Coevolution-based prediction of protein-protein interactions in polyketide biosynthetic assembly lines

Abstract

Polyketide synthases are multimodular enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by type I polyketide synthases (PKSs), which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein-protein interactions. The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Here, we introduce PKSpop, which uses a coevolution-based protein-protein interaction prediction algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (80% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for protein-protein interactions than coevolution between ketosynthase and acyl carrier protein domains.