%0 Journal Article
%T Relationships between kinetic constants and the amino acid composition of enzymes from the yeast Saccharomyces cerevisiae glycolysis pathway
%A Peteris Zikmanis
%A Inara Kampenusa
%J EURASIP Journal on Bioinformatics and Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1687-4153-2012-11
%X According to the concepts of systems biology, metabolic fluxes are net sums of underlying enzymatic reaction rates represented by integral outputs of three biological quantities which interact at the level of enzyme kinetics: kinetic parameters, enzyme and reactant concentrations [1]. Integrated view of enzymes suggests to consider them as dynamic assemblies whose variable structures are closely related to catalytic functions [2,3]. It is therefore an important task to extend the knowledge of the enzyme sequence, structure and function relationships which allow to specify a chemical mechanism of catalytic reaction and to be predictive for targeted modification of enzymes [4]. Site-directed mutagenesis has proved to be a powerful tool to probe certain amino acids (AA) within an enzyme, yet still somewhat less focusing on other residues and, therefore, tempted to ignore the actual interdependence of catalytic, binding, and structural residues being considered as a key feature of such complex cooperative systems [2,3,5]. Moreover, statistical evaluation of the relation between functionally and structurally important AA of the enzyme sequences reveals contribution of the catalytic residues to the structural stabilization of the respective proteins, which indicates both residue sets as rather overlapping than segregated [6]. In addition, the modest success of creating artificial enzymes also points to currently unknown, probably crucial, parameters that could significantly affect enzyme catalysis [7]. AA composition (AAC) is a simplest attribute of proteins among the so-called global sequence descriptors [8] which represents the frequencies of occurrence of the natural AA thereby creating a 20-dimensional feature for a given protein sequence [8,9]. AAC appears as a simple, yet powerful feature for a successful prediction of several protein properties, including protein folding and mutual interactions [10-12].On the other hand, these complex events can be measured in many
%K Michaelis-Menten constant
%K Turnover number
%K Specificity constant
%K Glycolytic enzymes
%K Sequence-dependent properties
%K Multivariate relationships
%U http://bsb.eurasipjournals.com/content/2012/1/11