DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

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

doi:10.1101/2022.04.08.487609

preprintbioRxiv (Cold Spring Harbor Laboratory)Apr 10, 2022GREEN OA

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

JHJeppe Hallgren KDKonstantinos D. Tsirigos MDMads Damgaard Pedersen JJJosé Juan Almagro Armenteros PMPaolo Marcatili

Technical University of Denmark · Stanford University · +3 more institutions

Indexed incrossref

Abstract

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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Topics & keywords

Topics

Keywords

BETA (programming language)
Transmembrane protein
Alpha (finance)
Proteome
Deep learning
Artificial intelligence
Artificial neural network
Transmembrane domain

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