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Related Concept Videos

Subcellular Fractionation01:32

Subcellular Fractionation

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The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
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Eukaryotic cells have different membrane-bound organelles with distinct protein requirements. The process by which proteins are targeted to a specific organelle is called protein sorting.
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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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Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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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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Predicting human protein subcellular localization by heterogeneous and comprehensive approaches.

Chi-Hua Tung1, Chi-Wei Chen2, Han-Hao Sun2

  • 1Department of Bioinformatics, Chung-Hua University, Hsinchu, Taiwan.

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Summary

We developed REALoc, a new system to predict protein subcellular localization for drug development. REALoc accurately identifies protein locations in human cells, outperforming existing prediction tools.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding protein subcellular localization is crucial for drug development and protein function studies.
  • Accurate prediction of protein location aids in target identification and understanding cellular mechanisms.

Purpose of the Study:

  • To develop and validate a novel computational system, REALoc, for predicting human protein subcellular localization.
  • To improve the accuracy and efficiency of predicting localization for both single and multiple proteins.

Main Methods:

  • REALoc integrates two heterogeneous frameworks using one-to-one and many-to-many machine learning approaches.
  • The system utilizes sequence-based features (amino acid composition, surface accessibility, etc.) and Gene Ontology function-based features.
  • Predictions cover six major subcellular compartments: cell membrane, cytoplasm, endoplasmic reticulum/Golgi, mitochondrion, nucleus, and extracellular.

Main Results:

  • REALoc achieved a 75.3% absolute true success rate in five-fold cross-validation.
  • The system demonstrated a 57.1% absolute true success rate on an independent test dataset.
  • REALoc's performance exceeded six other prediction systems by over 10%.

Conclusions:

  • REALoc provides a robust and accurate method for predicting human protein subcellular localization.
  • The system's high accuracy and performance make it a valuable tool for researchers in drug development and functional proteomics.
  • REALoc is freely accessible online, facilitating its adoption in biological research.