A map of the rubisco biochemical landscape

Noam Prywes, Naiya R. Phillips, Luke M. Oltrogge, Sebastian Lindner, Leah J. Taylor-Kearney, Yi Chin Candace Tsai, Benoit de Pins, Aidan E. Cowan, Hana A. Chang, Renée Z. Wang, Laina N. Hall, Daniel Bellieny-Rabelo, Hunter M. Nisonoff, Rachel F. Weissman, Avi I. Flamholz, David Ding, Abhishek Y. Bhatt, Oliver Mueller-Cajar, Patrick M. Shih, Ron MiloDavid F. Savage*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Rubisco is the primary CO2-fixing enzyme of the biosphere1, yet it has slow kinetics2. The roles of evolution and chemical mechanism in constraining its biochemical function remain debated3,4. Engineering efforts aimed at adjusting the biochemical parameters of rubisco have largely failed5, although recent results indicate that the functional potential of rubisco has a wider scope than previously known6. Here we developed a massively parallel assay, using an engineered Escherichia coli7 in which enzyme activity is coupled to growth, to systematically map the sequence–function landscape of rubisco. Composite assay of more than 99% of single-amino acid mutants versus CO2 concentration enabled inference of enzyme velocity and apparent CO2 affinity parameters for thousands of substitutions. This approach identified many highly conserved positions that tolerate mutation and rare mutations that improve CO2 affinity. These data indicate that non-trivial biochemical changes are readily accessible and that the functional distance between rubiscos from diverse organisms can be traversed, laying the groundwork for further enzyme engineering efforts.

Original languageEnglish
Pages (from-to)823-828
Number of pages6
JournalNature
Volume638
Issue number8051
Early online date22 Jan 2025
DOIs
Publication statusPublished - 20 Feb 2025

Funding

We thank N. Antonovsky and A. Bar-Even for taking part in formulating the basis for this work, as well as N. Tepper and S. Amram for originally conceiving of and producing the Delta rpi strain, respectively. We thank P. Romero, N. Thompson, L. Fedotov, O. Saltzman, E. Prywes, S. Wyman, B. Yu and J. Desmarais for essential help in the process of data analysis. For their assistance in the process of generating and validating the DMS library, we thank A. Glazer, K. Matreyek, J. Bloom and K. Reynolds. Additionally, we thank J. Tartaglia for the use of her sequencing primers and N. Krishnappa for assistance in running NGS samples. We would like to thank E. Meng for assistance using ChimeraX. Finally, we thank F. Wang for technical assistance over the weekends. D.F.S. is an Investigator of the Howard Hughes Medical Institute. This work was supported by US National Institutes of Health grant no. K99GM141455-01 (N.P.) and the US Department of Energy, Physical Biosciences Program, award number DE-SC0016240 (D.F.S.).

All Science Journal Classification (ASJC) codes

  • General

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