DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks
DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks
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.
Hallgren Jeppe、Krogh Anders、Pedersen Mads Damgaard、Marcatili Paolo、Almagro Armenteros Jos¨| Juan、Nielsen Henrik、Tsirigos Konstantinos D.、Winther Ole
BioLib TechnologiesDepartment of Computer Science, University of Copenhagen||Center for Health Data Science, University of CopenhagenBioLib TechnologiesDepartment of Health Technology, Technical University of DenmarkDepartment of Genetics, Stanford University School of MedicineDepartment of Health Technology, Technical University of DenmarkDepartment of Energy Conversion and Storage, Technical University of DenmarkDepartment of Biology, Bioinformatics Center, University of Copenhagen||Center for Genomic Medicine, Rigshospitalet (Copenhagen University Hospital)||Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark
分子生物学生物科学研究方法、生物科学研究技术
Hallgren Jeppe,Krogh Anders,Pedersen Mads Damgaard,Marcatili Paolo,Almagro Armenteros Jos¨| Juan,Nielsen Henrik,Tsirigos Konstantinos D.,Winther Ole.DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks[EB/OL].(2025-03-28)[2025-05-28].https://www.biorxiv.org/content/10.1101/2022.04.08.487609.点此复制
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