Any feedback?
Literature summary for 3.5.5.7 extracted from
- Classifying nitrilases as aliphatic and aromatic using machine learning technique (2018), 3 Biotech, 8, 68 .
Organism
| Organism | UniProt | Comment | Textmining |
|---|---|---|---|
| Achromobacter xylosoxidans | - |
- |
- |
| Acidovorax oryzae | - |
- |
- |
| Afipia carboxidovorans | - |
- |
- |
| Afipia carboxidovorans OM5 | - |
- |
- |
| Agrobacterium rhizogenes | - |
- |
- |
| Agrobacterium rhizogenes ATCC 15834 | - |
- |
- |
| Amycolatopsis taiwanensis | - |
- |
- |
| Azospirillum halopraeferens | - |
- |
- |
| Betaproteobacteria bacterium | - |
- |
- |
| Betaproteobacteria bacterium MOLA814 | - |
- |
- |
| Bosea sp. 117 | - |
- |
- |
| Bradyrhizobium elkanii | - |
- |
- |
| Bradyrhizobium sp. ORS 278 | A4YWK0 | - |
- |
| Bradyrhizobium sp. th.b2 | - |
- |
- |
| Burkholderia gladioli | - |
- |
- |
| Burkholderia sp. BT03 | - |
- |
- |
| Colletotrichum fioriniae | - |
- |
- |
| Colletotrichum fioriniae PJ7 | - |
- |
- |
| Comamonas testosteroni | - |
- |
- |
| Cupriavidus sp. WS | - |
- |
- |
| Danaus plexippus | - |
- |
- |
| Danaus plexippus F2 | - |
- |
- |
| Janthinobacterium sp. Marseille | - |
- |
- |
| Marinomonas ushuaiensis | - |
- |
- |
| Marinomonas ushuaiensis DSM 15871 | - |
- |
- |
| Mesorhizobium loti | - |
- |
- |
| Methylibium petroleiphilum | - |
- |
- |
| Methylobacterium sp. 88A | - |
- |
- |
| Methylobacterium sp. L2-4 | - |
- |
- |
| Methylopila sp. 73B | - |
- |
- |
| Methylopila sp. M107 | - |
- |
- |
| Methyloversatilis universalis | - |
- |
- |
| Nocardia sp. C-14-1 | - |
- |
- |
| Paraburkholderia kururiensis | - |
- |
- |
| Paraburkholderia mimosarum | - |
- |
- |
| Polycyclovorans algicola | - |
- |
- |
| Pseudomonas syringae pv. syringae | Q500U1 | - |
- |
| Pseudomonas syringae pv. syringae B728a | Q500U1 | - |
- |
| Rhizobium leguminosarum | - |
- |
- |
| Rhizobium leguminosarum bv. viciae 3841 | - |
- |
- |
| Rhizobium sp. JGI 0001019-L19 | - |
- |
- |
| Rhizoctonia solani | - |
- |
- |
| Rhizoctonia solani 123E | - |
- |
- |
| Rhodococcus rhodochrous | - |
- |
- |
| Rhodococcus rhodochrous J1 | - |
- |
- |
| Rhodococcus rhodochrous K22 | - |
- |
- |
| Saccharomonospora viridis | - |
- |
- |
| Saccharomonospora viridis DSM 43017 | - |
- |
- |
| Serratia sp. M24T3 | - |
- |
- |
| Shimwellia blattae | I2BBF1 | - |
- |
| Shimwellia blattae ATCC 29907 | I2BBF1 | - |
- |
| Sorangium cellulosum | - |
- |
- |
| Sorangium cellulosum So0157-2 | - |
- |
- |
| Sphingopyxis alaskensis | Q1GTC0 | - |
- |
| Sphingopyxis alaskensis DSM 13593 | Q1GTC0 | - |
- |
| Starkeya novella | - |
- |
- |
| Synechococcus elongatus PCC 6301 | Q31PZ9 | - |
- |
| Teredinibacter turnerae | - |
- |
- |
| Variovorax paradoxus | - |
- |
- |
| Variovorax paradoxus EPS | - |
- |
- |
| Variovorax sp. P21 | - |
- |
- |
| Xanthobacter sp. 126 | - |
- |
- |
General Information
| General Information | Comment | Organism |
|---|---|---|
| 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 | Rhodococcus rhodochrous |
| 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 | Starkeya novella |
| 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 | Comamonas testosteroni |
| 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 | Burkholderia gladioli |
| 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 | Agrobacterium rhizogenes |
| 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 | Mesorhizobium loti |
| 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 | Afipia carboxidovorans |
| 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 | Achromobacter xylosoxidans |
| 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 | Rhizoctonia solani |
| 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 | Variovorax paradoxus |
| 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 | Bradyrhizobium elkanii |
| 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 | Methyloversatilis universalis |
| 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 | Methylibium petroleiphilum |
| 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 | Sorangium cellulosum |
| 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 | Paraburkholderia kururiensis |
| 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 | Pseudomonas syringae pv. syringae |
| 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 | Teredinibacter turnerae |
| 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 | Saccharomonospora viridis |
| 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 | Synechococcus elongatus PCC 6301 |
| 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 | Sphingopyxis alaskensis |
| 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 | Bradyrhizobium sp. ORS 278 |
| 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 | Shimwellia blattae |
| 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 | Burkholderia sp. BT03 |
| 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 | Nocardia sp. C-14-1 |
| 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 | Rhizobium leguminosarum bv. viciae 3841 |
| 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 | Danaus plexippus |
| 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 | Polycyclovorans algicola |
| 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 | Methylobacterium sp. L2-4 |
| 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 | Bosea sp. 117 |
| 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 | Bradyrhizobium sp. th.b2 |
| 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 | Azospirillum halopraeferens |
| 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 | Rhizobium sp. JGI 0001019-L19 |
| 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 | Paraburkholderia mimosarum |
| 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 | Amycolatopsis taiwanensis |
| 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 | Variovorax sp. P21 |
| 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 | Acidovorax oryzae |
| 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 | Methylobacterium sp. 88A |
| 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 | Methylopila sp. 73B |
| 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 | Xanthobacter sp. 126 |
| 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 | Colletotrichum fioriniae |
| 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 | Marinomonas ushuaiensis |
| 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 | Betaproteobacteria bacterium |
| 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 | Cupriavidus sp. WS |
| 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 | Methylopila sp. M107 |
| 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 | Serratia sp. M24T3 |
| 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 | Janthinobacterium sp. Marseille |
| 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 | Rhizobium leguminosarum |
Use of this online version of BRENDA is free under the CC BY 4.0 license. See terms of use for full details.
Release 2023.1 (January 2023)
Legal
Curated at
Member of
