%0 Journal Article %1 Egelhofer2010Automatic %A Egelhofer, Volker %A Schomburg, Ida %A Schomburg, Dietmar %D 2010 %I Public Library of Science %J PLoS Comput Biol %K ec enzyme-classification enzymes protein-function %N 1 %P e1000661+ %R 10.1371/journal.pcbi.1000661 %T Automatic Assignment of EC Numbers %U http://dx.doi.org/10.1371/journal.pcbi.1000661 %V 6 %X A wide range of research areas in molecular biology and medical biochemistry require a reliable enzyme classification system, e.g., drug design, metabolic network reconstruction and system biology. When research scientists in the above mentioned areas wish to unambiguously refer to an enzyme and its function, the EC number introduced by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (IUBMB) is used. However, each and every one of these applications is critically dependent upon the consistency and reliability of the underlying data for success. We have developed tools for the validation of the EC number classification scheme. In this paper, we present validated data of 3788 enzymatic reactions including 229 sub-subclasses of the EC classification system. Over 80\% agreement was found between our assignment and the EC classification. For 61 (i.e., only 2.5\%) reactions we found that their assignment was inconsistent with the rules of the nomenclature committee; they have to be transferred to other sub-subclasses. We demonstrate that our validation results can be used to initiate corrections and improvements to the EC number classification scheme. The fundamental understanding of metabolism in organisms which can only be achieved by integrated studies on their biology using a systems biology approach will aid in the design of future metabolic engineering strategies. Metabolic network reconstruction provides insight into the molecular mechanisms of a particular organism. An annotated genome containing the specific metabolic genes found in a particular organism can be used to reconstruct its metabolic network. The correlation between the genome and metabolism is made by searching gene databases or by searching protein databases with a known EC number in order to find the associated gene. The success of the search process is critically dependent upon the consistency and reliability of the underlying data. Therefore we have developed tools which can be used to identify wrong or inconsistent classification of enzymes and help to remove them from the relevant search databases.

@article{Egelhofer2010Automatic, abstract = {A wide range of research areas in molecular biology and medical biochemistry require a reliable enzyme classification system, e.g., drug design, metabolic network reconstruction and system biology. When research scientists in the above mentioned areas wish to unambiguously refer to an enzyme and its function, the {EC} number introduced by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology ({IUBMB}) is used. However, each and every one of these applications is critically dependent upon the consistency and reliability of the underlying data for success. We have developed tools for the validation of the {EC} number classification scheme. In this paper, we present validated data of 3788 enzymatic reactions including 229 sub-subclasses of the {EC} classification system. Over 80\% agreement was found between our assignment and the {EC} classification. For 61 (i.e., only 2.5\%) reactions we found that their assignment was inconsistent with the rules of the nomenclature committee; they have to be transferred to other sub-subclasses. We demonstrate that our validation results can be used to initiate corrections and improvements to the {EC} number classification scheme. The fundamental understanding of metabolism in organisms which can only be achieved by integrated studies on their biology using a systems biology approach will aid in the design of future metabolic engineering strategies. Metabolic network reconstruction provides insight into the molecular mechanisms of a particular organism. An annotated genome containing the specific metabolic genes found in a particular organism can be used to reconstruct its metabolic network. The correlation between the genome and metabolism is made by searching gene databases or by searching protein databases with a known {EC} number in order to find the associated gene. The success of the search process is critically dependent upon the consistency and reliability of the underlying data. Therefore we have developed tools which can be used to identify wrong or inconsistent classification of enzymes and help to remove them from the relevant search databases.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Egelhofer, Volker and Schomburg, Ida and Schomburg, Dietmar}, biburl = {https://www.bibsonomy.org/bibtex/2dfea3da746c13240edac0b6a700f8e7b/karthikraman}, citeulike-article-id = {6604119}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000661}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/20126531}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=20126531}, day = 29, doi = {10.1371/journal.pcbi.1000661}, interhash = {de1695d4a90b94f6d47deb6167ca2e63}, intrahash = {dfea3da746c13240edac0b6a700f8e7b}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {ec enzyme-classification enzymes protein-function}, month = jan, number = 1, pages = {e1000661+}, pmid = {20126531}, posted-at = {2010-02-03 10:13:42}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Automatic Assignment of {EC} Numbers}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000661}, volume = 6, year = 2010 }