BETA(编程语言)
阿尔法(金融)
跨膜蛋白
蛋白质组
人工智能
跨膜结构域
深度学习
计算生物学
人工神经网络
计算机科学
拓扑(电路)
生物
生物信息学
数学
生物化学
组合数学
膜
结构效度
统计
受体
程序设计语言
心理测量学
作者
Jeppe Hallgren,Konstantinos D. Tsirigos,Mads Damgaard Pedersen,José Juan Almagro Armenteros,Paolo Marcatili,Henrik Nielsen,Anders Krogh,Ole Winther
标识
DOI:10.1101/2022.04.08.487609
摘要
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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