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.
Competing Interest Statement
A version of DeepTMHMM has been commercialized by the Technical University of Denmark - DTU (it is provided for a fee to commercial users). The revenue from these commercial sales is divided between the program developers and DTU.
Footnotes
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Posted April 10, 2022.
DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks
