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arXiv · 1007.4461

Cross-species analysis traces adaptation of Rubisco towards optimality in a low dimensional landscape

Yonatan Savir·Elad Noor·Ron Milo·Tsvi Tlusty
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Abstract

Rubisco, probably the most abundant protein in the biosphere, performs an essential part in the process of carbon fixation through photosynthesis thus facilitating life on earth. Despite the significant effect that Rubisco has on the fitness of plants and other photosynthetic organisms, this enzyme is known to have a remarkably low catalytic rate and a tendency to confuse its substrate, carbon dioxide, with oxygen. This apparent inefficiency is puzzling and raises questions regarding the roles of evolution versus biochemical constraints in shaping Rubisco. Here we examine these questions by analyzing the measured kinetic parameters of Rubisco from various organisms in various environments. The analysis presented here suggests that the evolution of Rubisco is confined to an effectively one-dimensional landscape, which is manifested in simple power law correlations between its kinetic parameters. Within this one dimensional landscape, which may represent biochemical and structural constraints, Rubisco appears to be tuned to the intracellular environment in which it resides such that the net photosynthesis rate is nearly optimal. Our analysis indicates that the specificity of Rubisco is not the main determinant of its efficiency but rather the tradeoff between the carboxylation velocity and CO2 affinity. As a result, the presence of oxygen has only moderate effect on the optimal performance of Rubisco, which is determined mostly by the local CO2 concentration. Rubisco appears as an experimentally testable example for the evolution of proteins subject both to strong selection pressure and to biochemical constraints which strongly confine the evolutionary plasticity to a low dimensional landscape.

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Yonatan Savir, Elad Noor, Ron Milo, Tsvi Tlusty. 2010-07-26. Cross-species analysis traces adaptation of Rubisco towards optimality in a low dimensional landscape. https://doi.org/10.1073/pnas.0911663107

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