scholarly journals The strength of protein-protein interactions controls the information capacity and dynamical response of signaling networks

2018 ◽  
Author(s):  
Ching-Hao Wang ◽  
Pankaj Mehta ◽  
Caleb J. Bashor
Keyword(s):  
Information Transmission ◽  
Protein Interactions ◽  
Weak Interactions ◽  
Interaction Strength ◽  
Signaling Network ◽  
Biochemical Networks ◽  
Information Capacity ◽  
Signaling Networks ◽  
Dynamical Response ◽  
Protein Protein Interactions

Eukaryotic cells transmit information by signaling through complex networks of interacting proteins. Here we develop a theoretical and computational framework that relates the biophysics of protein-protein interactions (PPIs) within a signaling network to its information processing properties. To do so, we generalize statistical physics-inspired models for protein binding to account for interactions that depend on post-translational state (e.g. phosphorylation). By combining these models with information-theoretic methods, we find that PPIs are a key determinant of information transmission within a signaling network, with weak interactions giving rise to “noise” that diminishes information transmission. While noise can be mitigated by increasing interaction strength, the accompanying increase in transmission comes at the expense of a slower dynamical response. This suggests that the biophysics of signaling protein interactions give rise to a fundamental “speed-information” trade-off. Surprisingly, we find that cross-talk between pathways in complex signaling networks do not significantly alter information capacity–an observation that may partially explain the promiscuity and ubiquity of weak PPIs in heavily interconnected networks. We conclude by showing how our framework can be used to design synthetic biochemical networks that maximize information transmission, a procedure we dub “InfoMax” design.

scholarly journals Application of TurboID-mediated proximity labeling for mapping a GSK3 kinase signaling network in Arabidopsis

2019 ◽  
Author(s):  
Tae-Wuk Kim ◽  
Chan Ho Park ◽  
Chuan-Chih Hsu ◽  
Jia-Ying Zhu ◽  
Yuchun Hsiao ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Transgenic Arabidopsis ◽  
Affinity Purification ◽  
High Specificity ◽  
Signaling Network ◽  
Signaling Networks ◽  
Protein Protein Interactions ◽  
Quantitative Mass Spectrometry ◽  
Cellular Regulation ◽  
Biotin Ligase

AbstractTransient protein-protein interactions (PPIs), such as those between posttranslational modifying enzymes and their substrates, play key roles in cellular regulation, but are difficult to identify. Here we demonstrate the application of enzyme-catalyzed proximity labeling (PL), using the engineered promiscuous biotin ligase TurboID, as a sensitive method for characterizing PPIs in signaling networks. We show that TurboID fused with the GSK3-like kinase BIN2 or a PP2A phosphatase biotinylates their known substrate, the BZR1 transcription factor, with high specificity and efficiency. We optimized the protocol of biotin labeling and affinity purification in transgenic Arabidopsis expressing a BIN2-TurboID fusion protein. Subsequent quantitative mass spectrometry (MS) analysis identified about three hundred proteins biotinylated by BIN2-TurboID more efficiently than the YFP-TurboID control. These include a significant subset of previously proven BIN2 interactors and a large number of new BIN2-proximal proteins that uncover a broad BIN2 signaling network. Our study illustrates that PL-MS using TurboID is a powerful tool for mapping signaling networks, and reveals broad roles of BIN2 kinase in cellular signaling and regulation in plants.Impact StatementTurboID-mediated proximity labeling is a powerful tool for protein interactomics in plants.

scholarly journals Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mayumi Kamada ◽  
Yusuke Sakuma ◽  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interactions ◽  
Interaction Strength ◽  
Feature Space ◽  
Relevance Vector Machine ◽  
Protein Domain ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Living Organisms ◽  
Domain Information

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.

scholarly journals Targeting Difficult Protein-Protein Interactions with Plain and General Computational Approaches

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2256 ◽  
Author(s):  
Mariarosaria Ferraro ◽  
Giorgio Colombo
Keyword(s):  
Protein Interactions ◽  
Signaling Networks ◽  
Protein Protein Interactions ◽  
Structural Dynamic ◽  
Antibody Recognition ◽  
Disease States ◽  
Interfacial Areas ◽  
Interacting Surfaces ◽  
Cell Signaling Networks ◽  
Antigen Antibody

Investigating protein-protein interactions (PPIs) holds great potential for therapeutic applications, since they mediate intricate cell signaling networks in physiological and disease states. However, their complex and multifaceted nature poses a major challenge for biochemistry and medicinal chemistry, thereby limiting the druggability of biological partners participating in PPIs. Molecular Dynamics (MD) provides a solid framework to study the reciprocal shaping of proteins’ interacting surfaces. Here, we review successful applications of MD-based methods developed in our group to predict interfacial areas involved in PPIs of pharmaceutical interest. We report two interesting examples of how structural, dynamic and energetic information can be combined into efficient strategies which, complemented by experiments, can lead to the design of new small molecules with promising activities against cancer and infections. Our advances in targeting key PPIs in angiogenic pathways and antigen-antibody recognition events will be discussed for their role in drug discovery and chemical biology.

scholarly journals Analysis of signaling networks distributed over intracellular compartments based on protein-protein interactions

BMC Genomics ◽  
2014 ◽  
Vol 15 (Suppl 12) ◽  
pp. S7 ◽  
Author(s):  
Olga Popik ◽  
Olga Saik ◽  
Evgeny Petrovskiy ◽  
Björn Sommer ◽  
Ralf Hofestädt ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Signaling Networks ◽  
Protein Protein Interactions ◽  
Intracellular Compartments

scholarly journals Inducible Yeast Two-Hybrid with Quantitative Measures

2021 ◽  
Author(s):  
Jesus Hernandez ◽  
Kevin D. Ross ◽  
Bruce A. Hamilton
Keyword(s):  
Protein Interactions ◽  
Interaction Strength ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
Growth Assay ◽  
Adh1 Promoter ◽  
Multiwell Plates ◽  
Two Hybrid ◽  
Cup1 Promoter

The yeast two-hybrid (Y2H) assay has long been used to identify new protein-protein interaction pairs and to compare relative interaction strengths. Traditional Y2H formats may be limited, however, by use of constitutive strong promoters if expressed proteins have toxic effects or post-transcriptional expression differences in yeast among a comparison group. As a step toward more quantitative Y2H assays, we modified a common vector to use an inducible CUP1 promoter, which showed quantitative induction of several "bait" proteins with increasing copper concentration. Using mouse Nxf1 (homologous to yeast Mex67p) as a model bait, copper titration achieved levels that bracket levels obtained with the constitutive ADH1 promoter. Using a liquid growth assay for an auxotrophic reporter in multiwell plates allowed log-phase growth rate to be used as a measure of interaction strength. These data demonstrate the potential for quantitative comparisons of protein-protein interactions using the Y2H system.

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.

scholarly journals Detection of protein–protein interactions at the septin collar inSaccharomyces cerevisiaeusing a tripartite split-GFP system

2016 ◽  
Vol 27 (17) ◽  
pp. 2708-2725 ◽  
Author(s):  
Gregory C. Finnigan ◽  
Angela Duvalyan ◽  
Elizabeth N. Liao ◽  
Aspram Sargsyan ◽  
Jeremy Thorner
Keyword(s):  
Protein Interactions ◽  
Weak Interactions ◽  
Glycolytic Enzyme ◽  
Fluorescence Signal ◽  
Physical Interaction ◽  
Protein Protein Interactions ◽  
New Information ◽  
Bud Neck ◽  
Molecular Ruler

Various methods can provide a readout of the physical interaction between two biomolecules. A recently described tripartite split-GFP system has the potential to report by direct visualization via a fluorescence signal the intimate association of minimally tagged proteins expressed at their endogenous level in their native cellular milieu and can capture transient or weak interactions. Here we document the utility of this tripartite split-GFP system to assess in living cells protein–protein interactions in a dynamic cytoskeletal structure—the septin collar at the yeast bud neck. We show, first, that for septin–septin interactions, this method yields a robust signal whose strength reflects the known spacing between the subunits in septin filaments and thus serves as a “molecular ruler.” Second, the method yields little or no spurious signal even with highly abundant cytosolic proteins readily accessible to the bud neck (including molecular chaperone Hsp82 and glycolytic enzyme Pgk1). Third, using two proteins (Bni5 and Hsl1) that have been shown by other means to bind directly to septins at the bud neck in vivo, we validate that the tripartite split-GFP method yields the same conclusions and further insights about specificity. Finally, we demonstrate the capacity of this approach to uncover additional new information by examining whether three other proteins reported to localize to the bud neck (Nis1, Bud4, and Hof1) are able to interact physically with any of the subunits in the septin collar and, if so, with which ones.

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