Valuable mutants of catechol 1,2-dioxygenase from Acinetobacter radioresistens S13 were produced with improved clorocatechols turnover and a change in specifity(1). The aim of the present project is to join the experimental results with a computational strategy in order to be able to predict the effect of novel mutagenesis. In particular we are interested in highlighting what kind of point mutations could create the most suitable environment for some recalcitrant substrates as 4,5-dichlorocatechols and 3,5-dichlorocatechols. Starting from the WT structure, the models were built with MOE for each characterized mutation; the side chain possible conformations are explored with the MOE rotamer library so that the mutation is only local. The models were superimposed to perform the analysis with GRID: energetically favorable binding sites are determined and the property distribution of interaction forces between a probe and the active site is designed. Probes are chosen to investigate the hydrophobic interaction and the pattern of hydrogen bonds. Also steric effect (with methyl probe) and interaction with chlorine atom (to investigate the affinity of active side residues with chlorinated probe) are taken into account. An overall map of interactions stabilizing the active site and driving the recognition of a substrate become available. The grid points are filtered according to an energy threshold fixed by BIOCUBE4mf so that the differences between mutations were underlined. To validate the computational predictive map, a comparison with experimental results is necessary. Among all the available experimental values - kcat, Ea and KM - the Michaelis-Menten constant seems the one that best relates to the enzyme affinity on a particular substrate. The interaction forces map was compared to KM changes respect to WT for each mutant. This study showed that the hydrophobic contribution only, even if required to keep the suitable environment for the catalysis, is not discriminant to evaluate important differences between the enzymes. It is necessary to combine all the information that the probes give. In this way it is possible to design a preliminary map on the chemical environment that characterizes a protein active site. This allows to make a prediction on which mutation may be occur in order to favor or not the displacement of a substrate in that cavity.

A computational strategy to predict recognition and catalysis on recalcitrant chlorinated substrates by a catechol1,2 dioxygenase.

ROSSO, CECILIA;CARON, Giulia;ERMONDI, Giuseppe;GIUNTA, Carlo;GILARDI, Gianfranco;VALETTI, Francesca
2010

Abstract

Valuable mutants of catechol 1,2-dioxygenase from Acinetobacter radioresistens S13 were produced with improved clorocatechols turnover and a change in specifity(1). The aim of the present project is to join the experimental results with a computational strategy in order to be able to predict the effect of novel mutagenesis. In particular we are interested in highlighting what kind of point mutations could create the most suitable environment for some recalcitrant substrates as 4,5-dichlorocatechols and 3,5-dichlorocatechols. Starting from the WT structure, the models were built with MOE for each characterized mutation; the side chain possible conformations are explored with the MOE rotamer library so that the mutation is only local. The models were superimposed to perform the analysis with GRID: energetically favorable binding sites are determined and the property distribution of interaction forces between a probe and the active site is designed. Probes are chosen to investigate the hydrophobic interaction and the pattern of hydrogen bonds. Also steric effect (with methyl probe) and interaction with chlorine atom (to investigate the affinity of active side residues with chlorinated probe) are taken into account. An overall map of interactions stabilizing the active site and driving the recognition of a substrate become available. The grid points are filtered according to an energy threshold fixed by BIOCUBE4mf so that the differences between mutations were underlined. To validate the computational predictive map, a comparison with experimental results is necessary. Among all the available experimental values - kcat, Ea and KM - the Michaelis-Menten constant seems the one that best relates to the enzyme affinity on a particular substrate. The interaction forces map was compared to KM changes respect to WT for each mutant. This study showed that the hydrophobic contribution only, even if required to keep the suitable environment for the catalysis, is not discriminant to evaluate important differences between the enzymes. It is necessary to combine all the information that the probes give. In this way it is possible to design a preliminary map on the chemical environment that characterizes a protein active site. This allows to make a prediction on which mutation may be occur in order to favor or not the displacement of a substrate in that cavity.
PROTEINE 2010
Parma
8-10 aprile 2010
-
Università di Parma
-
P-87
P-87
catechol 1; 2 dioxygenase; computational method.; active site engineering; substrate recognition; bioremediation
Cecilia Rosso; Giulia Caron; Giuseppe Ermondi; Carlo Giunta; Gianfranco Gilardi; Francesca Valetti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/74757
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