3.5.5.1: nitrilase
This is an abbreviated version!
For detailed information about nitrilase, go to the full flat file.
Word Map on EC 3.5.5.1
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3.5.5.1
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enantioselectivity
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amidase
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hydratase
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rhodococcus
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biocatalyst
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synthesis
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rhodochrous
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benzonitrile
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mandelonitrile
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indole-3-acetic
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alcaligenes
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indole-3-acetonitrile
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3-cyanopyridine
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dinitriles
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phenylacetonitrile
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acrylonitrile
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bromoxynil
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r-mandelic
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nhase
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acidovorax
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ozaenae
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facilis
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industry
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iminodiacetic
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gibberella
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analysis
- 3.5.5.1
-
enantioselectivity
- amidase
-
hydratase
- rhodococcus
-
biocatalyst
- synthesis
- rhodochrous
- benzonitrile
- mandelonitrile
-
indole-3-acetic
- alcaligenes
- indole-3-acetonitrile
- 3-cyanopyridine
-
dinitriles
- phenylacetonitrile
- acrylonitrile
- bromoxynil
-
r-mandelic
- nhase
-
acidovorax
- ozaenae
- facilis
- industry
-
iminodiacetic
- gibberella
- analysis
Reaction
Synonyms
3-cyanopyridinase, acetonitrilase, Arylacetonitrilase, auxin-producing nitrilase, benzonitrilase, benzonitrilase A, benzonitrilase B, bll6402, BrNIT-T, CrNIT1, CrNIT2, cyc705, CynD, humanNIT1, More, nirilase II, NIT, NIT-T2, Nit06, NIT1, Nit1 nitrilase, Nit102, NIT2, NIT3, NIT4A/B1, NIT4A/B2, NitA, NitAk1, nitmc-fb, NitraS-ATII, nitrilase, nitrilase 1, nitrilase AtNIT1, nitrilase bll6402, nitrilase I, nitrile hydratase/amidase, Nlase, PaCNit, SsAH, SSO2122
ECTree
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General Information
General Information on EC 3.5.5.1 - nitrilase
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malfunction
Arabidopsis nit1 knockout lines display a loss in the production of deaminated glutathione with accumulation of higher levels of the glutathione breakdown products cysteinylglycine and cystathionine
metabolism
physiological function
additional information
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use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
Drepanopeziza brunnea MB_m1
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use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
Pseudomonas oleovorans CECT:5344
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use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
Methanosarcina mazei BAA-159
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use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
-
metabolism
-
use of script based method for classification of aliphatic and aromatic group of nitrilases. The algorithm can be used as a tool to classify nitrilases as aliphatic and aromatic class. The overall accuracy achieved is 95.00%. These machine learning techniques can be used to predict different features of the gene/protein and selection of these algorithms for the prediction of gene/protein function
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the enzyme functions as a metabolite repair enzyme that removes deaminated glutathione from the cytoplasm and plastids
physiological function
the enzyme has profound effects on root morphogenesis in early seedling development
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generation of a model of the nitrilase by homology modeling, overview
additional information
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generation of a model of the nitrilase by homology modeling, overview
additional information
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generation of a model of the nitrilase by homology modeling, overview
additional information
generation of a model of the nitrilase by homology modeling, overview
additional information
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generation of a model of the nitrilase by homology modeling, overview
additional information
native and recombinant Escherichia coli-expressed enzymes differ in substrate specificity, acid/amide ratio, reaction optima and stability
additional information
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generation of a model of the nitrilase by homology modeling, overview
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additional information
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generation of a model of the nitrilase by homology modeling, overview
-
additional information
-
native and recombinant Escherichia coli-expressed enzymes differ in substrate specificity, acid/amide ratio, reaction optima and stability
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