Rubisco is slow across the tree of life

B Benoit de Pins C Cyril Malbranke (Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne) J Jagoda Jabłońska (Department of Plant and Environmental Sciences, Weizmann Institute of Science) A Assaf Shmuel (Department of Plant and Environmental Sciences, Weizmann Institute of Science) I Itai Sharon (Migal Galilee Research Institute) A Anne-Florence Bitbol (Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne) O Oliver Mueller-Cajar E Elad Noor (Department of Plant and Environmental Sciences, Weizmann Institute of Science) R Ron Milo

Abstract

Rubisco is the main gateway through which inorganic carbon enters the biosphere, catalyzing the vast majority of carbon fixation on Earth. This pivotal enzyme has long been observed to be kinetically constrained. Yet, this impression is based on kinetic measurements heavily focused on eukaryotic rubiscos, a rather conserved group of low genetic diversity. Moreover, the fastest rubiscos that we know of so far were found among the sparsely sampled prokaryotes. Could there be yet faster rubiscos among the uncharted regions of rubisco’s phylogenetic diversity? Here, we perform a characterization of more than 250 rubiscos from a wide range of bacteria and archaea, thereby doubling the coverage of the diversity of this key enzyme. We assess the distribution of the carboxylation rates at saturating levels of CO 2 , and establish that rubisco is a relatively slow enzyme across the tree of life, never exceeding ≈30 reactions per second at 30 °C. We show that relatively faster subclades share similar evolutionary contexts, involving micro-oxygenic environments or a CO 2 concentrating mechanism. Leveraging a simple machine learning model trained on this dataset, we predict the carboxylation rate for all ≈68,000 sequenced rubisco variants found in nature to date. This study provides the largest and most diverse dataset of natural variants for an enzyme and their associated rates, establishing a solid benchmark for future efforts to predict catalytic rates from sequence data.

Article Details

Volume / Issue Vol. 122, Issue 47
Published November 25, 2025
ISSN 0027-8424
Publisher National Academy of Sciences

Authors (9)

B

Benoit de Pins

C

Cyril Malbranke

Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne

J

Jagoda Jabłońska

Department of Plant and Environmental Sciences, Weizmann Institute of Science

A

Assaf Shmuel

Department of Plant and Environmental Sciences, Weizmann Institute of Science

I

Itai Sharon

Migal Galilee Research Institute

A

Anne-Florence Bitbol

Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne

O

Oliver Mueller-Cajar

E

Elad Noor

Department of Plant and Environmental Sciences, Weizmann Institute of Science

R

Ron Milo