Jove
Visualize
Contact Us

Related Concept Videos

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...
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as G-protein-linked receptors (GPCRs) and...
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...
Cotranslational Protein Translocation01:20

Cotranslational Protein Translocation

Translocation of proteins across membranes is an ancient process that occurs even in bacteria and archaebacteria. In fact, the components of the translocation machinery are still conserved between prokaryotes and eukaryotes.
Sec61 channel partners for cotranslational translocation
During cotranslational translocation, the Sec61 channel partners with the signal recognition particle (SRP), the signal recognition particle receptor (SR), and the ribosomes to transport the nascent polypeptide chain...
Insertion of Multi-pass Transmembrane Proteins in the RER01:29

Insertion of Multi-pass Transmembrane Proteins in the RER

The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
The multipass transmembrane proteins are the type IV integral membrane proteins with multiple topogenic sequences determining their spatial arrangement in the ER membrane. Nearly all multipass proteins lack a cleavable signal sequence and use...
Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Integrative QSAR modelling of multi-species pesticide baseline (narcosis) toxicity.

Comparative biochemistry and physiology. Toxicology & pharmacology : CBP·2026
Same author

QSAR modelling of pesticide modes of toxic action across trophic levels via multi-label extreme gradient boosting.

Pest management science·2026
Same author

Quantifying tolerances or maximum residue limits of pesticide in food commodities via deep neural networks.

Pest management science·2025
Same author

Unravelling patterns of food tolerance to pesticide residues via non-negative matrix factorization.

Journal of food science·2025
Same author

Single-task regression naturally adapts to multi-species (eco)toxicological modelling: a case study on animals.

Environmental science and pollution research international·2025
Same author

Transferring knowledge across aquatic species via clustering techniques to unravel patterns of pesticide toxicity.

The Science of the total environment·2024
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 21, 2026

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

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

Published on: June 30, 2018

Multi-label multi-kernel transfer learning for human protein subcellular localization.

Suyu Mei1

  • 1Software College, Shenyang Normal University, Shenyang, China. 061021053@fudan.edu.cn

Plos One
|June 22, 2012
PubMed
Summary
This summary is machine-generated.

A new multi-label multi-kernel transfer learning model (MLMK-TLM) accurately predicts human protein subcellular localization, even for proteins with multiple locations. This model improves upon existing methods, offering better performance for novel proteins and practical applicability in biological research.

More Related Videos

Differential Labeling of Cell-surface and Internalized Proteins after Antibody Feeding of Live Cultured Neurons
11:56

Differential Labeling of Cell-surface and Internalized Proteins after Antibody Feeding of Live Cultured Neurons

Published on: February 12, 2014

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

Related Experiment Videos

Last Updated: May 21, 2026

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

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

Published on: June 30, 2018

Differential Labeling of Cell-surface and Internalized Proteins after Antibody Feeding of Live Cultured Neurons
11:56

Differential Labeling of Cell-surface and Internalized Proteins after Antibody Feeding of Live Cultured Neurons

Published on: February 12, 2014

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

Area of Science:

  • Computational biology
  • Bioinformatics
  • Molecular biology

Background:

  • Existing protein subcellular localization models struggle with accuracy, especially for novel proteins and those with multiple locations.
  • Predicting multiple subcellular locations for human proteins remains a significant challenge in bioinformatics.
  • Current methods may overestimate performance or lack specialized capabilities for multiplex human protein localization.

Purpose of the Study:

  • To develop an advanced computational model for predicting human protein subcellular localization.
  • To address the complexity of proteins residing in multiple cellular compartments.
  • To enhance the accuracy and applicability of protein localization prediction for novel and multi-localized proteins.

Main Methods:

  • Proposed a novel multi-label multi-kernel transfer learning model (MLMK-TLM) for human protein subcellular localization.
  • Introduced a multi-label confusion matrix and formulated new multi-labeling performance measures.
  • Adapted probabilistic outputs for multi-label learning, extending previous Gene Ontology (GO)-TLM and MK-TLM models.

Main Results:

  • MLMK-TLM demonstrated significantly superior performance compared to baseline models on a human protein benchmark dataset.
  • The model exhibited robust multi-labeling capabilities, accurately predicting locations for proteins with multiple cellular compartments.
  • Experimental findings were validated against the latest Swiss-Prot database, confirming prediction accuracy.

Conclusions:

  • MLMK-TLM offers a practical and effective solution for predicting human protein subcellular localization, particularly for complex cases.
  • The model's transfer learning approach and multi-labeling capacity enhance its utility for novel protein predictions.
  • The developed software provides a valuable tool for researchers in molecular biology and bioinformatics.