Signal Peptides Generated by Attention-Based Neural Networks

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.