SignalP 5.0 improves signal peptide predictions using deep neural networks

被引：0

作者：

José Juan Almagro Armenteros

Konstantinos D. Tsirigos

Casper Kaae Sønderby

Thomas Nordahl Petersen

Ole Winther

Søren Brunak

Gunnar von Heijne

Henrik Nielsen

机构：

[1] Technical University of Denmark,Department of Bio and Health Informatics

[2] Stockholm University,Department of Biochemistry and Biophysics

[3] Stockholm University,Science for Life Laboratory

[4] Max Planck Institute for Molecular Genetics,Department of Genome Regulation

[5] University of Copenhagen,Bioinformatics Centre, Department of Biology

[6] Technical University of Denmark,National Food Institute

[7] Technical University of Denmark,Department of Applied Mathematics and Computer Science

[8] University of Copenhagen,Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences

来源：

Nature Biotechnology | 2019年 / 37卷

关键词：

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.

引用

页码：420 / 423

页数：3

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