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.