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Related Concept Videos

The Proteasome01:13

The Proteasome

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Eukaryotic cells can degrade proteins through several pathways. One of the most important among 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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A protein sequence-based deep transfer learning framework for identifying human proteome-wide

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Researchers developed TransDSI, a new method to predict deubiquitinase-substrate interactions (DSIs) using protein sequences. This approach identifies critical protein regions and aids in discovering potential drug targets for cancer therapy.

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Bioinformatics

Background:

  • Protein ubiquitination is crucial for cellular processes, regulated by ubiquitin ligases (E3s) and deubiquitinases (DUBs).
  • Deubiquitinase-substrate interactions (DSIs) are less understood than E3-substrate interactions, hindering a complete view of ubiquitination regulation.

Purpose of the Study:

  • To develop an ab initio method for predicting unknown DSIs using only protein sequence information.
  • To identify critical protein regions involved in DSIs and provide functional insights.
  • To offer potential avenues for drug discovery in cancer therapeutics.

Main Methods:

  • Introduced TransDSI, a protein sequence-based method leveraging proteome-scale evolutionary information.
  • Integrated an explainable module to pinpoint critical protein regions for DSI prediction.
  • Validated predictions using cross-validation, independent testing, and wet lab experiments.

Main Results:

  • TransDSI demonstrated superior performance compared to existing machine learning strategies.
  • Successfully predicted DUBs (USP11, USP20) for FOXP3 and substrates (AR, p53) for USP22, with experimental validation.
  • Identified novel regulatory DSIs, offering new functional perspectives on proteins.

Conclusions:

  • TransDSI is an effective tool for predicting DSIs, even with limited training data.
  • The method enhances understanding of DUB functions and protein regulation.
  • Findings support TransDSI's utility in identifying potential cancer drug targets and guiding precision medicine.