BRENDA, enzyme data and metabolic information

By I. Schomburg, Antje Chang, Dietmar Schomburg. Nucleic Acids Res., 2002

525 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/76e2ff238b575d1aaf3a866db46d85d089efc347

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Andrej Sali
2. Ben M. Webb
3. Min-Yi Shen
4. Yang Zhang
5. David Eramian
6. Narayanan Eswar
7. Ursula Pieper
8. Mallur Srivatsan Madhusudhan

In Molecular Biology

1. Andrej Sali
2. Ben M. Webb
3. Yang Zhang
4. Min-Yi Shen
5. David Eramian
6. Narayanan Eswar
7. Ursula Pieper
8. Mallur Srivatsan Madhusudhan

Citations in the corpus

• The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum (2013, methodology): “The main advantage of using existing models compared to reaction databases, such as KEGG or BRENDA [29], is that they contain information that can be difficult to obtain in an automated manner, in particular”
• Enzyme-specific profiles for genome annotation: PRIAM. (2003, background): “Specialised enzyme databases, such as ENZYME (4) or BRENDA (5), report enzyme-specific information as well as lists of polypeptides involved in every reported enzyme activity.”
• TRANSFAC®: transcriptional regulation, from patterns to profiles (2003, methodology): “Recently, the GENE table has been extended to one of the major link sources to external databases, including BRENDA ( 20 ), LocusLink, OMIM and RefSeq (21).”
• Plant metabolomics: large-scale phytochemistry in the functional genomics era. (2003, unknown): “, 2001), and BRENDA (Schomburg et al., 2002).”
• Subsequence-based feature map for protein function classification (2008, methodology): “Dataset for enzyme classification is extracted from BRENDA atabase (Schomburg et al., 2002).”
• Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. (2014, methodology): “For each reaction in the pathway, we calculated Vmax by multiplying the enzyme abundance we determined by its published turnover number (kcat) (Schomburg et al., 2002).”
• SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence (2003, background): “These include 46 families of enzymes from BRENDA (20), G-protein coupled receptors from GPCRDB (21), nuclear receptors from NucleaRDB (21), tyrosine receptor kinases derived from NCBI (31), five families of channels and one family of transporters from TCDB (22) and LGICdb (23) and DNA- and RNA-bindi”
• MODBASE: a database of annotated comparative protein structure models and associated resources (2005, background): “orthology (57)—with two experimentally characterized proteins in the BRENDA database (58): Q54755 (Synechococcus elongatus strain PCC 7942) and Q10YA8 (Trichodesmium erythraeum strain IMS101).”
• The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data (2004, background): “BRENDA currently has 3600 different EC numbers, i.e., 3600 different enzyme reactions.”
• Computational protein design enables a novel one-carbon assimilation pathway (2015, methodology): “LC-MS/MS analysis was performed as in Buescher et al.(18).”