🌱 MIT significantly improves photosynthesis efficiency through directed evolution

Researchers at MIT have made a major breakthrough in optimizing rubisco, a key enzyme in photosynthesis. By modifying a bacterial version of this molecule, they achieved a 25% higher catalytic activity, opening promising prospects for agriculture.

Rubisco: a weak link in photosynthesis

Rubisco converts atmospheric CO₂ into sugars, a crucial step in plant metabolism. However, it suffers from limited efficiency: its slowness and sensitivity to oxygen cause plants to lose energy, reducing their yield.

Previous attempts to improve this enzyme had yielded few results. The MIT team took a different approach: continuous directed evolution, a method capable of exploring a wide range of mutations.

The researchers chose to work on a bacterial version of rubisco, known for its speed but poorly adapted to an oxygen-rich environment. By applying their method, they obtained mutations that improve CO₂ selectivity, limiting unnecessary reactions with oxygen.

The mutations affect regions near the enzyme's active site, altering its behavior without changing its basic structure. This result serves as a proof of concept for future applications in plants.

Towards more productive and water-efficient plants

The next goal is to adapt this strategy to plant rubisco, which is far more complex. An optimized version could reduce photorespiration, an energy-intensive process triggered when the enzyme captures oxygen instead of CO₂.

In the long term, this breakthrough could increase agricultural yields or reduce water requirements—critical challenges in our era of climate change.

Zoom: how does directed evolution work?

Directed evolution simulates natural selection in the lab. It randomly generates thousands of mutations in a given gene, then isolates the most effective variants.

The MIT team used an automated version of this technique, called MutaT7, which speeds up the process by working directly in living cells. This method avoids in vitro manipulation steps and enables very rapid optimization cycles.

This approach could be extended to other enzymes of interest for biotechnology, health, or the environment.