A Deep-Learning-Based Server SecEff-Pred for Predicting Signal Peptide Secretion Efficiency to Improve Protein Production in Bacillus subtilis.

In Bacillus species, the signal peptide (SP) efficiently guiding the protein secretion is crucial for production, yet there is still a lack of reliable computational tools to accurately predict its efficiency. Therefore, we developed SecEff-Pred, a novel web server that leverages an ESM-2-based predictor. Enhanced by an innovative data simulation strategy and a multitask learning framework, SecEff-Pred accurately predicted the secretion efficiency of signal peptides in Bacillus subtilis. The server demonstrated exceptional performance, achieving prediction accuracies of 85.59% for α-amylase, 81.58% for alkaline xylanase, and 74.68% for cutinase. SecEff-Pred was further validated using phospholipase D (PLD) as a reporter protein, demonstrating high prediction accuracy for signal peptide efficiency (overall 72%), with an accuracy of 80% for "efficient" SPs (corresponding to a maximum PLD activity of 929 U/mL) and 62.50% for "inefficient" SPs. These results confirm the SecEff-Pred is a powerful tool for guiding protein secretion in B. subtilis.