scholarly journals From pathways to networks: Connecting dots by establishing protein-protein interaction networks in signaling pathways using affinity purification and mass spectrometry

PROTEOMICS ◽  
2014 ◽  
Vol 15 (2-3) ◽  
pp. 188-202 ◽  
Author(s):  
Xu Li ◽  
Wenqi Wang ◽  
Junjie Chen
Keyword(s):  
Mass Spectrometry ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Affinity Purification ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

scholarly journals A Machine Learning Framework for Predicting Drug-drug Interactions

2021 ◽  
Author(s):  
Suyu Mei ◽  
Kun Zhang
Keyword(s):  
Drug Interactions ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Drug Target ◽  
Target Genes ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Model Complexity ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

Abstract Understanding drug-drug interaction is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods commonly integrate multiple heterogeneous data sources to increase model performance but result in a high model complexity. To elucidate the molecular mechanisms behind drug-drug interactions and reserve rational biological interpretability is a major concern in computational modeling. In this study, we propose a simple representation of drug target profiles to depict drug pairs, based on which an l2-regularized logistic regression model is built to predict drug-drug interactions. In addition, we develop several statistical metrics to measure the communication intensity, interaction efficacy and action range between two drugs in the context of human protein-protein interaction networks and signaling pathways. Cross validation and independent test show that the simple feature representation via drug target profiles is effective to predict drug-drug interactions and outperforms the existing data integration methods. Statistical results show that two drugs easily interact when they target common genes, or their target genes communicate with each other via short paths in protein-protein interaction networks or through cross-talks between signaling pathways. The unravelled mechanisms provide biological insights into potential pharmacological risks of known drug-drug interactions and drug target genes.

scholarly journals PADPIN: protein-protein interaction networks of angiogenesis, arteriogenesis, and inflammation in peripheral arterial disease

2015 ◽  
Vol 47 (8) ◽  
pp. 331-343 ◽  
Author(s):  
Liang-Hui Chu ◽  
Chaitanya G. Vijay ◽  
Brian H. Annex ◽  
Joel S. Bader ◽  
Aleksander S. Popel
Keyword(s):  
Peripheral Arterial Disease ◽  
Signaling Pathways ◽  
Protein Interaction ◽  
Arterial Disease ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Mouse Strains ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
Peripheral Arterial

Peripheral arterial disease (PAD) results from an obstruction of blood flow in the arteries other than the heart, most commonly the arteries that supply the legs. The complexity of the known signaling pathways involved in PAD, including various growth factor pathways and their cross talks, suggests that analyses of high-throughput experimental data could lead to a new level of understanding of the disease as well as novel and heretofore unanticipated potential targets. Such bioinformatic analyses have not been systematically performed for PAD. We constructed global protein-protein interaction networks of angiogenesis (Angiome), immune response (Immunome), and arteriogenesis (Arteriome) using our previously developed algorithm GeneHits. The term “PADPIN” refers to the angiome, immunome, and arteriome in PAD. Here we analyze four microarray gene expression datasets from ischemic and nonischemic gastrocnemius muscles at day 3 posthindlimb ischemia (HLI) in two genetically different C57BL/6 and BALB/c mouse strains that display differential susceptibility to HLI to identify potential targets and signaling pathways in angiogenesis, immune, and arteriogenesis networks. We hypothesize that identification of the differentially expressed genes in ischemic and nonischemic muscles between the strains that recovers better (C57BL/6) vs. the strain that recovers more poorly (BALB/c) will help for the prediction of target genes in PAD. Our bioinformatics analysis identified several genes that are differentially expressed between the two mouse strains with known functions in PAD including TLR4, THBS1, and PRKAA2 and several genes with unknown functions in PAD including EphA4, TSPAN7, SLC22A4, and EIF2a.

scholarly journals A Simple Cross-Linking/Mass Spectrometry Workflow to Study System-Wide Protein Interactions

2019 ◽  
Author(s):  
Michael Götze ◽  
Claudio Iacobucci ◽  
Christian Ihling ◽  
Andrea Sinz
Keyword(s):  
Mass Spectrometry ◽  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Cross Linking ◽  
Protein Protein Interaction ◽  
Cellular Environment ◽  
Protein Protein Interaction Networks

ABSTRACTWe present a cross-linking/mass spectrometry (XLMS) workflow for performing proteome-wide cross-linking analyses within one week. The workflow is based on the commercially available MS-cleavable cross-linker disuccinimidyl dibutyric urea (DSBU) and can be employed by every lab having access to a mass spectrometer with tandem MS capabilities. We provide an updated version 2.0 of the freeware software tool MeroX, available at www.StavroX.com, that allows conducting fully automated and reliable studies delivering insights into protein-protein interaction networks and protein conformations at the proteome level. We exemplify our optimized workflow for mapping protein-protein interaction networks in Drosophila melanogaster embryos on a system-wide level. From cross-linked Drosophila embryo extracts, we detected 18,037 cross-link spectrum matches corresponding to 5,129 unique cross-linked residues in biological triplicate experiments at 5% FDR (3,098 at 1% FDR). Among these, 1,237 interprotein cross-linking sites were identified that contain valuable information on protein-protein interactions. The remaining 3,892 intra-protein cross-links yield information on conformational changes of proteins in their cellular environment.

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