Microfluidic Fluorescence-Activated Cell Sorting of Convolutional Neural Network-Designed Synthetic Yeast Promoter

The artificial design and high-throughput screening (HTS) of synthetic yeast promoters are crucial for gene expression and metabolite biosynthesis in synthetic biology. It is essential to expand the screening scope and enhance the product identification in promoter engineering. Here, we develop a branch convolutional neural network (B-CNN), learning the sequence composition and structural properties of natural promoters, to comprehensively predict their relative strength and redesign the core region. It is combined with a genetic algorithm to identify mutant regions/sites of the alcohol oxidase 1 promoter (P_AOX1) from Pichia pastoris (P. pastoris). Consecutive fluorescent recognition and automatic sorting of the GFP-expressing library of core P_AOX1 were finished in a laboratory-designed microfluidic fluorescence-activated cell sorting (μFACS) system. The rigid μFACS chip was fabricated, achieving single-cell sorting and meeting the requirement of complicated sterilization with reduced volumes and improved cell throughput (7000 cells/s). After extensive exploration of the sorting parameters, P. pastoris with high-intensity P_AOX1 (5.3× improvement) was successfully screened and validated by a microplate reader. The mutant P_AOX1 was further applied for the biosynthesis of PD-L1-specific protein with a 30% increase in production, proving its effectiveness and feasibility. This study provided novel insights into the rational design and HTS of yeast functional components with enhanced performance.