An aside from this week’s Unfinished Business.

Whilst writing yesterday’s post, I realised how often I (and I imagine many other modellers) resort to using “typical parameter values”, when no experimentally-determined numbers are available. If you read one of my papers, you’ll probably find a table listing these typical values and their provenance. But if you dig a little deeper, and follow these references back (all self-references, of course), you’ll eventually return to the paper you started with. Strange.

No, I don’t know where those numbers came from either. Still, we can derive them now using Brenda: a huge database of enzyme kinetic parameters. Database-level statistics in Brenda are presented as histograms; for example, there are over 10^{5} experimentally-determined Michaelis-Menten constants K_{M}, and we can use their statistics page to generate a histogram of the distribution of log_{10} K_{M}:

We can calculate the mean and standard deviation of log_{10} K_{M} from this curve, and then use a Taylor expansion to approximate the mean μ and standard deviation σ of K_{M}. This process can be applied to all the functional parameters in Brenda:

parameter | unit | μ | σ |

K_{M} |
mM | 0.96 | 0.52 |

K_{I} |
mM | 0.077 | 0.028 |

IC_{50} |
mM | 0.015 | 0.0068 |

pI | 6.0 | 1.6 | |

k_{cat} |
1/s | 27 | 14 |

specific activity | umol/min/mg | 0.34 | 1.9 |

pH optimum | 7.3 | 1.2 | |

temperature optimum | °C | 39 | 12 |

I’m pleased (OK: surprised) to see that my typical, typical parameter values of K_{M} = K_{I} = 0.1 mM, and k_{cat} = 10/s are close to the mark, though an order of magnitude estimate of K_{M} = 1 mM might be a better choice in the future.

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