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The Proteasome02:18

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Eukaryotic cells can degrade proteins through several pathways. One of the most important amongst these is the ubiquitin-proteasome pathway. It helps the cell eliminate the misfolded, damaged, or unwarranted cytoplasmic proteins in a highly specific manner.
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The ubiquitin-proteasome pathway is a well-known mechanism utilized by eukaryotic cells to remove cytoplasmic proteins that are misfolded, damaged, or no longer needed. In this pathway, the protein that needs to be eliminated undergoes a process called ubiquitination, where a chain of ubiquitin molecules is attached to the 48th lysine residue of the target protein. This ubiquitin modification helps the proteasome distinguish between a target protein and a healthy protein.
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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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Predicting Proteolysis in Complex Proteomes Using Deep Learning.

Matiss Ozols1, Alexander Eckersley1, Christopher I Platt1

  • 1Division of Cell Matrix Biology & Regenerative Medicine, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester M13 9PT, UK.

International Journal of Molecular Sciences
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study developed bioinformatics tools to predict protein susceptibility to tissue proteases and reactive oxygen species (ROS). New models accurately identified cleavage sites in skin proteins, aiding proteolysis research.

Keywords:
agingbiomarkersdeep-learningdegradomicsextracellular matrixmachine learningproteaseskin

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Proteomics

Background:

  • Proteolysis, mediated by proteases and reactive oxygen species (ROS), is crucial for tissue remodeling.
  • Predicting protein susceptibility to ROS is feasible via amino acid composition, but protease susceptibility prediction remains challenging.
  • Existing tools like PROSPER and DeepCleave have limitations in predicting endogenous protease cleavage sites.

Purpose of the Study:

  • To develop bioinformatics tools for predicting protease cleavage sites and comparing protein vulnerabilities to protease- and ROS-mediated proteolysis.
  • To evaluate existing protease cleavage site prediction models (PROSPER, DeepCleave) against experimental data for MMP9.
  • To create a novel web application, the Manchester proteome susceptibility calculator (MPSC), integrating new models.

Main Methods:

  • Experimental evaluation of PROSPER and DeepCleave using MMP9 cleavage sites in purified proteins and ECM proteome.
  • Development of deep bidirectional recurrent neural network (BRNN) models for predicting cleavage sites of 14 tissue proteases.
  • Integration of BRNN predictions and amino acid composition analysis (for ROS susceptibility) into the MPSC web app.

Main Results:

  • BRNN models demonstrated superior performance in predicting cleavage sites within native dermal ECM proteins compared to PROSPER and DeepCleave.
  • Application of MPSC to the skin proteome indicated fibrillar collagens are primarily susceptible to protease-mediated proteolysis, unlike the elastic fiber network.
  • Identified novel putative targets for oxidative damage (dermatopontin, fibulins, defensins) and protease action (laminins, nidogen).

Conclusions:

  • The developed BRNN models and MPSC offer improved prediction of protein susceptibility to proteolysis.
  • MPSC provides a valuable tool for identifying potential proteolysis targets across various tissues and disease states.
  • Findings highlight differential susceptibility of skin ECM components to protease versus ROS-mediated degradation.