About This Project

To mitigate climate change, this proposal aims to enhance CO2 removal efficiency of C3 plants by engineering carbon concentrating mechanisms through a deep learning, molecular modeling, and experimental approach. The goal is to design proteins that optimize the efficacy of Rubisco and carbonic anhydrase enzymes. The project represents a new framework to design carbon concentrating mechanisms.

Ask the Scientists

What is the context of this research?

Successful climate change mitigation through net-zero carbon strategies requires gigatons of CO₂ removed per year. To achieve this goal, a minimum of 100-fold increase in CO₂ removal efficiency compared to present technologies is required. Plants are a natural, biological avenue for carbon capture and utilization as they remove CO₂ through photosynthesis. Rubisco, the carbon fixing enzyme, is the most abundant protein on Earth; however, it is a slow and inefficient enzyme because 1 of every 4 turnovers uses O₂ instead of CO₂. Despite numerous attempts to engineer Rubisco itself to improve both the turnover rate (speed) and selectivity, it seems that humanity cannot outdo evolution. Other approaches to increase the productivity of Rubisco are needed to increase the rate of carbon fixation.

What is the significance of this project?

Photosynthetic organisms, including cyanobacteria and algae, have evolved to thrive under low-CO₂ conditions through carbon concentrating mechanisms (CCMs). Prior efforts to improve the carbon utilization by plants have focused on transplanting the CCM machinery from either cyanobacteria or algae into plants. These studies have demonstrated that it is possible to express non-native CCM proteins, such as Rubisco from algae, but significant strides are still needed to improve carbon fixation efficacy. An alternate approach is to engineer CCM-inspired proteins that are adapted specifically to C3 plant Rubisco and carbonic anhydrase (CA), which is another critical enzyme involved in CCM. In doing so, it is likely that CCM can be synthetically induced in plants for improved CO₂ fixation.

What are the goals of the project?

The vision is to reduce accidental fixation of O2 instead of CO2 by Rubisco, a process known as photorespiration, using protein engineering strategies inspired by aquatic single-cell organisms, such as algae and cyanobacteria, to concentrate CO2 near Rubisco. We hypothesize that deep learning methods for protein structure prediction can be leveraged with computational molecular modeling and experimental assays to create an efficient funneling pipeline to engineer CCM-inducing proteins in C3 plants. Through the introduction of our synthetic CCM machinery, the goal is to increase plant CO2 utilization by two-fold under ambient or low CO2 concentrations. This research will serve as a proof of concept for computationally-aided design of synthetic CCMs in plants.