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Related Concept Videos

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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MAPK Signaling Cascades01:07

MAPK Signaling Cascades

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Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
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Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

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Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
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Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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cAMP-dependent Protein Kinase Pathways01:25

cAMP-dependent Protein Kinase Pathways

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Cyclic Adenosine Monophosphate (cAMP) is an essential second messenger that activates protein kinase A (PKA) and regulates various biological processes. A single epinephrine molecule binds to GPCR and activates several heterotrimeric G proteins, each stimulating multiple adenylyl cyclase, amplifying the signal, and synthesizing large numbers of cAMP molecules. Small changes in cAMP concentration affect PKA activity. The binding of four cAMP molecules induces a conformational change in PKA,...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph

Mariah V Salcedo1, Nathan Gravel2, Abbas Keshavarzi3

  • 1Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States of America.

Peerj
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces RegPattern2Vec, a novel bioinformatics tool using knowledge graphs to predict functions for understudied protein kinases. The method enhances understanding of kinase roles in biological pathways, aiding future research.

Keywords:
ClassificationData integrationDrug discoveryEvolutionIlluminating Druggable Genome (IDG)Link predictionOntologiesPathway predictionRandom walkSignaling networks

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Area of Science:

  • Bioinformatics and Computational Biology
  • Molecular Biology and Genomics
  • Systems Biology

Background:

  • The human genome encodes 534 protein kinases, a significant class of drug targets, including many understudied 'dark' kinases.
  • Accurately predicting the functions of these understudied kinases is a critical challenge in bioinformatics.
  • Existing methods struggle to effectively leverage complex biological data for functional prediction.

Purpose of the Study:

  • To develop a novel graph mining approach for predicting protein and pathway associations for understudied kinases.
  • To introduce RegPattern2Vec, a scalable graph embedding method utilizing regular pattern constrained random walks.
  • To improve the accuracy and efficiency of functional predictions for kinases by integrating diverse biological data.

Main Methods:

  • Employed a graph mining approach using knowledge graphs (KGs) to capture evolutionary and functional context.
  • Developed RegPattern2Vec, a graph embedding technique using regular pattern constrained random walks for diverse node context sampling.
  • Integrated data on kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions into a kinase-centric KG.

Main Results:

  • RegPattern2Vec demonstrated improved accuracy and efficiency compared to other random walk-based graph embedding methods.
  • Model predictions showed overlap with pathway enrichment data derived from experimentally validated Protein-Protein Interaction (PPI) data.
  • Generated high-confidence pathway predictions for 34 dark kinases, with case studies illustrating biological interpretation via meta-path analysis.

Conclusions:

  • RegPattern2Vec effectively samples multiple node types for link prediction on biological knowledge graphs.
  • The predicted associations between understudied kinases, pseudokinases, and pathways provide a basis for hypothesis generation.
  • This approach offers a valuable tool for exploring the functional landscape of understudied kinases and advancing biological discovery.