Quantum Biology to Enhance CO2 Fixation

Lead Research Organisation: University of Nottingham
Department Name: Sch of Chemistry

Abstract

Despite the billions of tonnes of CO2 photosynthetic organisms fix every year, they do not have the capacity to absorb the quantities released into the atmosphere from human combustion of fossil fuels for energy. Progress must be made to assist carbon fixing autotrophs. One potential method could be the creation novel fixation pathways which, with further manipulation, could produce value-added multi-carbon products using CO2 as the feedstock. This would be a huge step towards a true sustainable, circular economy.

RuBisCo, the most abundant enzyme on the planet, is the carbon fixing enzyme in the Calvin-Benson cycle and fixes the majority of CO2. It allows plants to synthesise the multimeric carbon molecules they need for growth using carbon dioxide as the most basic building block. Unfortunately, reaction rates are very low and are further hampered by a side reaction with O2.

In a bid to speed up the carbon fixation process, Schwander et al., developed the CETCH cycle: the first synthetic carbon fixing cycle based around a recently discovered class of carboxylases which have the fastest reaction rates of all known carboxylases. Using this cycle, CO2 can be fixed up to 40 times faster than by using the Calvin-Benson cycle. To achieve this, some of the enzymes have been engineered to ensure continuous cycling and eliminate deleterious side products. We propose to build on this work by analysing the enzymes in the cycle and rationally improving them using machine learning.

Innovations such as these could be useful to the bioeconomy providing technology to use CO2 as a feedstock to produce useful chemicals. Combining the cycle with a photosynthetic energy source means that the whole process could be solar powered; a prototype has been demonstrated to have potential by Miller et al.

Proposed solution and methodology
We propose to develop a systematic workflow to characterise enzymes, glean insights into their mechanisms and then apply machine learning to suggest beneficial mutations in a data-driven manner. This workflow will be developed and tested using enzymes from the CETCH cycle in the hopes of contributing to the field of carbon fixation.

Characterising the target enzyme will give insights into the mechanism which will assist with the enhancement of enzymes by rational design. This can be achieved with a combination of molecular dynamics (MD) simulations and hybrid quantum mechanics/molecular mechanics (QM/MM) methods which predict what conformations are sampled by the enzymes and where interactions occur.

To assess the contribution of co-factors and the surrounding environment to reactivity and stability MD simulations will be performed. Quantum chemical models will be used to determine the feasibility of the reaction independently providing a baseline for how the reaction is likely to progress. These calculations also allow interrogation of the active site particularly in terms of bonds breaking and forming and how specific residues contribute to the mechanism.

The hybrid QM/MM model will allow for a deeper exploration of the reaction in the context of the entire enzyme. Reactivity descriptors will be derived from this model to provide metrics that will be used to develop an artificial intelligence model for the prediction of directed changes to improve enzyme function. The resultant mutants can then be subjected to the same methods as the wild type protein to assess stability and changes to enzyme efficiency.

Success will provide a novel method to design enzymes in silico for any challenge. Leaving the rational design for a computer to discover may throw up curious, previously unconsidered proposals. As a model system, the CETCH cycle for carbon fixation will be used as an enzyme source due to its importance to synthetic carbon fixation progress and potential applications in the bioeconomy.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022236/1 01/10/2019 31/03/2028
2284925 Studentship EP/S022236/1 01/10/2019 30/09/2023 Jennifer Lucy Hall