Engineering in vivo production of α-branched polyesters.

Clicks: 298
ID: 57594
2019
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Abstract
Polymers are an important class of materials that are used for a broad range of applications, from drug delivery to packaging. Given their widespread use, a major challenge in this area is the development of technology for their production from renewable sources and efforts to promote their efficient recycling and biodegradation. In this regard, the synthesis of polyesters based on the natural polyhydroxyalkanoate (PHA) pathway offers an attractive route for producing sustainable polymers. However, monomer diversity in naturally-occurring polyesters can be limited with respect to the design of polymers with material properties suitable for various applications. In this work, we have engineered a pathway to produce α-methyl branched PHA. In the course of this work, we have also identified a PHA polymerase (CapPhaEC) from activated sludge from waste water treatment that demonstrates a higher capacity for incorporation of α-branched monomer units than those previously identified or engineered. Production in Escherichia coli allows the construction of microbial strains that produce the copolyesters with 21-36% branched monomers using glucose and propionate as carbon sources. These polymers have typical weight-average molar masses (Mw) ranging between 1.7-2.0 × 105 g mol-1 and display no observable melting transition, only relatively low glass transition temperatures from -13 to -20 °C. The lack of a melting transition indicates that these polymers are amorphous materials with no crystallinity, which is in contrast to the natural poly(hydroxybutyrate) homopolymer. Our results expand the utility of PHA-based pathways and provide biosynthetic access to α-branched polyesters to enrich the properties of bio-based sustainable polymers.
Reference Key
dong2019engineeringjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dong, Hongjun;Liffland, Stephanie;Hillmyer, Marc A;Chang, Michelle Cy;
Journal Journal of the American Chemical Society
Year 2019
DOI
10.1021/jacs.9b08585
URL
Keywords
adaptive identification anaerobic digestion process parameter estimation state-space model

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