ◽  
Protein Protein Interactions ◽  
Research Institutes ◽  
The Government ◽  
Structure Research

The Industrialization Workshop Series aims to promote and discuss integration, automation, simulation, quality, availability and standards in the high-throughput life sciences. The main issues addressed being the transformation of bioinformatics and bioinformaticsbased drug design into a robust discipline in industry, the government, research institutes and academia. The latest workshop emphasized the influence of the post-genomic era on medicine and healthcare with reference to advanced biological systems modeling and simulation, protein structure research, protein-protein interactions, metabolism and physiology. Speakers included Michael Ashburner, Kenneth Buetow, Francois Cambien, Cyrus Chothia, Jean Garnier, Francois Iris, Matthias Mann, Maya Natarajan, Peter Murray-Rust, Richard Mushlin, Barry Robson, David Rubin, Kosta Steliou, John Todd, Janet Thornton, Pim van der Eijk, Michael Vieth and Richard Ward.

scholarly journals A critical and Integrated View of the Yeast Interactome

2004 ◽  
Vol 5 (5) ◽  
pp. 382-402 ◽  
Author(s):  
Michael Cornell ◽  
Norman W. Paton ◽  
Stephen G. Oliver
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
False Positive ◽  
Affinity Purification ◽  
Information Management System ◽  
Positive Interactions ◽  
Global Studies ◽  
Protein Protein Interactions ◽  
Integrative Analyses ◽  
Genome Information

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.

scholarly journals An atlas of protein-protein interactions across mammalian tissues

2018 ◽  
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.

scholarly journals EndoBind detects endogenous protein-protein interactions in real time

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.

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

2005 ◽  
Vol 13 (03) ◽  
pp. 287-298 ◽  
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.

The study of protein–protein interactions in bacteria

2012 ◽  
Vol 58 (11) ◽  
pp. 1241-1257 ◽  
Author(s):  
Roberto Velasco-García ◽  
Rocío Vargas-Martínez
Keyword(s):  
Hybrid System ◽  
High Throughput ◽  
Protein Interactions ◽  
Specific Protein ◽  
False Positives ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Extensive Data ◽  
False Positives And Negatives

Many of the functions fulfilled by proteins in the cell require specific protein–protein interactions (PPI). During the last decade, the use of high-throughput experimental technologies, primarily based on the yeast 2-hybrid system, generated extensive data currently located in public databases. This information has been used to build interaction networks for different species. Unfortunately, due to the nature of the yeast 2-hybrid system, these databases contain many false positives and negatives, thus they require purging. A method for confirming these PPI is to test them using a technique that operates in vivo and detects binary PPI. This article comprises an overview of the study of PPI and describes the main techniques that have been used to identify bacterial PPI, prioritizing those that can be used for their verification, and it also mentions a number of PPI that have been identified or confirmed using these methods.

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