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Publication Abstract from PubMed

O-methylation of flavonoid compounds is an important enzymatic reaction since it not only reduces the chemical reactivity of their phenolic hydroxyl groups but also increases their lipophilicity and, hence, their intracellular compartmentation. Several genes encoding flavonoid O-methyltransferases (OMTs) have been isolated and characterized both at the molecular and biochemical levels. In contrast with mammalian enzymes, plant OMTs exhibit narrow substrate specificities as well as position-specific activities, so that the homology comparison, derived using programs such as BLAST can not provide sufficient information on the enzyme function or its substrate preference. In order to study these characteristics, therefore, another approach, homology-based modelling is being carried out. We report here the determination of the 3-D structure of Arabidopsis thaliana O-methyltransferase, AtOMT1 as well as its dynamics when complexed with its substrate. The predicted structure obtained by homology-based modelling is conserved during molecular dynamics simulations. AtOMT1 exhibits a structure similar to that of caffeic acid O-methyltransferase, COMT when the latter was used as a template. Whereas COMT includes 20 alpha-helices and nine beta-sheets, AtOMT1 has 16 and 9, respectively. Although the homology between both proteins is higher than 77% and all amino acids surrounding the active sites, except one residue, are similar in their primary sequences, the two proteins exhibit different substrate preferences. The differences in substrate specificity may be explained on the basis of the predicted structures of the protein and its complex with the substrate. In addition, docking the substrate into the active site of the protein allowed the study of the structural change of the active site on the dihedral angle distribution of the residues surrounding the active site.

The three-dimensional structure of Arabidopsis thaliana O-methyltransferase predicted by homology-based modelling.,Yang H, Ahn JH, Ibrahim RK, Lee S, Lim Y J Mol Graph Model. 2004 Sep;23(1):77-87. PMID:15331056^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.