期刊
ACS SYNTHETIC BIOLOGY
卷 9, 期 8, 页码 2154-2161出版社
AMER CHEMICAL SOC
DOI: 10.1021/acssynbio.0c00219
关键词
machine learning; signal peptides; protein design; Bacillus subtilis; secretion
资金
- BASF through the California Research Alliance (CARA)
- National Science Foundation Division of Chemical, Bioengineering, Environmental and Transport Systems [CBET-1937902]
- National Science Foundation Graduate Fellowship [GRF2017227007]
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.
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