Machine learning-assisted rational design and evolution of novel signal peptides in .

Microbial proteins hold great promise as sustainable alternatives for future protein sources, and oleaginous yeast has emerged as a recognized platform for heterologous protein expression and secretion. N-terminal signal peptides (SPs) are crucial for directing proteins to the secretion pathway, which offers advantages in both academic and industrial protein production. Although some of the innate SPs of . have been reported, there is a growing need to expand the genetic toolkit of SPs to support the increasing use of as a cell factory for overproduction of various secretory proteins. In this study, we employed an efficient evolutionary approach to rapidly evolve the innate SP -pre by leveraging Gibson assembly with two synthetic overlapping oligos containing high portion of degenerate nucleotides. Using Nanoluc () luciferase as a robust reporter, we characterized the intracellular and extracellular enzymatic activity of 447 SP mutants and identified previously undescribed SPs exhibiting superior performance compared to -pre in luciferase secretion, with improvements of up to 2.91-fold of enzymatic activity in the supernatant. The generalizability of the top-performing SPs was evaluated using three additional heterologous enzymes (β-galactosidase, α-amylase, and PET hydrolase). Our results confirmed their versatility across different proteins with protein-specific efficiency. Additionally, based on our screening, we also evaluated the performance of different feature engineering strategies and machine learning models in the design and prediction of SP mutants. This study integrated rational design, directed evolution and machine learning to identify novel SPs, expanding the repertoire of signal peptides and benefiting secretory protein overexpression in . .