Chemoinformatic-Guided Engineering of Polyketide Synthases
Amin Zargar, Ravi Lal, Luis E. Valencia, Jessica Wang, Tyler W. H. Backman, Pablo Cruz‐Morales, Ankita Kothari, Miranda Werts, Andrew R. Wong, Constance B. Bailey, Arthur Loubat, Yuzhong Liu, Yan Chen, Samantha Chang, Veronica T. Benites, Amanda C. Hernández, Jesus F. Barajas, Mitchell G. Thompson, Carolina A. Barcelos, Rasha I. Anayah, Héctor García Martín, Aindrila Mukhopadhyay, Christopher J. Petzold, Edward E. K. Baidoo, Leonard Katz, Jay D. Keasling
Abstract
Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A significant challenge is re-engineering a partially reductive PKS module to produce a saturated β-carbon through a reductive loop (RL) exchange. In this work, we sought to establish that chemoinformatics, a field traditionally used in drug discovery, offers a viable strategy for RL exchanges. We first introduced a set of donor RLs of diverse genetic origin and chemical substrates into the first extension module of the lipomycin PKS (LipPKS1). Product titers of these engineered unimodular PKSs correlated with chemical structure similarity between the substrate of the donor RLs and recipient LipPKS1, reaching a titer of 165 mg/L of short-chain fatty acids produced by the host Streptomyces albus J1074. Expanding this method to larger intermediates that require bimodular communication, we introduced RLs of divergent chemosimilarity into LipPKS2 and determined triketide lactone production. Collectively, we observed a statistically significant correlation between atom pair chemosimilarity and production, establishing a new chemoinformatic method that may aid in the engineering of PKSs to produce desired, unnatural products.