Relationships between metabolic fluxes and enzyme amino acid composition

Abstract

Relationships between metabolic fluxes and enzyme amino acid composition Abbreviations AA -amino acid; AAC -amino acid composition; LOOCV -leave-one-out cross-validation; VIF -variance inflation factor. IntroductionCell metabolism is comprised of enzyme-catalyzed biochemical reactions and carrier-mediated transport processes. Taken as a whole, these reactions form interrelated metabolic pathways which are combined into a cellular metabolic network. Metabolic fluxes describe the amount of material chemically converted or transported per time unit and are considered as the key parameters of any metabolic pathway and hence, as the fundamental determinants of cell physiology [1][2][3][4]. Changes in metabolic fluxes in response to various types of genetic and environmental perturbations are critical for the metabolic flux control which is a key objective of metabolic engineering [5,6]. On the other hand, enzyme activity is one of the major factors influencing the magnitude of metabolic fluxes in any cell [6]. According to 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 [7]. An integrated view on enzymes suggests them as dynamic assemblies whose variable structures are closely related to catalytic functions [8,9]. It is therefore important to extend our knowledge of enzyme sequence, structure, and function relationships [10], as well as to explore coherencies between enzyme activity profiles and metabolic flux distributions in order to understand the physiological dynamics within a cell [2,11]. Amino acid (AA) composition (AAC) is a simplest attribute of proteins among so-called global sequence Keywords: Saccharomyces cerevisiae • Metabolic fluxes • Glycolytic enzymes • Amino acid composition • Multivariate relationshipsAbstract: Metabolic fluxes are a key parameter of metabolic pathways being closely related to the kinetic properties of enzymes and could be conditional on their sequence characteristics. This study examines possible relationships between the metabolic fluxes and the amino acid (AA) composition (AAC) for enzymes from the yeast Saccharomyces cerevisiae glycolysis pathway. Metabolic fluxes were quantified by the COPASI tool using the kinetic models of Hynne and Teusink at 25 mM, 50 mM, and 100 mM of external glucose or employing literature data for cognate kinetic or stoichiometric models. The enzyme sequences were taken from the UniProtKB, and the AAC computed by the ExPASy/ProtParam tool. Multiple linear regressions (89.07% < R2 adjusted < 91.82%; P<0.00001) were found between the values of metabolic fluxes and the selected sets of AA frequencies (5 to 7 for each model). Selected AA differed from the rest by their physicochemical and structural propensities, thus suggesting a distinctive contribution to the properties of enzymes, and hence the metabolic fluxes. The results provide evidenc...

Relationship between Metabolic Fluxes and Sequence-Derived Properties of Enzymes

“…This study in the most direct way continues our previous research [ 11 , 12 ] thereby forming the mutually affirmative and complementary set of results. In addition, the resulting relationships are well in line with the views of the actual interdependence of catalytic, binding, and structural residues to ensure the full-scale efficiency of biocatalysts [ 29 ] supported by the findings that a certain functional overlap may occur between the sets of AA [ 30 ] as well as by the evidence confirming that the primary structure-derived features [ 31 , 32 ] or integral physical-chemical indices of proteins [ 33 ] can be used to predict the values of kinetic constants for particular enzymes.…”

“…This allowed the hypothesis that it is possible to push further the use of AAC to describe protein sequences relative to their functions. Our previous studies confirm this possibility, since the relationship between the kinetic constants of the yeast Saccharomyces cerevisiae glycolytic enzymes and the AAC of corresponding sequences has been found [ 11 ], as well as statistically robust multivariate regression models established which link both the flux distribution through the glycolysis pathway and the AAC of respective enzymes [ 12 ]. At the same time, it should be noted that present results reflect only the first level, looking at a much wider set of potential relationships, as they do not give enough insights into the structural and physical-chemical properties of sequences as well as the actual contribution of AA [ 13 ].…”