Submit data

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Sequence submission: paste the sequence(s) and/or upload a local file

Mirror You can also use DeepTMHMM on BioLib


Or upload a FASTA file from your computer:

For example proteins Click here

Citation

If you use DeepTMHMM, please cite:

Jeppe Hallgren, Konstantinos D. Tsirigos, Mads D. Pedersen, José Juan Almagro Armenteros, Paolo Marcatili, Henrik Nielsen, Anders Krogh and Ole Winther (2022). DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks.https://doi.org/10.1101/2022.04.08.487609

Instructions

1. Specify the input sequences

All the input sequences must be in one-letter amino acid code. The allowed alphabet is as follows:

A C D E F G H I K L M N P Q R S T V W Y

The sequences can be input in the following two ways:

• Paste a single sequence (just the amino acids) or a number of sequences in FASTA format into the upper window of the main server page.
  
  
• Select a FASTA file on your local disk, either by typing the file name into the lower window or by browsing the disk.

2. Submit the job

Click on the "Submit" button. The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in the browser window.

At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; you will be notified by e-mail when it has terminated. The e-mail message will contain the URL under which the results are stored; they will remain on the server for 24 hours for you to collect them.

Example Outputs

Visualization on structure

With MembraneFold you can visualize DeepTMHMM's transmembrane prediction on a predicted or experimental structure

Abstract

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

Jeppe Hallgren, Konstantinos D. Tsirigos, Mads D. Pedersen, José Juan Almagro Armenteros, Paolo Marcatili, Henrik Nielsen, Anders Krogh and Ole Winther

BioRxiv, 2022

Transmembrane proteins span the lipid bilayer and are divided into two major structural classes, namely alpha helical and beta barrels. We introduce DeepTMHMM, a deep learning protein language model-based algorithm that can detect and predict the topology of both alpha helical and beta barrels proteins with unprecedented accuracy. DeepTMHMM scales to proteomes and covers all domains of life, which makes it ideal for metagenomics analyses.

https://doi.org/10.1101/2022.04.08.487609

Licensing and Download

This software is made freely available here for both academic and commercial purposes. For commercial users wishing to run the software on their own servers, a commercial license is required. This is not needed for academic users. To inquire about a commercial license, please contact licensing@biolib.com.