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DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks
Open accessAbstract
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 ( https://dtu.biolib.com/DeepTMHMM ) scales to proteomes and covers all domains of life, which makes it ideal for metagenomics analyses.
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Metadata
- Authors
- Hallgren, J., Tsirigos, K. D., Pedersen, M. D., Almagro Armenteros, J. J., Marcatili, P., Nielsen, H., Krogh, A., & Winther, O.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2022-04-10
- OpenAlex
- W4223551334
- Cited by
- 1,499
- References
- 3
- Type
- posted content
- Language
- en
- Open access
- green
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