TMbed predicts transmembrane beta barrel and alpha helical proteins, the position and orientation of their transmembrane segments, and signal peptides. We use a Protein Language Model, ProtT5-XL-U50 [1], to generate embeddings used as input for our method.
Pre-Print: bioRxiv
Publication: BMC Bioinformatics
TMbed is also available via bio_embeddings and LambdaPP [2].
Or you can try out TMbed using Google Colab.
Visit TMvisDB [3] to see precomputed predictions for AlphaFold DB [4] structures.
-
Clone the repository and run directly with
python -m tmbed
.git clone https://github.com/BernhoferM/TMbed.git tmbed cd tmbed/ python -m tmbed --help
-
Clone the repository and install with
pip
orpoetry
.git clone https://github.com/BernhoferM/TMbed.git tmbed cd tmbed/ pip install . tmbed --help
-
Directly install from the repository.
pip install git+https://github.com/BernhoferM/TMbed.git tmbed --help
If you want to use GPU acceleration (highly recommended), please install the corresponding version of PyTorch: Get Started
Reproducibility: TMbed tries to make predictions on GPU as deterministic as possible.
However, some things are left to the user, such as setting the CUBLAS_WORKSPACE_CONFIG EnvVar.
Regarding reproducibility in PyTorch, we also recommend their notes on randomness and reproducibility.
h5py >= 3.2.1
numpy >= 1.20.3
sentencepiece >= 0.1.96
torch >= 1.10.2
tqdm >= 4.62.3
transformers >= 4.11.3
typer >= 0.4.1
TMbed has two commands embed
and predict
that you can use to generate embeddings and predictions.
The first time TMbed is used to generate embeddings, it will automatically download the ProtT5-XL-U50 encoder model (2.25 GB) and save it inside the models/t5/
subdirectory.
Alternatively, you can use the download
command to download the ProtT5 model without generating embeddings.
You can generate embeddings for a set of proteins using the embed
command.
The only required input is a FASTA file containing the protein sequences.
python -m tmbed embed -f sample.fasta -e sample.h5
The created HDF5 file is indexed with the md5 hash sums of the corresponding protein sequences.
Every entry contains a L x 1024
matrix, where L
is the length of the protein sequence.
With the predict
command you can generate predictions for a set of proteins.
The only required input is a FASTA file containing the protein sequences. TMbed will generate the needed embeddings on the fly. If you also supply a file with embeddings, those embeddings will be used and only the subset of proteins contained within both input files will be predicted.
python -m tmbed predict -f sample.fasta -p sample.pred
python -m tmbed predict -f sample.fasta -e sample.h5 -p sample.pred
--out-format
sets the output format for the prediction file.
--batch-size
is an approximation of how many residues should be included per batch.
Each batch is constrained by N * L1.5 ≤ BS1.5, where N is the number of sequences in the batch, L is the length of the longest sequence in the batch, and BS is the batch size. Batches with only a single sequence can break this restriction.
--use-gpu / --no-use-gpu
controls whether TMbed will try to use an available GPU to speed up computations.
--cpu-fallback / --no-cpu-fallback
controls whether TMbed will try to use the CPU if it fails to compute the embeddings on GPU.
When in half-precision mode, the ProtT5-XL-U50 encoder needs about 2.5 GB of VRAM on the GPU.
Additional memory requirements to generate embeddings depend heavily on the sequence length.
We recommend a GPU with at least 12GB of VRAM, which is enough for sequences of up to ~4200 residues.
If you run into "out of memory" issues, try reducing the batch size.
TMbed supports five different output formats:
0
: 3-line format with directed segments.1
: 3-line format with undirected segments.2
: Tabular format with directed segments.3
: Tabular format with undirected segments.4
: 3-line format with directed segments and explicit inside/outside prediction (a mix of format0
and1
).
Predicted residue classes are encoded by single letters.
In 3-line format, every protein is represented by three lines: header, sequence, labels.
In tabular format, every protein is represented by a table containing sequence, labels, and class probabilities.
-
--out-format=0 (default)
B
: Transmembrane beta strand (IN-->OUT orientation)b
: Transmembrane beta strand (OUT-->IN orientation)H
: Transmembrane alpha helix (IN-->OUT orientation)h
: Transmembrane alpha helix (OUT-->IN orientation)S
: Signal peptide.
: Non-Transmembrane
>7acg_A|P18895|ALGE_PSEAE MNSSRSVNPRPSFAPRALSLAIALLLGAPAFAANSGEAPKNFGLDVKITGESENDRDLGTAPGGTLNDIGIDLRPWAFGQWGDWSAYFMGQAVAATDTIETDTLQSDTDDGNNSRNDGREPDKSYLAAREFWVDYAGLTAYPGEHLRFGRQRLREDSGQWQDTNIEALNWSFETTLLNAHAGVAQRFSEYRTDLDELAPEDKDRTHVFGDISTQWAPHHRIGVRIHHADDSGHLRRPGEEVDNLDKTYTGQLTWLGIEATGDAYNYRSSMPLNYWASATWLTGDRDNLTTTTVDDRRIATGKQSGDVNAFGVDLGLRWNIDEQWKAGVGYARGSGGGKDGEEQFQQTGLESNRSNFTGTRSRVHRFGEAFRGELSNLQAATLFGSWQLREDYDASLVYHKFWRVDDDSDIGTSGINAALQPGEKDIGQELDLVVTKYFKQGLLPASMSQYVDEPSALIRFRGGLFKPGDAYGPGTDSTMHRAFVDFIWRF SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.........BBBBBBBBBB.................bbbbbbbbbbb.....BBBBBBBBBB...............................bbbbbbbbbb.........BBBBBB...............bbbbbbbb....BBBBBBBB....................bbbbbbbb......BBBBBBBB..........................bbbbbbbb..........BBBBBBBBBB............................bbbbbbbbbb.....BBBBBBBB..............................................bbbbbbbbb.....BBBBBBBB............................bbbbbbbbb..................BBBBBBBBBB...............bbbbbbbbb.
-
--out-format=1
B
: Transmembrane beta strandH
: Transmembrane alpha helixS
: Signal peptidei
: Non-Transmembrane, insideo
: Non-Transmembrane, outside
>7acg_A|P18895|ALGE_PSEAE MNSSRSVNPRPSFAPRALSLAIALLLGAPAFAANSGEAPKNFGLDVKITGESENDRDLGTAPGGTLNDIGIDLRPWAFGQWGDWSAYFMGQAVAATDTIETDTLQSDTDDGNNSRNDGREPDKSYLAAREFWVDYAGLTAYPGEHLRFGRQRLREDSGQWQDTNIEALNWSFETTLLNAHAGVAQRFSEYRTDLDELAPEDKDRTHVFGDISTQWAPHHRIGVRIHHADDSGHLRRPGEEVDNLDKTYTGQLTWLGIEATGDAYNYRSSMPLNYWASATWLTGDRDNLTTTTVDDRRIATGKQSGDVNAFGVDLGLRWNIDEQWKAGVGYARGSGGGKDGEEQFQQTGLESNRSNFTGTRSRVHRFGEAFRGELSNLQAATLFGSWQLREDYDASLVYHKFWRVDDDSDIGTSGINAALQPGEKDIGQELDLVVTKYFKQGLLPASMSQYVDEPSALIRFRGGLFKPGDAYGPGTDSTMHRAFVDFIWRF SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSiiiiiiiiiBBBBBBBBBBoooooooooooooooooBBBBBBBBBBBiiiiiBBBBBBBBBBoooooooooooooooooooooooooooooooBBBBBBBBBBiiiiiiiiiBBBBBBoooooooooooooooBBBBBBBBiiiiBBBBBBBBooooooooooooooooooooBBBBBBBBiiiiiiBBBBBBBBooooooooooooooooooooooooooBBBBBBBBiiiiiiiiiiBBBBBBBBBBooooooooooooooooooooooooooooBBBBBBBBBBiiiiiBBBBBBBBooooooooooooooooooooooooooooooooooooooooooooooBBBBBBBBBiiiiiBBBBBBBBooooooooooooooooooooooooooooBBBBBBBBBiiiiiiiiiiiiiiiiiiBBBBBBBBBBoooooooooooooooBBBBBBBBBi
-
--out-format=2
and--out-format=3
AA
: Amino acidPRD
: Predicted class labelP(B)
: Probability for class 'transmembrane beta strand'P(H)
: Probability for class 'transmembrane alpha helix'P(S)
: Probability for class 'signal peptide'P(i)
: Probability for class 'non-transmembrane, inside'P(o)
: Probability for class 'non-transmembrane, outside'
--out-format=2
uses the same class labels as--out-format=0
.
--out-format=3
uses the same class labels as--out-format=1
.>7acg_A|P18895|ALGE_PSEAE AA PRD P(B) P(H) P(S) P(i) P(o) M S 0.00 0.00 0.94 0.05 0.00 N S 0.00 0.00 0.98 0.02 0.00 S S 0.00 0.00 0.99 0.01 0.00 S S 0.00 0.00 0.99 0.01 0.00 R S 0.00 0.00 1.00 0.00 0.00 S S 0.00 0.00 1.00 0.00 0.00 V S 0.00 0.00 0.99 0.00 0.00 N S 0.00 0.00 0.99 0.01 0.00 ...
-
--out-format=4
B
: Transmembrane beta strand (IN-->OUT orientation)b
: Transmembrane beta strand (OUT-->IN orientation)H
: Transmembrane alpha helix (IN-->OUT orientation)h
: Transmembrane alpha helix (OUT-->IN orientation)S
: Signal peptidei
: Non-Transmembrane, insideo
: Non-Transmembrane, outside
>7acg_A|P18895|ALGE_PSEAE MNSSRSVNPRPSFAPRALSLAIALLLGAPAFAANSGEAPKNFGLDVKITGESENDRDLGTAPGGTLNDIGIDLRPWAFGQWGDWSAYFMGQAVAATDTIETDTLQSDTDDGNNSRNDGREPDKSYLAAREFWVDYAGLTAYPGEHLRFGRQRLREDSGQWQDTNIEALNWSFETTLLNAHAGVAQRFSEYRTDLDELAPEDKDRTHVFGDISTQWAPHHRIGVRIHHADDSGHLRRPGEEVDNLDKTYTGQLTWLGIEATGDAYNYRSSMPLNYWASATWLTGDRDNLTTTTVDDRRIATGKQSGDVNAFGVDLGLRWNIDEQWKAGVGYARGSGGGKDGEEQFQQTGLESNRSNFTGTRSRVHRFGEAFRGELSNLQAATLFGSWQLREDYDASLVYHKFWRVDDDSDIGTSGINAALQPGEKDIGQELDLVVTKYFKQGLLPASMSQYVDEPSALIRFRGGLFKPGDAYGPGTDSTMHRAFVDFIWRF SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSiiiiiiiiiBBBBBBBBBBooooooooooooooooobbbbbbbbbbbiiiiiBBBBBBBBBBooooooooooooooooooooooooooooooobbbbbbbbbbiiiiiiiiiBBBBBBooooooooooooooobbbbbbbbiiiiBBBBBBBBoooooooooooooooooooobbbbbbbbiiiiiiBBBBBBBBoooooooooooooooooooooooooobbbbbbbbiiiiiiiiiiBBBBBBBBBBoooooooooooooooooooooooooooobbbbbbbbbbiiiiiBBBBBBBBoooooooooooooooooooooooooooooooooooooooooooooobbbbbbbbbiiiiiBBBBBBBBoooooooooooooooooooooooooooobbbbbbbbbiiiiiiiiiiiiiiiiiiBBBBBBBBBBooooooooooooooobbbbbbbbbi
We provide precomputed predictions for the human proteome and for UniProtKB/Swiss-Prot.
- Install via GitHub
- Publish pypi package
- Add data sets to GitHub
- Create Google Colab Notebook
- Add training scripts to GitHub
- Integrate into bio_embeddings
- Integrate into LambdaPP
[1] Elnaggar A, Heinzinger M, Dallago C, Rihawi G, Wang Y, Jones L, Gibbs T, Feher T, Angerer C, Bhowmik D, Rost B (2021). ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/TPAMI.2021.3095381.
[2] Olenyi T, Marquet C, Heinzinger M, Kröger B, Nikolova T, Bernhofer M, Sändig P, Schütze K, Littmann M, Mirdita M, Steinegger M, Dallago C, Rost B (2023). LambdaPP: Fast and accessible protein-specific phenotype predictions. Protein Sci, 32, 1:e4524.
[3] Marquet C, Grekova A, Houri L, Bernhofer M, Jimenez-Soto L F, Karl T, Heinzinger M, Dallago C, Rost B (2022). TMvisDB: resource for transmembrane protein annotation and 3D visualization. bioRxiv, 2022.11.30.518551.
[4] Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, Zidek A, Green T, Tunyasuvunakool K, Petersen S, Jumper J, Clancy E, Green R, Vora A, Lutfi M, Figurnov M, Cowie A, Hobbs N, Kohli P, Kleywegt G, Birney E, Hassabis D, Velankar S (2022). AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res, 50, D1:D439-D444.