A high-throughput screening method for determining the substrate scope of nitrilases

Black, Gary W.; Brown, Nicola L.; Perry, Justin J.; Randall, P. D.; Turnbull, Graeme; Zhang, Meng

doi:10.1039/c4cc06021k

Cited by 35 publications

(34 citation statements)

References 25 publications

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“…The activities for β-CA were calculated from the production of ammonia, which was determined spectrophotometrically [11,49]. The activities for HCN were determined from the production of formamide, which was determined spectrophotometrically [50] and by HPLC (see below). The activities for other substrates were determined from the production of carboxylic acids and/or amides, which were determined by HPLC (see below).…”

Section: Methodsmentioning

confidence: 99%

Genetic and Functional Diversity of Nitrilases in Agaricomycotina

Rucká

Chmátal

Кулик

et al. 2019

IJMS

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Nitrilases participate in the nitrile metabolism in microbes and plants. They are widely used to produce carboxylic acids from nitriles. Nitrilases were described in bacteria, Ascomycota and plants. However, they remain unexplored in Basidiomycota. Yet more than 200 putative nitrilases are found in this division via GenBank. The majority of them occur in the subdivision Agaricomycotina. In this work, we analyzed their sequences and classified them into phylogenetic clades. Members of clade 1 (61 proteins) and 2 (25 proteins) are similar to plant nitrilases and nitrilases from Ascomycota, respectively, with sequence identities of around 50%. The searches also identified five putative cyanide hydratases (CynHs). Representatives of clade 1 and 2 (NitTv1 from Trametes versicolor and NitAg from Armillaria gallica, respectively) and a putative CynH (NitSh from Stereum hirsutum) were overproduced in Escherichia coli. The substrates of NitTv1 were fumaronitrile, 3-phenylpropionitrile, β-cyano-l-alanine and 4-cyanopyridine, and those of NitSh were hydrogen cyanide (HCN), 2-cyanopyridine, fumaronitrile and benzonitrile. NitAg only exhibited activities for HCN and fumaronitrile. The substrate specificities of these nitrilases were largely in accordance with substrate docking in their homology models. The phylogenetic distribution of each type of nitrilase was determined for the first time.

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Section: Methodsmentioning

confidence: 99%

Genetic and Functional Diversity of Nitrilases in Agaricomycotina

Rucká

Chmátal

Кулик

et al. 2019

IJMS

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“…To prepare crude extracts of the recombinant enzymes, nitrilase gene sequences were transformed and expressed using E. coli BL21(DE3) host cells as described previously 24,27 . Briefly, a 15‐mL overnight culture of BL21(DE3) cells, with and without plasmids, grown in LB medium, was seeded into 250 ml baffled flasks containing 150 ml of 2x YPTG medium (yeast extract, 10 g/L; KH 2 PO 4 , 3 g/L; K 2 HPO 4 , 7 g/L; NaCL, 5 g/L; tryptone 16 g/L; and glucose 18 g/L) inoculated with carbenicillin (100 mg ml −1 ).…”

Section: Methodsmentioning

confidence: 99%

“…Various nitrilase activity assays have been described and are based on either fluorogenic or chromogenic substrates or pH indicator methods 21‐23 . Recently, a chromogenic method was developed as a convenient means to screen recombinantly produced nitrilases in crude cell extracts 24 . Alleviating purification steps facilitate high‐throughput screening and evaluation of diverse, potential substrates.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based prediction of enzyme substrate scope: Application to bacterial nitrilases

et al. 2020

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Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequence‐ and structure‐based annotation approaches are often accurate for predicting broad categories of substrate specificity, they generally cannot predict which specific molecules will be accepted as substrates for a given enzyme, particularly within a class of closely related molecules. Combining targeted experimental activity data with structural modeling, ligand docking, and physicochemical properties of proteins and ligands with various machine learning models provides complementary information that can lead to accurate predictions of substrate scope for related enzymes. Here we describe such an approach that can predict the substrate scope of bacterial nitrilases, which catalyze the hydrolysis of nitrile compounds to the corresponding carboxylic acids and ammonia. Each of the four machine learning models (logistic regression, random forest, gradient‐boosted decision trees, and support vector machines) performed similarly (average ROC = 0.9, average accuracy = ~82%) for predicting substrate scope for this dataset, although random forest offers some advantages. This approach is intended to be highly modular with respect to physicochemical property calculations and software used for structural modeling and docking.

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“…We compared cellular vs. cell-free enzymatic activity for 16 putative nitrilases in a typical industrial enzyme screening workflow. For cell-based expression, we transformed pET-24b plasmids into E. coli BL21 cells to overexpress enzymes of interest, followed by cell lysis and substrate conversion testing in crude cell extract (analogous to published high-throughput methods 19 ). For cell-free synthesis, we expressed putative nitrilases by minicircle RCA-enabled CFPS.…”

Section: Comparison Of Enzymatic Activity By Different Screening Workmentioning

confidence: 99%

Rolling circle amplification of synthetic DNA accelerates biocatalytic determination of enzyme activity relative to conventional methods

Hadi

Nozzi

Melby

et al. 2020

Sci Rep

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The ability to quickly and easily assess the activity of large collections of enzymes for a desired substrate holds great promise in the field of biocatalysis. Cell-free synthesis, although not practically amenable for large-scale enzyme production, provides a way to accelerate the timeline for screening enzyme candidates using small-scale reactions. However, because cell-free enzyme synthesis requires a considerable amount of template DNA, the preparation of high-quality DNA "parts" in large quantities represents a costly and rate-limiting prerequisite for high throughput screening. Based on time-cost analysis and comparative activity data, a cell-free workflow using synthetic DNA minicircles and rolling circle amplification enables comparable biocatalytic activity to cell-based workflows in almost half the time. We demonstrate this capability using a panel of sequences from the carbon-nitrogen hydrolase superfamily that represent possible green catalysts for synthesizing small molecules with less waste compared to traditional industrial chemistry. This method provides a new alternative to more cumbersome plasmid-or PCR-based protein expression workflows and should be amenable to automation for accelerating enzyme screening in industrial applications.

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A high-throughput screening method for determining the substrate scope of nitrilases

Cited by 35 publications

References 25 publications

Genetic and Functional Diversity of Nitrilases in Agaricomycotina

Genetic and Functional Diversity of Nitrilases in Agaricomycotina

Machine learning‐based prediction of enzyme substrate scope: Application to bacterial nitrilases

Rolling circle amplification of synthetic DNA accelerates biocatalytic determination of enzyme activity relative to conventional methods

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