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PrediKSion: An evolutionary guided and experimentally validated computational pipeline to unravel new polyketide synthase functionality

Project description

A sustainable way of producing novel antibiotics

Polyketides are a large class of secondary metabolites from bacteria, fungi and plants with interesting and valuable activities. They are exploited as antibiotics, antifungals and drugs and can be chemically synthesised or produced from biological sources. The EU-funded PrediKSion project proposes to exploit acyltransferase polyketide synthase (AT PKS) enzymes from bacteria as a sustainable way to produce polyketides. To unveil new biosynthetic features in AT PKS, researchers will develop a computational pipeline that will assess the functionality of uncharacterised enzymes. Long term this will lead to the discovery of novel pharmaceutical entities


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.



Net EU contribution
€ 191 149,44
Raemistrasse 101
8092 Zuerich

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Schweiz/Suisse/Svizzera Zürich Zürich
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00