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Related Concept Videos

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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
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,...
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,...
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...
Protein Complex Assembly02:41

Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...

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Related Experiment Video

Updated: Jun 13, 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

Multitask learning for protein subcellular location prediction.

Qian Xu1, Sinno Jialin Pan, Hannah Hong Xue

  • 1Bioengineering Program, Hong Kong University of Science and Technology, Clearwater Bay, Kowloon, Hong Kong. fleurxq@ust.hk

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|April 28, 2010
PubMed
Summary
This summary is machine-generated.

Multitask learning improves protein subcellular localization prediction by leveraging commonalities across related organisms. This approach significantly outperforms single-task methods, especially for closely related species, boosting accuracy by up to 25%.

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

Related Experiment Videos

Last Updated: Jun 13, 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:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Protein subcellular localization is crucial for understanding protein function and cellular processes.
  • Accurate prediction aids in genome annotation and drug target identification.
  • Traditional methods like Support Vector Machines (SVMs) struggle with data sparsity in specific species.

Purpose of the Study:

  • To address data sparsity in protein subcellular localization prediction.
  • To explore the utility of multitask learning across different organisms for improved prediction accuracy.
  • To compare different multitask learning strategies for this biological problem.

Main Methods:

  • Formulated protein subcellular localization as a multitask learning problem across 20 organisms.
  • Adapted and compared two multitask learning algorithm specializations.
  • Investigated parameter sharing (multitask and supertype kernels) versus latent feature sharing.

Main Results:

  • Multitask learning significantly outperformed traditional single-task methods.
  • Methods sharing parameters (multitask/supertype kernels) showed slight advantages over latent feature sharing.
  • Accuracy improvements reached up to 25% for closely related organisms.
  • Limited benefits were observed for distantly related or unrelated organisms.

Conclusions:

  • Multitask learning is a powerful approach for enhancing protein subcellular localization prediction, particularly when organisms share biological relationships.
  • The choice of multitask learning strategy, specifically parameter sharing, can influence performance.
  • Biological relatedness between species is a key factor for the success of cross-species multitask learning.