Plasmids constitute a key tool in synthetic biology, providing a versatile framework for various research and industrial ventures. An essential determinant of plasmid functionality is its copy number, impacting both protein production rates and host cell metabolic burden. However, currently there is no model that can computationally predict the plasmid copy number from the sequence of its origin of replication (ORI). We present a novel software solution tailored to simplify plasmid copy number design, poised to redefine plasmid engineering workflows. At the heart of our tool lies a comprehensive machine learning model, informed by numerous features extracted from the ORI. This computational model emphasizes the importance of promoter strength and the RNA folding dynamics of regulatory elements within the ORI. Additionally, we detail a robust protocol for the efficient manipulation of plasmid ORIs used to validate our model's predictive capabilities. This innovation represents a paradigm shift in plasmid-centric methodologies, offering unprecedented avenues for advancement in synthetic biology research and industrial applications. The software is available at: https://pcn-gradient.vercel.app/.

ColE1-like plasmids; gene expression; machine learning; plasmid copy number; plasmid replication; gene expression; machine learning; plasmid copy number; plasmid replication