The pulp and paper industry produces large volumes of condensed kraft lignin, which is challenging to convert to single chemical products. For this purpose, tandem chemical depolymerization and bioconversion to a single atom-efficient product is a potentially promising strategy. In this study, we conducted copper-catal...
Kathryn M. Mains, Dillon T. Hofsommer, Michael A. Gapuz et al.· bioRxiv· 1 citation
This study presents a systematic comparison of sugar catabolic pathways that enabled development of strains suited for the tradeoffs between rate and yield and indicates that these performance metrics can reduce the minimum selling price of muconate-derived adipic acid and greenhouse gas emissions.
Dowan Kim, Torrey M Lind, Chen Ling et al.· bioRxiv· 1 citation
Efficient co-utilization of hexose and pentose sugars from lignocellulose is essential for microbial bioconversion, yet engineered catabolic pathways can be unstable or suboptimal in complex resource environments. Here, we use a Pseudomonas putida strain engineered to catabolize xylose and arabinose to examine how reso...
Sunghwa Woo, H. Lim, B. Norton-Baker et al.· Nature Communications· 0 citations
Pro proteomics, metabolomics, and 13C-fluxomics are applied to quantitatively compare central carbon and energy metabolism in wild-type P. putida and a muconate-producing strain for improved bioproduction from renewable feedstocks.
R. Wilkes, P. Suthers, A. Borchert et al.· bioRxiv· 3 citations
Results indicate that catechol, not PCA, is the principal bottleneck in muconate production via the PCA decarboxylation route originally demonstrated by Draths et al., refining the understanding of pathway limitations and offering new strategies for improving rate, yield, and strain resilience in muconate bioproduction...
Alissa C. Bleem, Tracy L. Hodges, Torrey M Lind et al.· bioRxiv· 2 citations
This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental informati...
J. Lazar, Evan Komp, I. Martínez et al.· bioRxiv· 1 citation
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