The kinetic models of metabolic pathways represent a system of biochemical reactions in terms of metabolic fluxes and enzyme kinetics. Therefore, the apparent differences of metabolic fluxes might reflect distinctive kinetic characteristics, as well as sequence-dependent properties of the employed enzymes. This study aims to examine possible linkages between kinetic constants and the amino acid (AA) composition (AAC) for enzymes from the yeast Saccharomyces cerevisiae glycolytic pathway. The values of Michaelis-Menten constant (KM), turnover number (kcat), and specificity constant (ksp = kcat/KM) were taken from BRENDA (15, 17, and 16 values, respectively) and protein sequences of nine enzymes (HXK, GADH, PGK, PGM, ENO, PK, PDC, TIM, and PYC) from UniProtKB. The AAC and sequence properties were computed by ExPASy/ProtParam tool and data processed by conventional methods of multivariate statistics. Multiple linear regressions were found between the log-values of kcat (3 models, 85.74% < Radj.2 <94.11%, p < 0.00001), KM (1 model, Radj.2 = 96.70%, p < 0.00001), ksp (3 models, 96.15% < Radj.2 < 96.50%, p < 0.00001), and the sets of AA frequencies (four to six for each model) selected from enzyme sequences while assessing the potential multicollinearity between variables. It was also found that the selection of independent variables in multiple regression models may reflect certain advantages for definite AA physicochemical and structural propensities, which could affect the properties of sequences. The results support the view on the actual interdependence of catalytic, binding, and structural residues to ensure the efficiency of biocatalysts, since the kinetic constants of the yeast enzymes appear as closely related to the overall AAC of sequences.

Metabolic fluxes are key parameters of metabolic pathways being closely related to the kinetic properties of enzymes, thereby could be dependent on. This study examines possible relationships between the metabolic fluxes and the physical-chemical/structural features of 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 varied concentrations of external glucose. The enzyme sequences were taken from the UniProtKB and the average amino acid (AA) properties were computed using the set of Georgiev's uncorrelated scales that satisfy the VARIMAX criterion and specific AA indices that show the highest correlations with those. Multiple linear regressions (88.41%

The combined set of codon usage frequencies (61 sense codons) from the 111 annotated sequences of leaderless secreted type I, type III, type IV, and type VI proteins from proteobacteria were subjected to the forward and backward selection to obtain a combination of most effective predictor variables for classification/prediction purposes. The group of 24 codon frequencies displayed a strong discriminatory power with an accuracy of 100% for originally grouped and 97.3 +/- 1.6% for cross-validated (LOOCV) cases and an acceptable error rate (0.062 +/- 0.012) in k-fold (k = 6) cross-validation (KCV). The summary frequencies of synonymous codons for ten amino acids as the alternative predictor variables revealed a comparable discriminatory power (92.8 +/- 2.5% for LOOCV), however at somewhat lower levels of prediction accuracy (0.106 +/- 0.015 of KCV). A number of significant (p < 0.001) differences were found among indices of codon usage and amino acid composition depending on a definite secretion type. About 60% of secretion substrates were characterized as apparently originated from horizontal gene transfer events or putative alien genes and found to be unequally allocated in respect of groups. The proposed prediction approaches could be used to specify secretome proteins from genomic sequences as well as to assess the compatibility between bacterial secretion pathways and secretion substrates.

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...

C-and N-terminal sequences (64 amino acid residues each) of 89 non-classically secreted type I, type III and type IV proteins (SwissProt/TrEMBL) from proteobacteria were transformed into predicted secondary structures. Multivariate analysis of variance (MANOVA) confirmed the significance of location (C-or N-termini) and secretion type as essential factors in respect of quantitative representations of structured (a-helices, b-strands) and unstructured (coils) elements. The profiles of secondary structures were transcripted using unequal property values for helices, strands and coils and corresponding numerical vectors (independent variables) were subjected to multiple discriminant analysis with the types of secreted proteins as the dependent variables. The set of strong predictor variables (21 property values located at the region of 2-49 residues from the C-termini) was capable to classify all three types of non-classically secreted proteins with an accuracy of 93.3% for originally and 89.9% for cross-validated (leave-one-out procedure) grouped cases. The average error rate (0.137 ± 0.015) of k-fold (k = 3; 4; 6; 8; 10; 89) cross validation affirmed an acceptable prediction accuracy of defined discriminant functions with regard to the types of non-classically secreted proteins. The proposed prediction tool could be used to specify the secretome proteins from genomic sequences as well as to assess the compatibility between secretion pathways and secretion substrates of proteobacteria.