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Related Concept Videos

Phosphorylation01:02

Phosphorylation

53.8K
The addition or removal of phosphate groups from proteins is the most common chemical modification that regulates cellular processes. These modifications can affect the structure, activity, stability, and localization of proteins within cells as well as their interactions with other proteins.
During phosphorylation, protein kinases transfer the terminal phosphate group of ATP to specific amino acid side chains of substrate proteins. Serine, threonine, and tyrosine are the most commonly...
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Protein Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

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Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
Protein kinases
Many proteins in the cell are regulated by phosphorylation, the addition of a phosphate group. A family of enzymes called kinases...
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Protein Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Receptor Tyrosine Kinases01:26

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Receptor tyrosine kinases or RTKs are membrane-bound receptors that phosphorylate specific tyrosine on protein substrates. RTKs regulate cellular growth, differentiation, survival, and migration. They contain an extracellular ligand binding domain, a transmembrane domain, and a cytosolic tail with intrinsic kinase activity. Several extracellular signaling molecules activate RTKs in one or more ways and relay the signal downstream. Ligands such as platelet-derived growth factor (PDGF) or...
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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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Identification of Kinase-substrate Pairs Using High Throughput Screening
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CoPhosK: A method for comprehensive kinase substrate annotation using co-phosphorylation analysis.

Marzieh Ayati1,2, Danica Wiredja3, Daniela Schlatzer3

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH.

Plos Computational Biology
|February 28, 2019
PubMed
Summary
This summary is machine-generated.

CoPhosK predicts kinase-substrate associations (KSAs) using mass spectrometry data. This novel tool enhances KSA discovery by analyzing co-phosphorylation patterns, significantly expanding the known landscape of kinase-substrate interactions.

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Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Kinase-substrate associations (KSAs) are crucial for cellular signaling.
  • Identifying KSAs is challenging, especially for phosphopeptide substrates detected by mass spectrometry (MS).
  • Existing prediction methods often rely on static information, limiting their scope.

Purpose of the Study:

  • To develop CoPhosK, a computational tool for predicting KSAs from MS data.
  • To leverage dynamic signatures of kinase substrates through correlation analysis.
  • To infer KSAs for substrates lacking prior annotations.

Main Methods:

  • Utilized a Naïve Bayes framework incorporating known KSAs as priors.
  • Employed correlation analysis of phosphopeptide intensity data to identify collective dynamic signatures.
  • Benchmarked CoPhosK against existing methods using public MS data from cancer models.

Main Results:

  • CoPhosK significantly improves KSA prediction performance when combined with static information.
  • The tool provides reliable predictions for a substantial number of previously unannotated substrates.
  • CoPhosK triples the number of identifiable KSAs from experimental MS data.

Conclusions:

  • CoPhosK offers a comprehensive and reliable method for characterizing kinase-substrate interactions.
  • Co-phosphorylation analysis enhances KSA prediction beyond current limitations.
  • The tool expands the discoverable landscape of kinase-substrate interactions from MS data.