Signal Peptides Generated by Attention-Based Neural Networks

被引：53

作者：

Wu, Zachary ^{[1
]}

Yang, Kevin K. ^{[1
]}

Liszka, Michael J. ^{[2
]}

Lee, Alycia ^{[3
]}

Batzilla, Alina ^{[2
]}

Wernick, David ^{[2
]}

Weiner, David P. ^{[2
]}

Arnold, Frances H. ^{[1
]}

机构：

[1] CALTECH, Dept Chem & Chem Engn, Pasadena, CA 91125 USA

[2] BASF Enzymes, San Diego, CA 92121 USA

[3] CALTECH, Dept Computat & Math Sci, Pasadena, CA 91125 USA

来源：

ACS SYNTHETIC BIOLOGY | 2020年 / 9卷 / 08期

基金：

美国国家科学基金会;

关键词：

machine learning; signal peptides; protein design; Bacillus subtilis; secretion; RECOMBINANT PROTEIN SECRETION; BACILLUS-SUBTILIS; TRANSLOCATION; OPTIMIZATION; TOOL;

D O I：

10.1021/acssynbio.0c00219

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Short (15-30 residue) chains of amino acids at the amino termini of expressed proteins known as signal peptides (SPs) specify secretion in living cells. We trained an attention-based neural network, the Transformer model, on data from all available organisms in Swiss-Prot to generate SP sequences. Experimental testing demonstrates that the model-generated SPs are functional: when appended to enzymes expressed in an industrial Bacillus subtilis strain, the SPs lead to secreted activity that is competitive with industrially used SPs. Additionally, the model-generated SPs are diverse in sequence, sharing as little as 58% sequence identity to the closest known native signal peptide and 73% +/- 9% on average.

引用

页码：2154 / 2161

页数：8

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