Jove
Visualize
Contact Us
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 Concept Videos

Single-pass Transmembrane Proteins01:25

Single-pass Transmembrane Proteins

5.2K
Integral membrane proteins are tightly associated with the cell membrane and play a crucial role in cell communication, signaling, adhesion, and transport of the molecules. Some integral membrane proteins are present only in the membrane monolayer. For example, the enzyme fatty acid amide hydrolase is present in the cytoplasmic side of the membrane monolayer. In contrast, another type of integral membrane protein, also known as a transmembrane protein, spans across the membrane. Transmembrane...
5.2K
Insertion of Multi-pass Transmembrane Proteins in the RER01:29

Insertion of Multi-pass Transmembrane Proteins in the RER

8.2K
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...
8.2K
Insertion of Single-pass Transmembrane Proteins in the RER01:26

Insertion of Single-pass Transmembrane Proteins in the RER

7.0K
Integral membrane proteins are proteins adhered to the lipid bilayer of a cell organelle or membrane. They can be of two types: transmembrane integral proteins that span the lipid bilayer and monotopic proteins that are attached to either side of the membrane but do not pass through it.
Integral transmembrane proteins possess transmembrane and extra membrane domains. The transmembrane domains are primarily made of 20-25 hydrophobic amino acids arranged in a helical secondary confirmation. These...
7.0K
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

5.5K
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...
5.5K
Translocation of Proteins into the Mitochondria01:19

Translocation of Proteins into the Mitochondria

3.2K
Mitochondrial precursors are translocated to the internal subcompartments via independent mechanisms involving distinct protein machineries called translocases.
Sorting of outer membrane proteins:
Mitochondrial outer membrane proteins are of two types: the transmembrane, beta-barrel porins, and the membrane-anchored, alpha-helical proteins. Beta-barrel porin precursors are translocated by the TOM complex and inserted into the outer mitochondrial membrane by the SAM complex. In contrast,...
3.2K
Protein Transport into the Inner Mitochondrial Membrane01:34

Protein Transport into the Inner Mitochondrial Membrane

4.1K
Nuclear encoded mitochondrial precursors are imported to the inner membrane in a multistep process involving two separate translocons, TIM22 and TIM23. TIM23 is a cation-selective pore that remains closed by the N terminal segment of the protein. Negative charges on the TIM23 act as a receptor for the incoming precursor, pulling the positively charged matrix-targeting sequence for peptide insertion and translocation.
Transport of mitochondrial precursors across the TIM23 channel is driven by...
4.1K

You might also read

Related Articles

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

Sort by
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Advances in Protein Function Prediction from the Fifth CAFA Challenge.

bioRxiv : the preprint server for biology·2026
Same author

Whole-genome prediction of bacterial pathogenic capacity on novel bacteria using protein language models with PathogenFinder2.

Bioinformatics (Oxford, England)·2026
Same author

Biocentral: Embedding-based Protein Predictions.

Journal of molecular biology·2026
Same author

Toxin data quality: a critical examination of bacterial exotoxins and animal toxins.

BMC research notes·2025
Same author

FlatProt: 2D visualization eases protein structure comparison.

BMC bioinformatics·2025

Related Experiment Video

Updated: Sep 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

675

TMbed: transmembrane proteins predicted through language model embeddings.

Michael Bernhofer1,2, Burkhard Rost3,4,5

  • 1Department of Informatics, Bioinformatics and Computational Biology ‑ i12, Technical University of Munich (TUM), Boltzmannstr. 3, 85748, Garching, Germany. bernhoferm@rostlab.org.

BMC Bioinformatics
|August 8, 2022
PubMed
Summary
This summary is machine-generated.

TMbed, a new method using protein Language Models, accurately predicts transmembrane protein regions. It offers high performance and speed for proteome-wide analysis, aiding structural biology research.

Keywords:
Protein language modelsProtein structure predictionTransmembrane protein prediction

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

68.8K
Determining Membrane Protein Topology Using Fluorescence Protease Protection FPP
08:14

Determining Membrane Protein Topology Using Fluorescence Protease Protection FPP

Published on: April 20, 2015

17.9K

Related Experiment Videos

Last Updated: Sep 2, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

675
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

68.8K
Determining Membrane Protein Topology Using Fluorescence Protease Protection FPP
08:14

Determining Membrane Protein Topology Using Fluorescence Protease Protection FPP

Published on: April 20, 2015

17.9K

Area of Science:

  • Structural Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Transmembrane proteins (TMPs) are crucial in molecular biology and medicine, yet their experimental 3D structures are underrepresented.
  • Current methods like AlphaFold2 predict TMP structures but struggle with accurate transmembrane region annotation.

Purpose of the Study:

  • To develop a novel computational method, TMbed, for accurate prediction of transmembrane regions in proteins.
  • To enable efficient, proteome-wide annotation of transmembrane helices and strands.

Main Methods:

  • Utilized embeddings from protein Language Models (pLMs), specifically ProtT5.
  • Developed TMbed incorporating Gaussian and Viterbi filters for residue classification (transmembrane helix, transmembrane strand, signal peptide, other).
  • Evaluated performance on per-protein and per-segment levels, assessing speed and accuracy on standard hardware.

Main Results:

  • TMbed achieved high accuracy, correctly identifying 94% of beta barrel TMPs and 98% of alpha helical TMPs.
  • Demonstrated excellent per-segment prediction, locating 9 out of 10 transmembrane segments within five residues of experimental data.
  • Showcased computational efficiency, processing entire proteomes within hours on a single consumer-grade machine.

Conclusions:

  • TMbed predicts transmembrane proteins with accuracy comparable or superior to existing methods, featuring significantly lower false positive rates.
  • Its speed and accuracy make TMbed ideal for annotating large-scale structural predictions, such as those from AlphaFold2.