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Biology subjects

Summers, R. M.

Publications and source records attributed to Summers, R. M..

2 recordsLinked to original sources

Rational Protein Engineering of Bacterial N-Demethylases to Create Biocatalysts for the Production of Methylxanthines

Methylxanthines have a rich history as therapeutics and pharmaceuticals. However, natural dimethyl- and monomethylxanthines are difficult to produce synthetically, which has limited further exploration of these compounds in medicinal applications. A biosynthetic method for production of methylxanthines from whole cell biocatalysts is an attractive alternative. The bacterium Pseudomonas putida CBB5 contains a set of five enzymes, NdmABCDE, which are responsible for methylxanthine metabolism via N-demethylation to xanthine. The recent elucidation of the crystal structures of NdmA and NdmB, which remove the N1- and N3-methyl groups of caffeine, respectively, has opened new avenues to create biocatalysts for methylxanthine production. We have created a set of fifteen N-demethylase mutants and expressed them in E. coli BL21(DE3) as whole cell biocatalysts. The activity of each mutant was characterized for their affinity towards caffeine, theobromine, and theophylline. Two mutant enzymes in particular, labeled NdmA3 and NdmA4, both exhibited selectivity towards the N3-methyl group instead of the N1-methyl group. We also discovered that specific point mutations in NdmD resulted in the ability to tune the rate of the N-demethylase reaction. These new enzymes provide the capability of producing high-value methylxanthines, such as paraxanthine and 1-methylxanthine, through a biocatalytic route.

microbiology↗

BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in Millions

Big data, accumulated from biomedical and agronomic studies, provides the potential to identify genes controlling complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big data also leads to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed Fixed and random model Circulating Probability Unification (FarmCPU) method uses a bin method under the assumption that Quantitative Trait Nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to a remarkable improvement in computing time. Now, a dataset with half million markers and one million individuals can be analyzed within five hours, compared with one week using FarmCPU.

genomics↗