Transfer Learning for Designing Efficient Signal Peptides to Improve the Secretion Level of Recombinant Protein in Bacillus amyloliquefaciens

Signal peptides (SPs) play an essential role in determining the secretion efficiency of proteins of interest (POIs). However, the manual identification of SPs with a high secretion potential is both time-consuming and labor-intensive. Recently, many advanced machine learning (ML) techniques have emerged in biology and food research. This research aimed to utilize experimental SP-POI secretion data to create ML models that could predict how SPs influence the POI secretion efficiency. Given the limitations of the available data, which affected model accuracy, this study introduced transfer learning and confirmed its effectiveness through model selection experiments, leading to the development of more precise ML models. Utilizing the SP generator and ML models developed, high-quality SPs were successfully designed. Experimental validation confirmed that 80% of ML-designed SPs secreted the POI, with 60% achieving high-level secretion.