RESUMO
Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.
Assuntos
Teorema de Bayes , Enzimas/química , Enzimas/classificação , Conformação Proteica , Algoritmos , Humanos , Alinhamento de SequênciaRESUMO
Diamond STING is a new version of the STING suite of programs for a comprehensive analysis of a relationship between protein sequence, structure, function and stability. We have added a number of new functionalities by both providing more structure parameters to the STING Database and by improving/expanding the interface for enhanced data handling. The integration among the STING components has also been improved. A new key feature is the ability of the STING server to handle local files containing protein structures (either modeled or not yet deposited to the Protein Data Bank) so that they can be used by the principal STING components: (Java)Protein Dossier ((J)PD) and STING Report. The current capabilities of the new STING version and a couple of biologically relevant applications are described here. We have provided an example where Diamond STING identifies the active site amino acids and folding essential amino acids (both previously determined by experiments) by filtering out all but those residues by selecting the numerical values/ranges for a set of corresponding parameters. This is the fundamental step toward a more interesting endeavor-the prediction of such residues. Diamond STING is freely accessible at http://sms.cbi.cnptia.embrapa.br and http://trantor.bioc.columbia.edu/SMS.
