High throughput strategies for probing the different organizational levels of protein interaction networks

2013 ◽  
Vol 9 (9) ◽  
pp. 2201 ◽  
Author(s):  
Anders R. Kristensen ◽  
Leonard J. Foster
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Organizational Levels

Integrative analysis of human omics data using biomolecular networks

2016 ◽  
Vol 12 (10) ◽  
pp. 2953-2964 ◽  
Author(s):  
Jonathan L. Robinson ◽  
Jens Nielsen
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Omics Data ◽  
Protein Protein Interaction ◽  
Biomolecular Networks ◽  
New Information ◽  
Protein Protein Interaction Networks ◽  
Genome Scale

Biomolecular networks, such as genome-scale metabolic models and protein–protein interaction networks, facilitate the extraction of new information from high-throughput omics data.

scholarly journals Computational identification of signaling pathways in protein interaction networks

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1522
Author(s):  
Angela U. Makolo ◽  
Temitayo A. Olagunju
Keyword(s):  
Signaling Pathways ◽  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interaction

The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained from high-throughput experiments. However, these high-throughput methods are known to produce very high rates of false positive and negative interactions. To construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed.A weighted interaction graph of Saccharomyces Cerevisiae was constructed. The weights were obtained using a Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model.We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. Cerevisiae.

Computational Models for Self-Interacting Proteins Prediction

2020 ◽  
Vol 27 (5) ◽  
pp. 392-399
Author(s):  
Jia Qu ◽  
Yan Zhao ◽  
Li Zhang ◽  
Shu-Bin Cai ◽  
Zhong Ming ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Biomedical Research ◽  
Protein Interaction ◽  
Computational Models ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Interacting Proteins ◽  
Cellular Functions ◽  
And Function

: Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other, have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs). Knowing whether a protein can act on itself is important to understand its functions. Previous studies on SIPs have focused on their structures and functions, while their whole properties are less emphasized. Not surprisingly, identifying SIPs is one of the most important works in biomedical research, which will help to understanding the function and mechanism of proteins. It is worth noting that high throughput methods can be used for SIPs prediction, but can be costly, time consuming and challenging. Therefore, it is urgent to design computational models for the identification of SIPs. In this review, the concept and function of SIPs were introduced in detail. We further introduced SIPs data and some excellent computational models that have been designed for SIPs prediction. Specially, the most existing approaches were developed based on machine learning through carrying out different extract feature methods. Finally, we discussed several difficult problems in developing computational models for SIPs prediction.

High-throughput identification of transient extracellular protein interactions

2010 ◽  
Vol 38 (4) ◽  
pp. 919-922 ◽  
Author(s):  
Gavin J. Wright ◽  
Stephen Martin ◽  
K. Mark Bushell ◽  
Christian Söllner
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Living Cell ◽  
Protein Interaction Networks ◽  
Extracellular Protein ◽  
Interaction Networks ◽  
Receptor Proteins ◽  
Major Class ◽  
Extracellular Environment

Protein interactions are highly diverse in their biochemical nature, varying in affinity and are often dependent on the surrounding biochemical environment. Given this heterogeneity, it seems unlikely that any one method, and particularly those capable of screening for many protein interactions in parallel, will be able to detect all functionally relevant interactions that occur within a living cell. One major class of interactions that are not detected by current popular high-throughput methods are those that occur in the extracellular environment, especially those made by membrane-embedded receptor proteins. In the present article, we discuss some of our recent research in the development of a scalable assay to identify this class of protein interaction and some of the findings from its application in the construction of extracellular protein interaction networks.

Gaining confidence in high-throughput protein interaction networks

2003 ◽  
Vol 22 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Joel S Bader ◽  
Amitabha Chaudhuri ◽  
Jonathan M Rothberg ◽  
John Chant
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks

scholarly journals High-throughput characterization of protein–protein interactions by reprogramming yeast mating

2017 ◽  
Vol 114 (46) ◽  
pp. 12166-12171 ◽  
Author(s):  
David Younger ◽  
Stephanie Berger ◽  
David Baker ◽  
Eric Klavins
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Binding Proteins ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Mating Efficiency ◽  
Extracellular Environment

High-throughput methods for screening protein–protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination ofSaccharomyces cerevisiaecan be reprogrammed to link interaction strength with mating efficiency using synthetic agglutination (SynAg). Validation of SynAg with 89 previously characterized interactions shows a log-linear relationship between mating efficiency and protein binding strength for interactions withKds ranging from below 500 pM to above 300 μM. Using induced chromosomal translocation to pair barcodes representing binding proteins, thousands of distinct interactions can be screened in a single pot. We demonstrate the ability to characterize protein interaction networks in a modifiable environment by introducing a soluble peptide that selectively disrupts a subset of interactions in a representative network by up to 800-fold. SynAg enables the high-throughput, quantitative characterization of protein–protein interaction networks in a fully defined extracellular environment at a library-on-library scale.

Simple Protein Complex Purification and Identification Method for High-Throughput Mapping of Protein Interaction Networks

2005 ◽  
Vol 4 (2) ◽  
pp. 268-274 ◽  
Author(s):  
Lye Meng Markillie ◽  
Chiann-Tso Lin ◽  
Joshua N. Adkins ◽  
Deanna L. Auberry ◽  
Eric A. Hill ◽  
...  
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Complex ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Identification Method
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