RubisCO.2 : Engineering new RubisCOs with increased CO2 catalytic activity
Description of 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 largely failed so far. The RubisCO.2 project aims to relax the existing constraints by starting from 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., Science (2020) ; Malbranke et al., Curr. Op. Struct. Biol. (2023)] and enzymatic RNA [Fernandez-de-Cossio-Diaz et al., Nat Comm (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, 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., ACS Synth Biol (2018)] and David Bikard (Synthetic Biology, Institut Pasteur) [Rochette et al. Nat Biotechnol (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 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.
Keywords
Photosynthesis, Catalytic activity, Protein design, Statistical Physics, Machine learning, Biochemistry
Research Unit, UMR number and acronym
ENS - Laboratory of Physics ENS, UMR8023, LPENS
Description of the research Unit/subunit
The DC hired on RubisCO.2 will take part in the team Statistical Physics and Inference for Biology (SPIB) team of the Laboratory of Physics of ENS-PSL (LPENS), at the interface between physics, AI and biology. SPIB members are working on computational and modeling aspects of complex biological matter and systems, ranging from molecular and cell biology to neuroscience and genomics, with a strong focus on data and collaborations with experimentalists. The DC will also benefit from the vibrant environment of the LPENS, a major physics laboratory in the Paris area, with about 100 permanent researchers, 130 PhD students and 80 post-doctorates. LPENS will offer the computing power and facilities (library, office space, ...) necessary for carrying out the PhD project.
Name of the supervisor
Simona COCCO (simona.cocco@ens.psl.eu)
Name of the co-supervisor
Rémi MONASSON (remi.monasson@ens.psl.eu)
3i Aspects of the proposal
Intersectoriality
RubisCO.2 integrates a strong intersectoral dimension through the collaborations with the groups at Institut Pasteur and in the Paris Biofoundry, a cutting-edge platform bridging academia and industry. P. Crozet and D. Bikard have proven track records in innovation/valorization as they founded Biomemory (DNA data storage, €22M raised), Neoplants (engineered plants for air quality), and Eligo Bioscience (gene-editing therapies). These startups demonstrate the consortium’s ability to translate fundamental research into applied biotechnology. In addition, the Cocco-Monasson team supervising the DC is also taking part to the PariSanté project, in which PSL is a key actor, with strong connections with the biomedical industry in the Paris area. Several alumni have already joined startups (e.g. Phagos) or large AI companies (Meta, Google). The project’s focus also strongly aligns with industrial interests in sustainable bioproduction and carbon fixation, opening avenues for partnerships with agri-tech or synthetic biology companies.
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., Science (2020) ; 10.1126/science.aba3304). This collaboration leverages Ranganathan’s pioneering work on epistasis, protein sequence-function relationships, and evolutionary landscapes—directly aligning with the project’s AI-driven protein design goals. The secondment will enable the student to integrate the computational approaches (statistical modeling of mutational effects) developed at LPENS and computational/experimental validation techniques developed in Ranganathan’s lab, enriching the PhD’s focus on predictive modeling for Rubisco optimization. This international exposure will broaden the student’s expertise, enhance the project’s innovative potential, and strengthen global collaborations in computational biology and synthetic evolution.
Interdisciplinarity
The RubisCO.2 project is highly interdisciplinary, seamlessly integrating physics, biochemistry, synthetic biology, and artificial intelligence to address a fundamental challenge in carbon fixation and photosynthesis. The project relies on physics-based tools and concepts (statistical physics models and simulations) and AI-driven design (generative models) to predicts and optimizes mutations capable of exploring Rubisco’s sequence-function landscape, in close connection with experiments done by biologists (directed evolution in Chlamydomonas, in vivo diversification with hypermutating systems). Structural biology (crystallography, synchrotron access) further bridges chemistry and biology to validate enzyme improvements. This convergence allows the project to transcend traditional boundaries : physics informs AI models, biology provides experimental data, and synthetic biology implements innovations. The iterative design-build-test-learn cycle exemplifies this synergy, where computational predictions guide experiments, and biological insights refine algorithms. Such integration is essential to overcome Rubisco’s evolutionary constraints.
Expected profile of the candidate
We seek a highly motivated DC with a strong background in physics, including statistical physics and theoretical soft matter, to contribute to the AI-driven design and analysis of Rubisco evolution. The candidate is expected to have acquired advanced training in artificial intelligence (generative models) and in data-driven modeling. Experience with programming and high-performance computing, e.g. through internships is essential. Familiarity with and/or interest for computational biology—such as sequence analysis, protein structure prediction, or evolutionary modeling—will be a significant asset, enabling the integration of experimental datasets into AI models. The ideal candidate will combine rigorous analytical skills with creativity to bridge physics-based modeling, AI, and biological data. Strong collaborative and communication skills are required to work effectively within this interdisciplinary project.
Important dates
Call for applications : from September 1st to October 31st 2026
Eligibility check results : November
3i Committee evaluation results : December
Interviews from the shortlisted candidates with the Selection Committee : January 2027
Start of the PhD : March 1st 2027