scholarly journals A systems chemoproteomic analysis of acyl-CoA/protein interaction networks

2019 ◽  
Author(s):  
Michaella J. Levy ◽  
David C. Montgomery ◽  
Mihaela E. Sardiu ◽  
Sarah E. Bergholtz ◽  
Kellie D. Nance ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Proteins ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
High Confidence ◽  
Acetyl Coa ◽  
High Throughput Analysis ◽  
Response Data ◽  
Novel Protein

SummaryAcyl-CoA/protein interactions are required for many functions essential to life including membrane synthesis, oxidative metabolism, and macromolecular acetylation. However, despite their importance, the global scope and selectivity of these protein-metabolite interactions remains undefined. Here we describe the development of CATNIP (CoA/AcetylTraNsferase Interaction Profiling), a chemoproteomic platform for the high-throughput analysis of acyl-CoA/protein interactions in endogenous proteomes. First, we apply CATNIP to identify acetyl-CoA-binding proteins through unbiased clustering of competitive dose-response data. Next, we use this method to profile diverse protein-CoA metabolite interactions, identifying biological processes susceptible to altered acetyl-CoA levels. Finally, we apply systems-level analyses to assess the features of novel protein networks that may interact with acyl-CoAs, and demonstrate a strategy for high-confidence proteomic annotation of acetyl-CoA binding proteins. Overall our studies illustrate the power of integrating chemoproteomics and systems biology, and provide a resource for understanding the roles of acyl-CoA metabolites in biology and disease.

DISCOVERING PROTEIN–PROTEIN INTERACTIONS

2004 ◽  
Vol 01 (04) ◽  
pp. 711-741 ◽  
Author(s):  
SEE-KIONG NG ◽  
SOON-HENG TAN
Keyword(s):  
Protein Interactions ◽  
Experimental Methods ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Comprehensive Description ◽  
New Genes ◽  
Genomics And Proteomics ◽  
Living Organisms

The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein–protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein–protein interactions, covering both the conventional experimental methods and new computational approaches.

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.

An Integrated Prediction Method for Identifying Protein-Protein Interactions

2020 ◽  
Vol 17 (4) ◽  
pp. 271-286
Author(s):  
Chang Xu ◽  
Limin Jiang ◽  
Zehua Zhang ◽  
Xuyao Yu ◽  
Renhai Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Feature Vector ◽  
Prediction Method ◽  
Feature Representation ◽  
Protein Interaction Networks ◽  
Learning Approach ◽  
Biological Processes ◽  
Integrated Learning ◽  
Protein Protein Interactions ◽  
Training Process

Background: Protein-Protein Interactions (PPIs) play a key role in various biological processes. Many methods have been developed to predict protein-protein interactions and protein interaction networks. However, many existing applications are limited, because of relying on a large number of homology proteins and interaction marks. Methods: In this paper, we propose a novel integrated learning approach (RF-Ada-DF) with the sequence-based feature representation, for identifying protein-protein interactions. Our method firstly constructs a sequence-based feature vector to represent each pair of proteins, viaMultivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC). Then, we feed the 638- dimentional features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Meanwhile, we also employ double fault detection in order to suppress over-adaptation during the training process. Results: To evaluate the performance of our method, we conduct several comprehensive tests for PPIs prediction. On the H. pyloridataset, our method achieves 88.16% accuracy and 87.68% sensitivity, the accuracy of our method is increased by 0.57%. On the S. cerevisiaedataset, our method achieves 95.77% accuracy and 93.36% sensitivity, the accuracy of our method is increased by 0.76%. On the Humandataset, our method achieves 98.16% accuracy and 96.80% sensitivity, the accuracy of our method is increased by 0.6%. Experiments show that our method achieves better results than other outstanding methods for sequence-based PPIs prediction. The datasets and codes are available at https://github.com/guofei-tju/RF-Ada-DF.git.

G4-PROTAC: Targeted degradation of a G-quadruplex binding protein

2021 ◽  
Author(s):  
Kiran Patil ◽  
Danielle Chin ◽  
Hui Ling Seah ◽  
Qi Shi ◽  
Kah Wai Lim ◽  
...  
Keyword(s):  
Binding Proteins ◽  
Binding Protein ◽  
Interaction Networks ◽  
Biological Processes ◽  
G Quadruplex

G-quadruplex (G4)-binding proteins regulate important biological processes, but their interaction networks are poorly understood. We report the first use of G4 as warhead of a proteolysis-targeting chimera (G4-PROTAC) for targeted...

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1969
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1969 ◽  
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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 Glycosaminoglycan–Protein Interactions: The First Draft of the Glycosaminoglycan Interactome

2020 ◽  
pp. 002215542094640 ◽  
Author(s):  
Sylvain D. Vallet ◽  
Olivier Clerc ◽  
Sylvie Ricard-Blum
Keyword(s):  
Extracellular Matrix ◽  
Protein Interactions ◽  
Dermatan Sulfate ◽  
Protein Complexes ◽  
Keratan Sulfate ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Matrix Proteins ◽  
Matrix Assembly ◽  
Gag Protein

The six mammalian glycosaminoglycans (GAGs), chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate, are linear polysaccharides. Except for hyaluronan, they are sulfated to various extent, and covalently attached to proteins to form proteoglycans. GAGs interact with growth factors, morphogens, chemokines, extracellular matrix proteins and their bioactive fragments, receptors, lipoproteins, and pathogens. These interactions mediate their functions, from embryonic development to extracellular matrix assembly and regulation of cell signaling in various physiological and pathological contexts such as angiogenesis, cancer, neurodegenerative diseases, and infections. We give an overview of GAG–protein interactions (i.e., specificity and chemical features of GAG- and protein-binding sequences), and review the available GAG–protein interaction networks. We also provide the first comprehensive draft of the GAG interactome composed of 832 biomolecules (827 proteins and five GAGs) and 932 protein–GAG interactions. This network is a scaffold, which in the future should integrate structures of GAG–protein complexes, quantitative data of the abundance of GAGs in tissues to build tissue-specific interactomes, and GAG interactions with metal ions such as calcium, which plays a major role in the assembly of the extracellular matrix and its interactions with cells. This contextualized interactome will be useful to identify druggable GAG–protein interactions for therapeutic purpose:

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.

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