BernhoferM/TMbed
Transmembrane proteins predicted through Language Model embeddings
TMbed helps biologists and biochemists predict the structure and location of transmembrane proteins from their amino acid sequences. You provide a FASTA file of protein sequences, and it tells you if a protein is a transmembrane alpha helix or beta barrel, its specific segments, and any signal peptides. This is valuable for researchers studying protein function, drug discovery, or membrane biology.
Use this if you need to quickly and accurately identify transmembrane regions and signal peptides within protein sequences to understand their function and cellular location.
Not ideal if you are looking for general protein structure prediction beyond transmembrane regions, or if you do not have protein sequences as your starting material.
Stars
46
Forks
8
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 24, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/BernhoferM/TMbed"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
sacdallago/bio_embeddings
Get protein embeddings from protein sequences
tbepler/prose
Multi-task and masked language model-based protein sequence embedding models.
Rostlab/VESPA
VESPA is a simple, yet powerful Single Amino Acid Variant (SAV) effect predictor based on...
DeepRank/DeepRank-GNN-esm
Graph Network for protein-protein interface including language model features
bschilder/VEP_protein
Using Protein Language Models to compute Variant Effect Predictions across population-scale populations.