SignalP 5.0 improves signal peptide predictions using deep neural networks

被引：3368

作者：

Armenteros, Jose Juan Almagro ^{[1
]}

Tsirigos, Konstantinos D. ^{[1
,2
,3
,4
]}

Sonderby, Casper Kaae ^{[5
]}

Petersen, Thomas Nordahl ^{[6
]}

Winther, Ole ^{[5
,7
]}

Brunak, Soren ^{[1
,8
]}

von Heijne, Gunnar ^{[2
,3
]}

Nielsen, Henrik ^{[1
]}

机构：

[1] Tech Univ Denmark, Dept Bio & Hlth Informat, Lyngby, Denmark

[2] Stockholm Univ, Dept Biochem & Biophys, Stockholm, Sweden

[3] Stockholm Univ, Sci Life Lab, Solna, Sweden

[4] Max Planck Inst Mol Genet, Dept Genome Regulat, Berlin, Germany

[5] Univ Copenhagen, Bioinformat Ctr, Dept Biol, Copenhagen, Denmark

[6] Tech Univ Denmark, Natl Food Inst, Lyngby, Denmark

[7] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark

[8] Univ Copenhagen, Novo Nord Fdn Ctr Prot Res, Fac Hlth & Med Sci, Dis Syst Biol Program, Copenhagen, Denmark

来源：

NATURE BIOTECHNOLOGY | 2019年 / 37卷 / 04期

关键词：

TRANSLOCATION; TOPOLOGY; SEC; IDENTIFICATION; GENERATION; PROTEINS; MODEL;

D O I：

10.1038/s41587-019-0036-z

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 [微生物学]; 090105 [作物生产系统与生态工程];

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 / +

页数：6

共 50 条

[1]

Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265

[2]

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs [J].

Altschul, SF ;

Madden, TL ;

Schaffer, AA ;

Zhang, JH ;

Zhang, Z ;

Miller, W ;

Lipman, DJ .

NUCLEIC ACIDS RESEARCH, 1997, 25 (17) :3389-3402

[3]

Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI [10.1093/nar/gkw1099, 10.1093/nar/gkh131]

[4]

DeepLoc: prediction of protein subcellular localization using deep learning [J].

Armenteros, Jose Juan Almagro ;

Sonderby, Casper Kaae ;

Sonderby, Soren Kaae ;

Nielsen, Henrik ;

Winther, Ole .

BIOINFORMATICS, 2017, 33 (21) :3387-3395

[5]

Prediction of signal peptides in archaea [J].

Bagos, P. G. ;

Tsirigos, K. D. ;

Plessas, S. K. ;

Liakopoulos, T. D. ;

Hamodrakas, S. J. .

PROTEIN ENGINEERING DESIGN & SELECTION, 2009, 22 (01) :27-35

[6]

Combined prediction of Tat and Sec signal peptides with hidden Markov models [J].

Bagos, Pantelis G. ;

Nikolaou, Elisanthi P. ;

Liakopoulos, Theodore D. ;

Tsirigos, Konstantinos D. .

BIOINFORMATICS, 2010, 26 (22) :2811-2817

[7]

Prediction of Lipoprotein Signal Peptides in Gram-Positive Bacteria with a Hidden Markov Model [J].

Bagos, Pantells G. ;

Tslrigos, Konstantinos D. ;

Liakopoulos, Theodore D. ;

Hamodrakas, Stavros J. .

JOURNAL OF PROTEOME RESEARCH, 2008, 7 (12) :5082-5093

[8]

Prediction of twin-arginine signal peptides [J].

Bendtsen, JD ;

Nielsen, H ;

Widdick, D ;

Palmer, T ;

Brunak, S .

BMC BIOINFORMATICS, 2005, 6 (1)

[9]

Improved prediction of signal peptides: SignalP 3.0 [J].

Bendtsen, JD ;

Nielsen, H ;

von Heijne, G ;

Brunak, S .

JOURNAL OF MOLECULAR BIOLOGY, 2004, 340 (04) :783-795

[10]

The Twin-Arginine Protein Franslocation Pathway [J].

Berks, Ben C. .

ANNUAL REVIEW OF BIOCHEMISTRY, VOL 84, 2015, 84 :843-864

← 1 2 3 4 5 →