Abstract
Terpenoids are the most diverse class of plant primary and specialized metabolites, and trans-prenyltransferases (trans-PTs) are the first branch point to synthesize precursors of various chain lengths for further metabolism. Whereas the catalytic mechanism of the enzyme is known, there is no reliable method for precisely predicting the functions of trans-PTs. With the exponentially increasing number of available trans-PTs genes in public databases, an in silico functional prediction method for this gene family is urgently needed. Here, we present PTS-Pre, a web tool developed on the basis of the “three floors” model, which shows an overall 86% prediction accuracy for 141 experimentally determined trans-PTs. The method was further validated by in vitro enzyme assays for randomly selected trans-PTs. In addition, using this method, we identified nine new GFPPSs from different plants which are beyond the previously reported Brassicaceae clade, suggesting these genes may have occurred via convergent evolution and are more likely lineage-specific. The high accuracy of our blind prediction validated by enzymatic assays suggests that PTS-Pre provides a convenient and reliable method for genome-wide functional prediction of trans-PTs enzymes and will surely benefit the elucidation and metabolic engineering of terpenoid biosynthetic pathways.
Funder
Science and Technology Commission of Shanghai Municipality
National Natural Science Foundation of China
Subject
Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis
Cited by
1 articles.
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