Gregg T. Beckham

This paper investigates how engineered Pseudomonas putida reconfigures metabolic fluxes to enhance muconate production from lignin-related aromatics.

This paper engineers Pseudomonas putida to catabolize kraft lignin-derived aromatics into muconate, achieving high monomer utilization and carbon conversion efficiency.

This study compares two arabinose utilization pathways in Pseudomonas putida to optimize muconic acid production from lignocellulosic sugars, finding a tradeoff between growth rate and yield.

This paper improves muconic acid production in Pseudomonas putida by overcoming PCA and catechol accumulation through adaptive evolution and metabolic engineering, showing that increased CatA1 expression enhances pathway efficiency.

The paper uses machine learning to guide cell-free expression of carbonic anhydrases, identifying variants with improved stability under high temperatures.

The paper develops a fluorescent probe-based method to screen plastic-degrading enzymes using rhodamine-doped polymer substrates and validates it with PET and polyethylene materials.

This study identifies a new bacterium and multicopper oxidase enzyme capable of oxidizing polyethylene, causing surface damage and releasing oxygenated products.

This study evolves a microbial strain under dynamic conditions to optimize sugar catabolism, finding that mixed-sugar environments promote stable retention and coordinated optimization of multiple pathways.

This paper redefines the role of PhaG and CoA ligases in PHA biosynthesis in Pseudomonas putida and demonstrates engineered production of 3-hydroxyacids.

This study quantifies how Pseudomonas putida KT2440 couples carbon and energy metabolism during lignin-derived phenolic compound utilization, revealing metabolic remodeling and cofactor production mechanisms.

This study attempts to replicate two recent claims of enzymatic degradation of polyethylene and poly(vinyl chloride) but finds no activity under various conditions, suggesting previous results may have been misinterpreted.

The paper enhances the Chi.Bio bioreactor with pH control, demonstrating its effectiveness in PET hydrolysis with reduced enzyme requirements.

This study uses machine learning to uncover the genome-scale transcriptional regulatory network in Pseudomonas putida using gene expression data.

This study uses machine learning to identify sequence features that predict functional subtypes and CBM presence in GH7 glycoside hydrolases with high accuracy.

This study improves enzyme optimum temperature prediction by applying ensemble learning and resampling strategies to address data imbalance, reducing error on high Topt values and increasing overall R2.