Rubisco is slow across the tree of life

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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. Significance Discovering a fast carboxylating rubisco has been a long-standing challenge in the scientific community, given its potential impact on sustainable food and fuel production. Yet, only a small fraction of rubisco’s natural diversity has been kinetically characterized. Here, we present a large-scale kinetic survey covering the entire spectrum of rubisco’s diversity found in nature. Focusing on genetic clusters with above-average rates, we show that rubisco’s catalytic rate does not exceed ≈30 reactions per second at 30°C. Supported by a machine-learning predictive model, we extend this finding to all sequenced natural variants. Our study provides the most comprehensive kinetic dataset for a single enzyme to date.

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