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Subcellular Fractionation

Subcellular Fractionation

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
Differential centrifugation is...

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Related Experiment Video

Updated: Apr 29, 2026

Heterokaryon Technique for Analysis of Cell Type-specific Localization

Heterokaryon Technique for Analysis of Cell Type-specific Localization

Published on: March 11, 2011

Human proteins characterization with subcellular localizations.

Lei Yang¹, Yingli Lv¹, Tao Li²

¹College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.

Journal of Theoretical Biology

|May 28, 2014

Summary

Understanding protein subcellular localization is key to cell biology. This study compared human proteins across seven locations using various properties, enabling accurate prediction of protein function and localization.

Keywords:

Biological properties Codon usage bias Expression level Physicochemical properties Topological properties

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Area of Science:

Cell Biology
Proteomics
Bioinformatics

Background:

Proteins perform essential cellular functions, and their subcellular localization provides critical insights into their roles.
Characterizing human protein localization is a fundamental goal in cell biology and proteomics.
Large-scale studies now enable detailed analysis of protein subcellular localization.

Purpose of the Study:

To compare human proteins across seven distinct subcellular localizations.
To identify distinguishing properties of proteins based on their cellular location.
To develop a machine learning model for predicting protein subcellular localization.

Main Methods:

Classified 8842 human proteins into seven subcellular localizations using Swiss-Prot annotations.
Compared proteins using topological, biological, codon usage, mRNA expression, complexity, and physicochemical properties.
Developed a machine learning classifier incorporating these properties and pseudo-amino acid compositions.

Main Results:

Significant differences were observed in multiple properties across the seven subcellular localization categories.
The identified properties effectively differentiate proteins based on their cellular location.
The developed machine learning model demonstrated predictive capability for protein subcellular localization.

Conclusions:

The study successfully identified key properties that distinguish human proteins by subcellular localization.
The findings facilitate a better understanding of protein functions within different cellular compartments.
The developed computational approach aids in the accurate prediction of protein subcellular localization.