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Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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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.
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Related Experiment Video

Updated: May 13, 2026

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

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Published on: June 30, 2018

Automated protein subcellular localization based on local invariant features.

Chao Li1, Xue-hong Wang, Li Zheng

  • 1Department of Computer Science and Technology, Shanghai Normal University, Shanghai, 200234, China.

The Protein Journal
|March 21, 2013
PubMed
Summary
This summary is machine-generated.

A new method called LDPs accurately identifies protein subcellular locations using image analysis. This technique improves high-throughput imaging for understanding protein function and cellular processes.

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Area of Science:

  • Cell Biology
  • Bioinformatics
  • Image Analysis

Background:

  • Determining protein subcellular location is crucial for understanding protein function.
  • Fluorescence microscopy is a common high-throughput method for imaging subcellular localizations.
  • Automated analysis of cellular images relies on effective image feature calculation.

Purpose of the Study:

  • To propose a novel method, Local Difference Patterns (LDPs), for robust feature extraction from cellular images.
  • To evaluate the efficacy of LDPs in classifying protein subcellular locations using image analysis.

Main Methods:

  • Developed LDPs, a feature extraction technique invariant to translation and rotation, by analyzing local pixel differences.
  • Calculated D-values between central pixels and their eight neighbors to define image features.
  • Trained and tested Support Vector Machine (SVM) classifiers on two distinct image datasets.

Main Results:

  • The LDPs method achieved high classification accuracies for protein subcellular localization.
  • Achieved 96.7% accuracy on a dataset of endogenously expressed proteins in 10 locations.
  • Achieved 92.3% accuracy on a dataset of transfected proteins in 11 locations.

Conclusions:

  • LDPs is an effective and accurate method for protein subcellular localization classification.
  • The proposed method demonstrates significant potential for automated high-throughput cellular image analysis.
  • Accurate subcellular localization via LDPs aids in understanding protein function and cellular mechanisms.