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Related Concept Videos

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...
Subcellular Fractionation01:32

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...
Nuclear Protein Sorting01:34

Nuclear Protein Sorting

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.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

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.
Protein sorting can be of two types: signal-based sorting and vesicle-based trafficking. In signal-based sorting, specific amino acid sequences called sorting signals target proteins to the proper location inside the cell either via gated transport or by protein translocation.  In gated transport, folded...
Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Related Experiment Video

Updated: Jul 4, 2026

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach
04:25

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

Published on: August 8, 2025

Protein subcellular localization prediction using artificial intelligence technology.

Rajesh Nair1, Burkhard Rost

  • 1CUBIC Department of Biochemistry and Molecular Biophysics and Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 2, 2008
PubMed
Summary
This summary is machine-generated.

Predicting protein subcellular localization is crucial for understanding biological functions and diseases. Advanced artificial intelligence (AI) methods significantly improve prediction accuracy, aiding biological research.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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Last Updated: Jul 4, 2026

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach
04:25

Enriching Subcellular Proteins in Leptospira Using a Triton X-114-Based Fractionation Approach

Published on: August 8, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Computational biology
  • Bioinformatics
  • Molecular biology

Background:

  • Proteins are vital for numerous biological processes, and their subcellular localization is key to their function.
  • Mislocalization of proteins is linked to diseases like cancer and Alzheimer's.
  • Accurate protein function prediction relies heavily on understanding subcellular localization.

Purpose of the Study:

  • To review and highlight key computational methods for predicting protein subcellular localization.
  • To emphasize the impact of artificial intelligence (AI) on improving prediction accuracy.
  • To discuss the role of these methods in advancing biological research and disease understanding.

Main Methods:

  • Review of established methods like sequence homology.
  • Analysis of AI-based techniques including Hidden Markov Models (HMMs), Neural Networks (NNs), and Support Vector Machines (SVMs).
  • Focus on *ab initio* methods utilizing native amino acid sequences and predicted features.

Main Results:

  • AI-based methods have dramatically improved protein subcellular localization prediction accuracy.
  • State-of-the-art methods, particularly NN and HMM-based approaches, accurately predict sorting signals.
  • *Ab initio* methods show remarkable improvements, increasing accuracy by over 30% in the last decade.

Conclusions:

  • AI has revolutionized protein subcellular localization prediction, surpassing traditional methods.
  • Improved prediction accuracy aids in understanding protein function and disease mechanisms.
  • These computational tools are becoming indispensable for directing experimental biological research.