Interface Engineering of Carrier-Protein-Dependent Metabolic Pathways

Carrier-protein-dependent metabolic pathways biosynthesize fatty acids, polyketides, and non-ribosomal peptides, producing metabolites with important pharmaceutical, environmental, and industrial properties. Recent findings demonstrate that these pathways rely on selective communication mechanisms involving protein-protein interactions (PPIs) that guide enzyme reactivity and timing. While rational design of these PPIs could enable pathway design and modification, this goal remains a challenge due to the complex nature of protein interfaces. Computational methods offer an encouraging avenue, though many score functions fail to predict experimental observables, leading to low success rates. Here, we improve upon the Rosetta score function, leveraging experimental data through iterative rounds of computational prediction and mutagenesis, to design a hybrid fatty acid-non-ribosomal peptide initiation pathway. By increasing the weight of the electrostatic score term, the computational protocol proved to be more predictive, requiring fewer rounds of iteration to identify mutants with high in vitro activity. This allowed efficient design of new PPIs between a non-ribosomal peptide synthetase adenylation domain, PltF, and a fatty acid synthase acyl carrier protein, AcpP, as validated by activity and structural studies. This method provides a promising platform for customized pathway design, establishing a standard for carrier-protein-dependent pathway engineering through PPI optimization.