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.

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.

scholarly journals High-Throughput Characterization of Protein-Protein Interactions by Reprogramming Yeast Mating

2017 ◽  
Author(s):  
David Younger ◽  
Stephanie Berger ◽  
David Baker ◽  
Eric Klavins
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
Interaction Strength ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Quantitative Characterization ◽  
Mating Efficiency ◽  
Extracellular Environment

AbstractHigh-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 of S. cerevisiae can 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 with KD’s 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.Significance StatementDe novo engineering of protein binders often requires experimental screening to select functional variants from a design library. We have achieved high-throughput, quantitative characterization of protein-protein binding interactions without requiring purified recombinant proteins, by linking interaction strength with yeast mating. Using a next-generation sequencing output, we have characterized protein networks consisting of thousands of pairwise interactions in a single tube and have demonstrated the effect of changing the binding environment. This approach addresses an existing bottleneck in protein binder design by enabling the high-throughput and quantitative characterization of binding strength between designed protein libraries and multiple target proteins in a fully defined environment.

Discovering Interaction Motifs from Protein Interaction Networks

2009 ◽  
pp. 99-116 ◽  
Author(s):  
Hugo Willy
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Patterns ◽  
Protein Protein Interaction ◽  
Family Based ◽  
High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

scholarly journals Quantifying noise in mass spectrometry and yeast two-hybrid protein interaction detection experiments

2015 ◽  
Vol 12 (110) ◽  
pp. 20150573 ◽  
Author(s):  
A. Annibale ◽  
A. C. C. Coolen ◽  
N. Planell-Morell
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Yeast Two Hybrid ◽  
Binary Matrix ◽  
Interaction Detection ◽  
Experimental Protocols ◽  
True Protein ◽  
Two Hybrid

Protein interaction networks (PINs) are popular means to visualize the proteome. However, PIN datasets are known to be noisy, incomplete and biased by the experimental protocols used to detect protein interactions. This paper aims at understanding the connection between true protein interactions and the protein interaction datasets that have been obtained using the most popular experimental techniques, i.e. mass spectronomy and yeast two-hybrid. We start from the observation that the adjacency matrix of a PIN, i.e. the binary matrix which defines, for every pair of proteins in the network, whether or not there is a link, has a special form, that we call separable. This induces precise relationships between the moments of the degree distribution (i.e. the average number of links that a protein in the network has, its variance, etc.) and the number of short loops (i.e. triangles, squares, etc.) along the links of the network. These relationships provide powerful tools to test the reliability of datasets and hint at the underlying biological mechanism with which proteins and complexes recruit each other.

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 PINAWeb: A web-based tool for the comparison of Protein-Protein Interaction Networks Aligners

2021 ◽  
Author(s):  
A. Alcalá ◽  
G. Riera ◽  
I. García ◽  
R. Alberich ◽  
M. Llabrés
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Considerable Effort ◽  
Web Based ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
User Friendly

AbstractMotivationSeveral protein-protein interaction networks (PPIN) aligners have been developed during the last 15 years. One of their goals is to help the functional annotation of proteins and the prediction of protein-protein interactions. A correct aligner must preserve the network’s topology as well as the biological coherence. However, this is a trade-off that is hard to achieve. In addition, most aligners require a considerable effort to use in practice and many researchers must choose an aligner without the opportunity to previously compare the performance of different aligners.ResultsWe developed PINAWeb, a user-friendly web-based tool to obtain and compare the results produced by the aligners: AligNet, HubAlign, L-GRAAL, PINALOG and SPINAL. PPINs can be uploaded either from the STRING database or from a user database. The source code of PINAWeb is freely available on GitHub to enable researchers to add other aligners, network databases or alignment score metrics. In addition, PINAWeb provides a report with the analysis for every alignment in terms of topological and functional information scores, as well as the visualization of the alignments’ comparison (agreement/differences) when more than one aligner are considered.Availabilityhttps://bioinfo.uib.es/~recerca/PINAWeb

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.

scholarly journals Protein Interaction Networks and Disease: Highlights of the 3rd Challenges in Computational Biology Meeting

2017 ◽  
Vol 4 (1) ◽  
pp. 100056
Author(s):  
Gregorio Alanis-Lobato ◽  
Spyros Petrakis
Keyword(s):  
Computational Biology ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Current Status ◽  
Protein Interaction Data ◽  
Cellular Functions ◽  
Human Disorders

Cellular functions are managed by a complex network of protein interactions, the malfunction of which may derive in disease phenotypes. In spite of the incompleteness and noise present in our current protein interaction maps, computational biologists are making strenuous efforts to extract knowledge from these intricate networks and, through their integration with other types of biological data, expedite the development of novel and more effective treatments against human disorders. The 3rd Challenges in Computational Biology meeting revolved around the Protein Interaction Networks and Disease subject, bringing expert network biologists to the city of Mainz, Germany to debate the current status and limitations of protein interaction data and computational resources. This editorial outlines the meeting's background and programme, putting special emphasis on the extended abstracts of contributed talks collected in the present issue of Genomics and Computational Biology.

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