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Open Access Highly Accessed Research article

qPIPSA: Relating enzymatic kinetic parameters and interaction fields

Razif R Gabdoulline12, Matthias Stein1 and Rebecca C Wade1*

Author affiliations

1 Molecular and Cellular Modeling Group, EML Research gGmbH, Schloss Wolfsbrunnenweg 33, Heidelberg, 69118, Germany

2 BIOMS (Center for Modeling and Simulation in the Biosciences), University of Heidelberg, Im Neuenheimer Feld 368, Heidelberg, 69120, Germany

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Citation and License

BMC Bioinformatics 2007, 8:373  doi:10.1186/1471-2105-8-373

Published: 5 October 2007

Abstract

Background

The simulation of metabolic networks in quantitative systems biology requires the assignment of enzymatic kinetic parameters. Experimentally determined values are often not available and therefore computational methods to estimate these parameters are needed. It is possible to use the three-dimensional structure of an enzyme to perform simulations of a reaction and derive kinetic parameters. However, this is computationally demanding and requires detailed knowledge of the enzyme mechanism. We have therefore sought to develop a general, simple and computationally efficient procedure to relate protein structural information to enzymatic kinetic parameters that allows consistency between the kinetic and structural information to be checked and estimation of kinetic constants for structurally and mechanistically similar enzymes.

Results

We describe qPIPSA: quantitative Protein Interaction Property Similarity Analysis. In this analysis, molecular interaction fields, for example, electrostatic potentials, are computed from the enzyme structures. Differences in molecular interaction fields between enzymes are then related to the ratios of their kinetic parameters. This procedure can be used to estimate unknown kinetic parameters when enzyme structural information is available and kinetic parameters have been measured for related enzymes or were obtained under different conditions. The detailed interaction of the enzyme with substrate or cofactors is not modeled and is assumed to be similar for all the proteins compared. The protein structure modeling protocol employed ensures that differences between models reflect genuine differences between the protein sequences, rather than random fluctuations in protein structure.

Conclusion

Provided that the experimental conditions and the protein structural models refer to the same protein state or conformation, correlations between interaction fields and kinetic parameters can be established for sets of related enzymes. Outliers may arise due to variation in the importance of different contributions to the kinetic parameters, such as protein stability and conformational changes. The qPIPSA approach can assist in the validation as well as estimation of kinetic parameters, and provide insights into enzyme mechanism.