Redox Biocatalysis: Quantitative Comparisons of Nicotinamide Cofactor Regeneration Methods

Enzymatic processes, particularly those capable of performing redox reactions, have recently been of growing research interest. Substrate specificity, optimal activity at mild temperatures, high selectivity, and yield are among the desirable characteristics of these oxidoreductase catalyzed reactions. Nicotinamide adenine dinucleotide (phosphate) or NAD(P)H-dependent oxidoreductases have been extensively studied for their potential applications like biosynthesis of chiral organic compounds, construction of biosensors, and pollutant degradation. One of the main challenges associated with making these processes commercially viable is the regeneration of the expensive cofactors required by the enzymes. Numerous efforts have pursued enzymatic regeneration of NAD(P)H by coupling a substrate reduction with a complementary enzyme catalyzed oxidation of a co-substrate. While offering excellent selectivity and high total turnover numbers, such processes involve complicated downstream product separation of a primary product from the coproducts and impurities. Alternative methods comprising chemical, electrochemical, and photochemical regeneration have been developed with the goal of enhanced efficiency and operational simplicity compared to enzymatic regeneration. Despite the goal, however, the literature rarely offers a meaningful comparison of the total turnover numbers for various regeneration methodologies. This comprehensive Review systematically discusses various methods of NAD(P)H cofactor regeneration and quantitatively compares performance across the numerous methods. Further, fundamental barriers to enhanced cofactor regeneration in the various methods are identified, and future opportunities are highlighted for improving the efficiency and sustainability of commercially viable oxidoreductase processes for practical implementation.

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

OBJECTIVES

Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available.

DATA DESCRIPTION

Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.