Molecular Biology of Protein-Protein Interactions for Computer Scientists

2009 ◽  
pp. 1-13 ◽  
Author(s):  
Christian Schönbach
Keyword(s):  
Molecular Biology ◽  
Protein Interactions ◽  
Wnt Signaling Pathway ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Detection Technology ◽  
Challenges And Opportunities ◽  
Computer Scientists ◽  
Assay Methods

Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose completeness appears to depend on the extent of data derived from different PPI assay methods and the complexity of the studied organism. Despite the partial nature of human PPI networks, computational data integration and analyses helped to elucidate new interactions and disease pathways. The success of computational analyses considerably depends on PPI data understanding. Exploration of the data and verification of their quality requires basic knowledge of the molecular biology of PPIs and familiarity with the assay methods used to detect PPIs. Both topics are reviewed in this chapter. After introducing various types of PPIs the principles of selected PPI assays are explained and their limitations discussed. Case studies of the Wnt signaling pathway and splice regulation demonstrate some of the challenges and opportunities that arise from assaying and analyzing PPIs. The chapter is concluded with an extrapolation to human systems biology that offers a glimpse into the future of PPI networks.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals MIPPIE: the mouse integrated protein–protein interaction reference

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Jannik S Möllmann ◽  
Martin H Schaefer ◽  
Miguel A Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Model Organism ◽  
Web Interface ◽  
Protein Protein Interactions ◽  
Environmental Signals ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
User Friendly

Abstract Cells operate and react to environmental signals thanks to a complex network of protein–protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein–Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.

scholarly journals CellMap visualizes protein-protein interactions and subcellular localization

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1824 ◽  
Author(s):  
Christian Dallago ◽  
Tatyana Goldberg ◽  
Miguel Angel Andrade-Navarro ◽  
Gregorio Alanis-Lobato ◽  
Burkhard Rost
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Cellular Component ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
A Cell ◽  
Areas Of Interest

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers.

scholarly journals CellMap visualizes protein-protein interactions and subcellular localization

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1824 ◽  
Author(s):  
Christian Dallago ◽  
Tatyana Goldberg ◽  
Miguel Angel Andrade-Navarro ◽  
Gregorio Alanis-Lobato ◽  
Burkhard Rost
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Cellular Component ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
A Cell ◽  
Areas Of Interest

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers.

scholarly journals Predict protein-protein interactions from protein primary sequences: using wavelet transform combined with stacking algorithm

2017 ◽  
Author(s):  
Pin-San Xu ◽  
Jun Luo ◽  
Tong-Yi Dou
Keyword(s):  
Wavelet Transform ◽  
Protein Interactions ◽  
Prediction Accuracy ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Data Set ◽  
Virtual Technology ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Protein Functions

Most biological processes within a cell are carried out by protein-protein interaction (PPI) networks, or so called interactomics. Therefore, identification of PPIs is crucial to elucidating protein functions and further understanding of various cellular biological processes. Currently, a series of high-throughput experimental technologies for detect PPIs have been presented. However, the time-consuming and labor-driven characteristics of these methods forced people to turn to virtual technology for PPIs prediction. Herein, we developed a new predictor which uses stacking algorithm with information extraction by wavelet transform. When applied on the Saccharomyces cerevisiae PPI dataset, the proposed method got a prediction accuracy of 83.35% with sensitivity of 92.95% at the specificity of 65.41%. An independent data set of 2726 Helicobacter pylori PPIs was also used to evaluate this prediction model, and the prediction accuracy is 80.39%, which is better than that of most existing methods.

scholarly journals Transient protein-protein interactions perturb E. coli metabolome and cause gene dosage toxicity

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sanchari Bhattacharyya ◽  
Shimon Bershtein ◽  
Jin Yan ◽  
Tijda Argun ◽  
Amy I Gilson ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Gene Dosage ◽  
Protein Protein Interactions ◽  
Molecular Systems ◽  
E Coli ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Proteome Level ◽  
Metabolic Imbalance

Gene dosage toxicity (GDT) is an important factor that determines optimal levels of protein abundances, yet its molecular underpinnings remain unknown. Here, we demonstrate that overexpression of DHFR in E. coli causes a toxic metabolic imbalance triggered by interactions with several functionally related enzymes. Though deleterious in the overexpression regime, surprisingly, these interactions are beneficial at physiological concentrations, implying their functional significance in vivo. Moreover, we found that overexpression of orthologous DHFR proteins had minimal effect on all levels of cellular organization – molecular, systems, and phenotypic, in sharp contrast to E. coli DHFR. Dramatic difference of GDT between ‘E. coli’s self’ and ‘foreign’ proteins suggests the crucial role of evolutionary selection in shaping protein-protein interaction (PPI) networks at the whole proteome level. This study shows how protein overexpression perturbs a dynamic metabolon of weak yet potentially functional PPI, with consequences for the metabolic state of cells and their fitness.

scholarly journals Predict protein-protein interactions from protein primary sequences: using wavelet transform combined with stacking algorithm

2017 ◽  
Author(s):  
Pin-San Xu ◽  
Jun Luo ◽  
Tong-Yi Dou
Keyword(s):  
Wavelet Transform ◽  
Protein Interactions ◽  
Prediction Accuracy ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Data Set ◽  
Virtual Technology ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Protein Functions

Most biological processes within a cell are carried out by protein-protein interaction (PPI) networks, or so called interactomics. Therefore, identification of PPIs is crucial to elucidating protein functions and further understanding of various cellular biological processes. Currently, a series of high-throughput experimental technologies for detect PPIs have been presented. However, the time-consuming and labor-driven characteristics of these methods forced people to turn to virtual technology for PPIs prediction. Herein, we developed a new predictor which uses stacking algorithm with information extraction by wavelet transform. When applied on the Saccharomyces cerevisiae PPI dataset, the proposed method got a prediction accuracy of 83.35% with sensitivity of 92.95% at the specificity of 65.41%. An independent data set of 2726 Helicobacter pylori PPIs was also used to evaluate this prediction model, and the prediction accuracy is 80.39%, which is better than that of most existing methods.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals Universal Screening Methods and Applications of ThermoFluor®

2006 ◽  
Vol 11 (7) ◽  
pp. 854-863 ◽  
Author(s):  
Maxwell D. Cummings ◽  
Michael A. Farnum ◽  
Marina I. Nelen
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Unfolding ◽  
Direct Detection ◽  
Functional Characterization ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Bacterial Enzyme ◽  
Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

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