DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

Hallgren, J.; Tsirigos, K.; Damgaard Pedersen, M.; Almagro Armenteros, J. J.; Marcatili, P.; Nielsen, H.; Krogh, A.; Winther, O.

doi:10.1101/2022.04.08.487609

bioRxiv · 10.1101/2022.04.08.487609

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

Hallgren, J.·Tsirigos, K.·Damgaard Pedersen, M.·Almagro Armenteros, J. J.·Marcatili, P.·Nielsen, H.·Krogh, A.·Winther, O.

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Abstract

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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Hallgren, J., Tsirigos, K., Damgaard Pedersen, M., Almagro Armenteros, J. J., Marcatili, P., Nielsen, H., Krogh, A., Winther, O.. 2022-04-10. DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. https://doi.org/10.1101/2022.04.08.487609

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