bioRxivBiochemistry
Structural elements that modulate the substrate specificity of plant purple acid phosphatases: avenues for improved phosphorus acquisition in crops
D. Feder, R. P. McGeary, N. Mitic, T. Lonhienne, A. Furtado, B. L. Schulz, R. J. Henry, S. Schmidt, L. W. Guddat, G. Schenk
Abstract
Phosphate acquisition by plants is an essential process that is directly implicated in the optimization of crop yields. Purple acid phosphatases (PAPs) are ubiquitous metalloenzymes, which catalyze the hydrolysis of a wide range of phosphate esters and anhydrides. While some plant PAPs display a preference for ATP as the substrate, others are efficient in hydrolyzing phytate or 2-phosphoenolpyruvate (PEP). PAP from red kidney bean (rkbPAP) is an efficient ATP- and ADPase, but has no activity towards phytate. The crystal structure of this enzyme in complex with an ATP analogue (to 2.20 [A] resolution) provides insight into the amino acid residues that play an essential role in binding this substrate. Homology modelling was used to generate three-dimensional structures for the active sites of PAPs from tobacco (NtPAP) and Arabidopsis thaliana (AtPAP12 and AtPAP26) that are efficient in hydrolyzing phytate and PEP as substrates, respectively. In combination with substrate docking simulations and a phylogenetic analysis of 49 plant PAP sequences (including the first PAP sequences reported from Eucalyptus), several active site residues were identified that are important in defining the substrate specificities of plant PAPs. These results may inform bioengineering studies aimed at identifying and incorporating suitable plant PAP genes into crops to improve phosphorus use efficiency. Organic phosphorus sources increasingly supplement or replace inorganic fertilizer, and efficient phosphorus use of crops will lower the environmental footprint of agriculture while enhancing food production.
- organism: plant (other)
- assay: protein structure
- research focus: protein folding & dynamics
- approach: experimental assay
- approach: statistical model
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