scholarly journals BFG-PCA: tools and resources that expand the potential for binary protein interaction discovery

2021 ◽  
Author(s):  
Daniel Evans-Yamamoto ◽  
François D Rouleau ◽  
Piyush Nanda ◽  
Koji Makanae ◽  
Yin Liu ◽  
...  
Keyword(s):  
Protein Interaction ◽  
High Sensitivity ◽  
High Specificity ◽  
Model Organisms ◽  
Protein Protein Interaction ◽  
Test Protein ◽  
Protein Fragment Complementation ◽  
Yeast Interactions ◽  
Protein Fragment Complementation Assay

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein-protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, Dihydrofolate Reductase Protein-Fragment Complementation Assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions of >11,000 protein pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

scholarly journals Perturbation of Interaction Networks for Application to Cancer Therapy

2007 ◽  
Vol 5 ◽  
pp. 117693510700500 ◽  
Author(s):  
Adrian P. Quayle ◽  
Asim S. Siddiqui ◽  
Steven J. M. Jones
Keyword(s):  
Protein Interaction ◽  
Experimental Testing ◽  
Cell Types ◽  
Drug Combinations ◽  
Interaction Networks ◽  
Data Sources ◽  
Model Organisms ◽  
Multiple Cancer ◽  
Protein Protein Interaction ◽  
Cancer Types

We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types. The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.

Photobiont selectivity in the epiphytic lichens Hypogymnia physodes and Lecanora conizaeoides

2007 ◽  
Vol 39 (2) ◽  
pp. 195-204 ◽  
Author(s):  
Markus HAUCK ◽  
Gert HELMS ◽  
Thomas FRIEDL
Keyword(s):  
High Sensitivity ◽  
High Specificity ◽  
Common Type ◽  
Lichen Species ◽  
Model Organisms ◽  
Epiphytic Lichens ◽  
Hypogymnia Physodes ◽  
Lichen Symbiosis ◽  
Key Factor ◽  
Nuclear Its

Abstract:In two lichen species, Hypogymnia physodes and Lecanora conizaeoides, often used as model organisms for pollution-sensitive and pollution-tolerant epiphytic lichens, respectively, the hypothesis was tested that the toxitolerance of the Trebouxia photobiont limits the tolerance of the entire lichen symbiosis. Being lecanoralean-trebouxioid associations, H. physodes and L. conizaeoides represent the most common type of lichens. Photobionts of both lichen species deriving from microhabitats with varying supply of S and heavy metals were identified using nuclear ITS nrDNA sequencing. The photobiont of L. conizaeoides was identified as T. simplex, whereas the photobiont of H. physodes belongs to an undescribed Trebouxia species, related to T. jamesii subsp. angustilobata and provisionally named as T. hypogymniae Hauck & Friedl ined. Since T. hypogymniae ined. is also known from Lecidea silacea, which is characteristic of rock and slag with high heavy metal content, a high sensitivity of this alga to pollutants is unlikely to be a key factor for the relatively low toxitolerance of H. physodes. Furthermore, the photobiont cannot be crucial for the extremely high toxitolerance of L. conizaeoides, as T. simplex is also known from pollution-sensitive lichens of the fruticose genus Pseudevernia. These findings suggest that the photobiont is not generally a key factor determining pollution sensitivity in the most common type of lichen symbiosis. The high specificity for T. simplex in L. conizaeoides in existing populations of L. conizaeoides suggest that already established thalli could be a source of photobiont cells for re-lichenization.

scholarly journals Sensitive detection of protein ubiquitylation using a protein-fragment complementation assay

2019 ◽  
Author(s):  
Marie Le Boulch ◽  
Audrey Brossard ◽  
Gaëlle Le Dez ◽  
Gwenaël Rabut
Keyword(s):  
Protein Modification ◽  
Cultured Cells ◽  
Model Organisms ◽  
Protein Fragment ◽  
Ubiquitin Chain ◽  
Complementation Assay ◽  
A Genome ◽  
Protein Fragment Complementation ◽  
Fragment Complementation ◽  
Protein Fragment Complementation Assay

ABSTRACTUbiquitylation is a reversible post-translational protein modification that regulates a multitude of cellular processes. Detection of ubiquitylated proteins is often challenging, because of their low abundance. Here, we present NUbiCA, a sensitive protein-fragment complementation assay to facilitate the monitoring of ubiquitylation events in cultured cells and model organisms. Using yeast as a model system, we demonstrate that NUbiCA enables to accurately monitor mono- and poly-ubiquitylation of proteins expressed at endogenous levels. We also show that it can be applied to decipher ubiquitin chain linkages. We used NUbiCA to investigate the ubiquitylation of the low abundance centromeric histone Cse4, and found that it is ubiquitylated during S-phase. Finally, we assembled a genome wide collection of yeast strains ready to investigate the ubiquitylation of proteins with this new assay. This resource will facilitate the analysis of local or transient ubiquitylation events that are difficult to detect with current methods.Summary statementWe describe a sensitive protein-fragment complementation assay to facilitate the monitoring of ubiquitylation events that take place in cultured cells or model organisms.

scholarly journals Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Peng Liu ◽  
Lei Yang ◽  
Daming Shi ◽  
Xianglong Tang
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Statistical Tests ◽  
Interaction Network ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Small Parts

A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptivek-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

scholarly journals Building Protein-Protein Interaction Networks with Proteomics and Informatics Tools

2011 ◽  
Vol 286 (27) ◽  
pp. 23645-23651 ◽  
Author(s):  
Mihaela E. Sardiu ◽  
Michael P. Washburn
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Model Organisms ◽  
Biological Research ◽  
Data Sets ◽  
Proteomics Data ◽  
Protein Protein Interaction ◽  
Systematic Identification

The systematic characterization of the whole interactomes of different model organisms has revealed that the eukaryotic proteome is highly interconnected. Therefore, biological research is progressively shifting away from classical approaches that focus only on a few proteins toward whole protein interaction networks to describe the relationship of proteins in biological processes. In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks. In particular, we will focus on the recent development of protein interaction networks derived from quantitative proteomics data sets.

scholarly journals NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Stefano Perna ◽  
Pietro Pinoli ◽  
Stefano Ceri ◽  
Limsoon Wong
Keyword(s):  
Protein Interaction ◽  
Positional Information ◽  
Ppi Network ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Physical Interactions ◽  
Novel Interactions ◽  
Factor Interactions

Abstract Background Inferring the mechanisms that drive transcriptional regulation is of great interest to biologists. Generally, methods that predict physical interactions between transcription factors (TFs) based on positional information of their binding sites (e.g. chromatin immunoprecipitation followed by sequencing (ChIP-Seq) experiments) cannot distinguish between different kinds of interaction at the same binding spots, such as co-operation and competition. Results In this work, we present the Network-Augmented Transcriptional Interaction and Coregulation Analyser (NAUTICA), which employs information from protein-protein interaction (PPI) networks to assign TF-TF interaction candidates to one of three classes: competition, co-operation and non-interactions. NAUTICA filters available PPI network edges and fits a prediction model based on the number of shared partners in the PPI network between two candidate interactors. Conclusions NAUTICA improves on existing positional information-based TF-TF interaction prediction results, demonstrating how PPI information can improve the quality of TF interaction prediction. NAUTICA predictions - both co-operations and competitions - are supported by literature investigation, providing evidence on its capability of providing novel interactions of both kinds. Reviewers This article was reviewed by Zoltán Hegedüs and Endre Barta.

Protein–protein interaction network with machine learning models and multiomics data reveal potential neurodegenerative disease-related proteins

2020 ◽  
Vol 29 (8) ◽  
pp. 1378-1387 ◽  
Author(s):  
Xinjian Yu ◽  
Siqi Lai ◽  
Hongjun Chen ◽  
Ming Chen
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disease ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Computational Study ◽  
Interaction Network ◽  
Model Organisms ◽  
Protein Protein Interaction ◽  
Related Proteins ◽  
Protein Protein Interaction Network

Abstract Research of protein–protein interaction in several model organisms is accumulating since the development of high-throughput experimental technologies and computational methods. The protein–protein interaction network (PPIN) is able to examine biological processes in a systematic manner and has already been used to predict potential disease-related proteins or drug targets. Based on the topological characteristics of the PPIN, we investigated the application of the random forest classification algorithm to predict proteins that may cause neurodegenerative disease, a set of pathological changes featured by protein malfunction. By integrating multiomics data, we further showed the validity of our machine learning model and narrowed down the prediction results to several hub proteins that play essential roles in the PPIN. The novel insights into neurodegeneration pathogenesis brought by this computational study can indicate promising directions for future experimental research.

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