Computational and in vitro study of isolated domains from fungal polyketide synthases

Abstract

Diverse approaches have been explored to generate new polyketides by engineering polyketide synthases (PKS). Although it has been proven possible to produce new compounds by designed PKS, engineering strategies failed to make polyketides available via widely applicable rules and protocols. The aim of this work was the first rational engineering of an iterative highly-reducing polyketide synthase (HR-PKS). This approach was performed on the Squalestatin Tetraketide Synthase (SQTKS), which catalyses the biosynthesis of the tetraketide side chain of squalestatin-S1 53, which is a potent squalene synthase inhibitor and can be potentially used to treat serum cholesterol related diseases. Second, tenellin 62 was investigated, which is the product of the iterative Type I polyketide synthase non ribosomal peptide synthetase (PKS-NRPS) TENS. Using a combination of different in silico methods, structural models of the enoyl reductase (ER) domain of SQTKS were obtained and validated. With the generated protein models different rational engineering experiments in silico were performed, in which amino acids for the mutagenesis approach in vitro were identified. The subsequent in vitro experiments revealed that it was possible to rationally engineer the ER domain of SQTKS. In addition, the different integrated mutations showed different effects on the intrinsic programming of the ER domain. Further, the chemical selectivity and kinetic parameters of the tested di-, tri-, tetra- and heptaketide substrate were influenced in a specific way through the different mutated ER domains. In addition, the structural-biological foundations and analysis for the domain swaps between Pretenellin A Synthetase (TENS), Predesmethylbassianin A Synthetase (DMBS) and Premilitarinone C Synthetase (MILS) were investigated and validated. Through different in silico structural analyses it was possible to consider the effects of swaps on protein structure and to understand the effect of the swaps at the structural level. Additionally, the in silico analysis helped to clarify the influence of extrinsic and intrinsic programming factors.