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Prediction of catalytic site residues (CSR) for enzymes by applying pattern recognition on protein structural descriptors found in STING database


The enzymes perform their biological role by means of specific amino acids, known ascatalytic residues. Most methods for the prediction of catalytic residues of enzymes use at least one parameter of primary sequence conservation. Several approaches which have been used so far and problems found in the function prediction of proteins are reviewed in this text. Whereas the protein function is determined from its structure, this research project aims to identify which structural descriptors, available in the database of software STING, are of utmost importance to discriminate catalytic residues that, due to its importance, cause a high conservation in primary sequence. Consequently, the project will propose a new classifier method of the protein function using these descriptors as input. Different classifiers will be created with the purpose of differentiation of the catalytic site residues for threefoldgranularity levels of the problem:enzymes in general, the six classes of families of enzymes EC and EC specific families. Theexpected results are: new methods for identifying the most important structural descriptors forenzymes catalytic site residues recognition, determination of specific parameters for groups ofenzymes, consisting of queries formed by lower and upper delimiters in a range of values foreach descriptor, the implementation of methods for determining the catalytic residues from theparameters found; creation of a "periodic table" of descriptors for protein families andsubfamilies; expansion of the algorithm to other motifs combinations which bind specific ligands(Mg , Ca, etc.).Also multivariate statistical analysis of data from the database STING will be performedin order to: 1) validation of the method, 2) identification of new proteins catalytic residues. (AU)

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(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DE MORAES, FABIO R.; NESHICH, IZABELLA A. P.; MAZONI, IVAN; YANO, INACIO H.; PEREIRA, JOSE G. C.; SALIM, JOSE A.; JARDINE, JOSE G.; NESHICH, GORAN. Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages. PLoS One, v. 9, n. 1 JAN 28 2014. Web of Science Citations: 4.
DIAS-LOPES, CAMILA; NESHICH, IZABELLA A. P.; NESHICH, GORAN; ORTEGA, JOSE MIGUEL; GRANIER, CLAUDE; CHAVEZ-OLORTEGUI, CARLOS; MOLINA, FRANCK; FELICORI, LIZA. Identification of New Sphingomyelinases D in Pathogenic Fungi and Other Pathogenic Organisms. PLoS One, v. 8, n. 11 NOV 1 2013. Web of Science Citations: 11.

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