Screening and machine-learning assisted prediction of translation-enhancing peptides reducing ribosomal stalling in Escherichia coli

Abstract

We previously reported that the nascent SKIK peptide enhances translation and alleviates ribosomal stalling caused by arrest peptides (APs) such as SecM and polyproline when positioned immediately upstream of the APs in both Escherichia coli in vivo and in vitro translation systems. In this study, we performed a comprehensive screening of translation-enhancing peptides (TEPs) using a randomized artificial tetrapeptide library. The screening was based on the ability of the peptides to suppress SecM AP-induced translational stalling in E. coli cells. Various TEPs exhibiting a range of translation-enhancing activities were identified. In vitro translation analysis suggested that the fourth amino acid in the tetrapeptide plays a key role in reducing SecM AP-mediated stalling. Furthermore, we developed a machine learning model using a random forest algorithm to predict TEP activity. The predicted values showed a strong correlation with experimentally measured activities. These findings offer a compact peptide toolkit and a data-driven approach for mitigating AP-induced ribosome stalling, with potential applications in synthetic biology.