RubisCO.2 - Engineering new RubisCOs with increased CO2 catalytic activity

RubisCO.2
- Engineering new RubisCOs with increased CO2 catalytic activity

20/08/2026 Financement de l'Union européenne

RubisCO.2
- Engineering new RubisCOs with increased CO2 catalytic activity

Photosynthesis, Catalytic activity, Protein design, Statistical Physics, Machine learning, Biochemistry

PRISM programme

The PRISM (PhD Research Programme for International Training in Sustainable Soft Matter) programme has launched its first call for applications, offering up to 14 fully funded PhD fellowships starting from 1 March 2027 at Paris Sciences & Lettres (PSL) University. The programme trains researchers to address ecological transition challenges through sustainable soft matter science, with projects focused on eco-friendly chemical processes, circular economy, renewable energies, and carbon capture, storage, and valorisation. Co-funded by the European Union under Horizon Europe MSCA COFUND (Grant Agreement 101261637) and partner institutions, PRISM provides interdisciplinary, international, and intersectoral training, including mobility opportunities, secondments, and courses in sustainability, innovation, entrepreneurship, career development, and transferable skills.

The PhD project

Context: Photosynthetic carbon fixation is limited by the inefficiency of the RubisCO protein, at the core of the Calvin-Benson cycle. Empirical attempts to enhance efficiency by modifying current RubisCO’s through mutations have not been successful so far. The RubisCO.2 project aims to avoid the strong constraints in current RubisCO proteins by considering putative ancestral proteins, thought to be much more flexible [Schulz et al., Science (2022)]. New and efficient RubisCO’s will then be designed, using statistical-physics modeling, generative AI and in vivo directed evolution. This PhD will take place at LPENS in the Cocco-Monasson team, with a track record on the design of protein [Russ et al. (2020); Malbranke et al. (2023)] and enzymatic RNA [Fernandez-de-Cossio-Diaz et al. (2025)], in close collaboration with partners in biochemistry and biology labs in Sorbonne Université and Institut Pasteur. This strongly interdisciplinary consortium combines the expertise required for such a challenging project, and offers great opportunities to the DC for adapting the project across the 3-year duration of the PhD.

Objectives: The DC will develop predictive models to navigate RubisCO’s sequence-function landscape and improve its catalytic performance. The models will be both physics-grounded and data-driven, combining bio-physical/chemical information, and biological sequence and structure data to capture large-scale evolutionary constraints and propose novel, functional variants of RubisCO. Models will also integrate experimental datasets from algal evolution, which estimate mutational effects on stability and catalytic efficiency. Using these models, the DC will infer ancestral protein sequences [Thornton et al. (2004)], and then identify mutation combinations that will bypass evolutionary bottlenecks and allow for changing the balance between CO₂/O₂ specificity and catalytic speed.

Connections with experiments: The DC will implement interpretable AI models to propose de novo RubisCO designs, prioritizing variants for experimental validation. Collaboration with Pierre Crozet (CQSB, SU and Paris Biofoundry) [Crozet et al. (2018)] and David Bikard (Synthetic Biology, Institut Pasteur) [Rochette et al. (2026)] will allow the DC to test these AI-designed variants in vivo in the Chlamydomonas algae, a model organism for photosynthesis, and to use the results to retrain models and improve predictive accuracy. A secondment in R. Ranganathan’s lab in Chicago will enable the student to incorporate advanced methods for epistasis mapping and evolutionary landscape analysis, enriching the PhD’s computational toolkit. The connections with the experimental partners, all involved in startup creation, will be instrumental in exposing the DC to valorization environments.

Expected outcome: This PhD will bridge Physics-Informed AI models and experiments at the interface of physics, biochemistry, and engineering. The project will produce predictive frameworks for RubisCO performance, linking sequence to function, as well as novel RubisCO variants with improved catalytic properties, validated experimentally. We expect the acquired expertise on AI-guided enzyme design to be very valuable on the job market after completion of the PhD, either in an industrial or academic context.

INTERNATIONAL: The RubisCO.2 PhD project embodies a strong international dimension through its planned 3-month secondment of the DC to R. Ranganathan’s laboratory at the University of Chicago, a renowned expert in protein evolution and computational biology and a long-standing collaborator of the Cocco-Monasson group at LPENS [Russ et al. (2020)]. This collaboration leverages