In a new paper, researchers say they have condensed the time-intensive protein building and testing process to just 24 hours. The team, from Stanford University, call their approach MIDAS for Microbe-Independent Deep Assembly and Screening. MIDAS could rapidly accelerate biological research in fields stretching from oncology to environmental sciences.
In traditional protein engineering, when researchers identify a promising variant, they have to assemble and clone the gene expressing the protein into a circular genetic structure known as a plasmid. They must then transfer the modified plasmids into the DNA of bacteria or yeast to produce suitable quantities of each unique plasmid DNA, which must then be transferred into mammalian cells for validation.
This clone-and-transfer process is laborious, slow, expensive, and it greatly restricts the number of variants that can feasibly be evaluated. MIDAS changes that calculus. The team’s key insight was to do away with the circular plasmids, which are incompatible with PCR. Instead, they treat DNA as linear information that is ideally suited to PCR. This allows them to assemble hundreds of gene variants at a time and directly transfer them into mammalian cells in quantity to identify the best performers quickly and cost-effectively.
A practical test of 384 variants using MIDAS took about four hours of hands-on lab work and about $2,000 in reagents. By existing methods, an experienced researcher would need approximately 192 hours and about $20,000 in reagents to evaluate just 24 variants. The researchers calculate that MIDAS is almost 50x faster and 1/10 of the cost of cloning-based approaches.
MIDAS could have immediate real-world implications for biological research. First, it should accelerate important enzyme and biosensor studies, the researchers say. Second, it could improve the automatic production of PCR primers that are ideally suited to modern liquid-handling robots, which can evaluate hundreds of new proteins at a time. Last, and perhaps most importantly, they believe MIDAS could drive better and bigger sequence-fitness datasets that could improve data-intensive AI training, leading to ever more powerful molecular design models.
Looking forward, the team believes MIDAS could yield deeper combinatorial searches, tighter integration with robotics, and the generation of gene sequence-molecular fitness maps to feed improved machine-learning models that can fuel computational design and experimental validation.
Data from Stanford University