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• W4386136977 abstract "Abstract Understanding how to tune enzymatic activity is important not only for biotechnological applications, but also to elucidate the basic principles guiding the design and optimization of biological systems in nature. So far, the Michaelis-Menten equation has provided a fundamental framework of enzymatic activity. However, there is still no concrete guideline on how the parameters should be optimized towards higher activity. Here, we demonstrate that tuning the Michaelis-Menten constant ( $${K}_{m}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> </mml:math> ) to the substrate concentration ( $$[{{{{{rm{S}}}}}}]$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mo>[</mml:mo> <mml:mrow> <mml:mi>S</mml:mi> </mml:mrow> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> ) enhances enzymatic activity. This guideline ( $${K}_{m}=[{{{{{rm{S}}}}}}]$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>[</mml:mo> <mml:mrow> <mml:mi>S</mml:mi> </mml:mrow> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> ) was obtained mathematically by assuming that thermodynamically favorable reactions have higher rate constants, and that the total driving force is fixed. Due to the generality of these thermodynamic considerations, we propose $${K}_{m}=[{{{{{rm{S}}}}}}]$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>[</mml:mo> <mml:mrow> <mml:mi>S</mml:mi> </mml:mrow> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> as a general concept to enhance enzymatic activity. Our bioinformatic analysis reveals that the $${K}_{m}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> </mml:math> and in vivo substrate concentrations are consistent across a dataset of approximately 1000 enzymes, suggesting that even natural selection follows the principle $${K}_{m}=[{{{{{rm{S}}}}}}]$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>[</mml:mo> <mml:mrow> <mml:mi>S</mml:mi> </mml:mrow> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> ." @default.
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• W4386136977 date "2023-08-24" @default.
• W4386136977 modified "2023-10-02" @default.
• W4386136977 title "Thermodynamic principle to enhance enzymatic activity using the substrate affinity" @default.
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• W4386136977 doi "https://doi.org/10.1038/s41467-023-40471-y" @default.
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