Many of the most valuable, yet complex chemicals in society are obtained from bacteria, who use giant, multimodular acyltransferase polyketide synthase (AT PKS) enzyme complexes to make these products. The modular nature of these enzymes holds the promise to engineer biosynthetic assembly lines to produce new, societally relevant products in a benign and sustainable manner. However, the chemical functionalities installed by the textbook cis-AT PKSs is mostly limited to several basic moieties.
In contrast, in a second class of PKSs, trans-AT PKSs, over 150 different module types have been identified, yet with many more still uncharacterized. Initial results show that bioinformatically-guided approaches can be effective ways to assign the functionality of these uncharacterized modules, but only individual examples have been studied. To catalyze the discovery of new functionality in trans-AT PKSs, I propose PrediKSion: a comprehensive, evolution-guided and experimentally validated computational pipeline to unravel the hidden chemical functionality of unassigned trans-AT PKS modules.
PrediKSion will facilitate unbiased discovery of new module functionalities by looking at the phylogeny of ketosynthases (KSs) in the PKS sequences. The high substrate selectivity in KSs reveals crucial information on chemistry installed by upstream modules and can thereby lead to the discovery of new and unexpected biosynthetic features in uncharacterized PKS modules and elusive trans-acting components. PrediKSion will be applied on the complete bacterial tree of life and achieve great impact by providing the community with a global mapping of predicted chemical functionality in trans-AT PKSs. The computational suggestions will finally be validated experimentally and the substrate scope of new PKS modules will be studied. In this way, PrediKSion will accelerate the mapping of uncharacterized PKSs and the discovery of new metabolites and potentially interesting pharmaceutical platforms.
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