Prediction of enzyme kinetic parameters based on statistical learning

Values of enzyme kinetic parameters are a key requisite for the kinetic modelling of biochemical systems. For most kinetic parameters, however, not even an order of magnitude is known, so the estimation of model parameters from experimental data remains a major task in systems biology. We propose a statistical approach to infer values for kinetic parameters across species and enzymes making use of parameter values that have been measured under various conditions and that are nowadays stored in databases. We fit the data by a statistical regression model in which the substrate, the combination enzyme-substrate and the combination organism-substrate have a linear effect on the logarithmic parameter value. As a result, we obtain predictions and error ranges for unknown enzyme parameters. We apply our method to decadic logarithmic Michaelis-Menten constants from the BRENDA database and confirm the results with leave-one-out crossvalidation, in which we mask one value at a time and predict it from the remaining data. For a set of 8 metabolites we obtain a standard prediction error of 1.01 for the deviation of the predicted values from the true values, while the standard deviation of the experimental values is 1.16. The method is applicable to other types of kinetic parameters for which many experimental data are available.

Metabolic enzymes that bind RNA: yet another level of cellular regulatory network?

Several enzymes that were originally characterized to have one defined function in intermediatory metabolism are now shown to participate in a number of other cellular processes. Multifunctional proteins may be crucial for building of the highly complex networks that maintain the function and structure in the eukaryotic cell possessing a relatively low number of protein-encoding genes. One facet of this phenomenon, on which I will focus in this review, is the interaction of metabolic enzymes with RNA. The list of such enzymes known to be associated with RNA is constantly expanding, but the most intriguing question remains unanswered: are the metabolic enzyme-RNA interactions relevant in the regulation of cell metabolism? It has been proposed that metabolic RNA-binding enzymes participate in general regulatory circuits linking a metabolic function to a regulatory mechanism, similar to the situation of the metabolic enzyme aconitase, which also functions as iron-responsive RNA-binding regulatory element. However, some authors have cautioned that some of such enzymes may merely represent "molecular fossils" of the transition from an RNA to a protein world and that the RNA-binding properties may not have a functional significance. Here I will describe enzymes that have been shown to interact with RNA (in several cases a newly discovered RNA-binding protein has been identified as a well-known metabolic enzyme) and particularly point out those whose ability to interact with RNA seems to have a proven physiological significance. I will also try to depict the molecular switch between an enzyme's metabolic and regulatory functions in cases where such a mechanism has been elucidated. For most of these enzymes relations between their enzymatic functions and RNA metabolism are unclear or seem not to exist. All these enzymes are ancient, as judged by their wide distribution, and participate in fundamental biochemical pathways.

Metabolism-directed optimisation of antithrombotics: the prodrug principle

Thromboembolic disorders are the major cause of mortality and morbidity in Western societies. Coagulation enzymes, such as thrombin, factor Xa and a tissue factor/factor VIIa complex, together with platelet GPIIb/IIIa receptors, are the focal point of attention in pharmaceutical research aimed at finding new antithrombotic agents. However, finding orally active drugs for these particular molecular targets has proved to be anything but straightforward. Thrombin, factor Xa, tissue factor/factor VIIa and platelet GPIIb/IIIa receptors display a preference for molecules containing highly basic arginine and/or acidic aspartate moieties, which are, however, associated with poor bioavailability after oral application. Different approaches have been taken to achieve favourable absorption, metabolism, distribution and clearance, without compromising the antithrombotic activity of the compounds. This review highlights the use of the prodrug principle in optimising antithrombotic agents.

In vitro homogeneous and heterogeneous degradation of poly(ε-caprolactone/polyethylene glycol/L-lactide): The absence of autocatalysis and the role of enzymes

This study investigated the in vitro degradation behavior of poly(epsilon-caprolactone/polyethylene glycol/L-lactide) (PCEL) in comparison with that of three other biodegradable polymers. Polymer membranes were incubated in pancreatin solution, Ringer's solution, and distilled water at 37 degrees C for up to 20 weeks. Characterization involved measuring weight loss, observing the morphological changes by scanning electron microscopy (SEM), analyzing molecular weight using size exclusion chromatography (SEC), and studying the crystalline structure using differential scanning calorimetry (DSC). The hydrolysis in a simple aqueous solution experienced no autocatalysis, which was attributed to the high permeability of PCEL to water-soluble degradation products. Similar degradation rates were recorded for the PCEL and poly(L,L-lactide) (PLLA) test membranes. In the presence of pancreatin, the PCEL membrane experienced rapid heterogeneous surface erosion likely caused by the selective loss of its surface PEG components under enzymatic action. Pancreatin also substantially increased the even physical resorption of the other test polymers by eliminating autocatalysis. This study demonstrated that autocatalysis commonly experienced by poly(alpha-hydroxyl acid) can be reduced through chemical formulation or high enzyme activity.

In silico discovery of enzyme-substrate specificity-determining residue clusters

The binding between an enzyme and its substrate is highly specific, despite the fact that many different enzymes show significant sequence and structure similarity. There must be, then, substrate specificity-determining residues that enable different enzymes to recognize their unique substrates. We reason that a coordinated, not independent, action of both conserved and non-conserved residues determine enzymatic activity and specificity. Here, we present a surface patch ranking (SPR) method for in silico discovery of substrate specificity-determining residue clusters by exploring both sequence conservation and correlated mutations. As case studies we apply SPR to several highly homologous enzymatic protein pairs, such as guanylyl versus adenylyl cyclases, lactate versus malate dehydrogenases, and trypsin versus chymotrypsin. Without using experimental data, we predict several single and multi-residue clusters that are consistent with previous mutagenesis experimental results. Most single-residue clusters are directly involved in enzyme-substrate interactions, whereas multi-residue clusters are vital for domain-domain and regulator-enzyme interactions, indicating their complementary role in specificity determination. These results demonstrate that SPR may help the selection of target residues for mutagenesis experiments and, thus, focus rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.

A Review of Biological Factors Implicated in Abdominal Aortic Aneurysm Rupture

Abdominal aortic aneurysm (AAA) rupture is the 13th commonest cause of death in the Western World. Although considerable research has been applied to the aetiology and mechanism of aneurysm expansion, little is known about the mechanism of rupture. Aneurysm rupture was historically considered to be a simple physical process that occurred when the aortic wall could no longer contain the haemodynamic stress of the circulation. However, AAAs do not conform to the law of Laplace and there is growing evidence that aneurysm rupture involves a complex series of biological changes in the aortic wall. This paper reviews the available data on patient variables associated with aneurysm rupture and presents the evidence implicating biological factors in AAA rupture.

Active site identification through geometry-based and sequence profile-based calculations: burial of catalytic clefts

Electrostatics calculations with proteins that are uniformly charged over volume can aid enzyme/non-enzyme discrimination. For known enzymes, such methods locate active sites to within 5% on the enzyme surface, in 77% of a test set. We now report that removing the dielectric boundary improves active site location to 80%, with optimal discrimination between enzymes and non-enzymes of around 80% specificity and 80% sensitivity. This calculation quantifies burial of solvent-accessible regions. Many of the true enzymes incorrectly assigned as non-enzymes have active sites at subunit boundaries. These are missed in monomer-based calculations. Catalytic and non-catalytic antibodies are studied in this context of active/binding site burial. Whilst catalytic antibodies, on average, have marginally higher active site burial than non-catalytic antibodies, these values are generally smaller than for non-antibody enzymes, possibly contributing to their relatively low turnover. Prediction of active site location improves further when sequence profile-based weights replace the uniform charge distribution, so that a combination of burial and amino acid conservation is assessed. Accuracy rises to 93% of active sites to within 5%, in the test set, for the optimal profile weights scheme. The equivalent value in a separate validation set is 89% to within 5%. Enzyme/non-enzyme and enzyme functional site predictions are made for structural genomics proteins, suggesting that a substantial majority of these are non-enzymes.

Role of COX-2, thromboxane A2 synthase, and prostaglandin I2 synthase in papillary thyroid carcinoma growth

The development of papillary thyroid carcinoma is influenced by many factors including genetic alterations, growth factors, and physical agents such as radiation. Arachidonic acid and its derivatives including prostaglandins (PG) and thromboxane along with the enzymes involved in their synthesis have been shown to influence the growth of various tumors. We analyzed the immunoreactivity for cyclooxygenase-2 (COX-2) and mRNA expression levels of the enzymes COX-2, thromboxane A(2) (TXA(2)) synthase, and PGI(2) synthase by RT-PCR in papillary carcinomas and matching normal tissues to determine the role of these enzymes in the development of papillary thyroid carcinomas. A papillary thyroid carcinoma cell line TPC-1 was also studied in vitro to determine the role of the specific COX-2 inhibitor NS-398 on COX-2 and vascular endothelial growth factor-A, since COX-2 also has a role in regulating tumor angiogenesis. RT-PCR analysis showed significant increases in TXA(2) synthase mRNA levels in papillary thyroid carcinomas compared to normal thyroid tissues. Although COX-2 mRNA levels were generally increased in papillary carcinomas, the differences were not statistically significant. There were no significant differences in PGI(2) synthase mRNA levels. COX-2 protein expression was greater in papillary carcinoma compared to normal thyroid tissues; however, the levels were quite variable. In vitro studies with a COX-2 inhibitor, NS-398, showed inhibition of tumor growth along with increased levels of COX-2 and vascular endothelial growth factor-A mRNA expression. These results indicate that specific enzyme levels in the PG synthesis pathway such as TXA(2) synthase are increased in papillary thyroid carcinomas. COX-2 also has a role in papillary thyroid growth, since a specific inhibitor of COX-2 regulates papillary thyroid carcinoma cell proliferation. These results implicate several enzymes in the synthesis of prostanoids as regulators of thyroid papillary carcinoma proliferation and suggest that increased levels of expression of these enzymes may play a role in the pathogenesis of these tumors.

Reconstruction of phylogenetic relationships from metabolic pathways based on the enzyme hierarchy and the gene ontology

There has been much interest in the structural comparison and alignment of metabolic pathways. Several techniques have been conceived to assess the similarity of metabolic pathways of different organisms. In this paper, we show that the combination of a new heuristic algorithm for the comparison of metabolic pathways together with any of three enzyme similarity measures (hierarchical, information content, and gene ontology) can be used to derive a metabolic pathway similarity measure that is suitable for reconstructing phylogenetic relationships from metabolic pathways. Experimental results on the Glycolysis pathway of 73 organisms representing the three domains of life show that our method outperforms previous techniques.

[Emergence and biological complexity]

Biological networks possess an organization that expresses their potential information. A function, I(X:Y)N, called mutual information of integration, define, on a quantitative basis, three types of organization. If I(X:Y)N=0, the properties of the global system XY can be reduced to the properties of its component sub-systems X and Y. Hence, XY is not a real system displaying collective properties but the mere collection of X and Y. Its properties are the properties of the sub-systems X and Y. If I(X:Y)N>0, the system is integrated. Although it behaves as a coherent whole, it does not possess many collective properties. Last, if I(X:Y)N<0, the system possesses emergent collective properties and can be considered complex for it possesses many collective properties that cannot be predicted from the independent study of component sub-systems X and Y. In a biological system, the emergence of information usually means the emergence of a novel function. This is probably what is occurring with enzymes. If a protein binds two ligands able to interact, and if the condition above is fulfilled, then the protein behaves as an enzyme able to allow a catalytic reaction between the two reagents.

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Gibberellin biosynthesis in fungi: genes, enzymes, evolution, and impact on biotechnology

Gibberellins (GAs) constitute a large family of tetracyclic diterpenoid carboxylic acids, some members of which function as growth hormones in higher plants. As well as being phytohormones, GAs are also present in some fungi and bacteria. In recent years, GA biosynthetic genes from Fusarium fujikuroi and Arabidopsis thaliana have been cloned and well characterised. Although higher plants and the fungus both produce structurally identical GAs, there are important differences indicating that GA biosynthetic pathways have evolved independently in higher plants and fungi. The fact that horizontal gene transfer of GA genes from the plant to the fungus can be excluded, and that GA genes are obviously missing in closely related Fusarium species, raises the question of the origin of fungal GA biosynthetic genes. Besides characterisation of F. fujikuroi GA pathway genes, much progress has been made in the molecular analysis of regulatory mechanisms, especially the nitrogen metabolite repression controlling fungal GA biosynthesis. Basic research in this field has been shown to have an impact on biotechnology. Cloning of genes, construction of knock-out mutants, gene amplification, and regulation studies at the molecular level are powerful tools for improvement of production strains. Besides increased yields of the final product, GA3, it is now possible to produce intermediates of the GA biosynthetic pathway, such as ent-kaurene, ent-kaurenoic acid, and GA14, in high amounts using different knock-out mutants. This review concentrates mainly on the fungal biosynthetic pathway, the genes and enzymes involved, the regulation network, the biotechnological relevance of recent studies, and on evolutionary aspects of GA biosynthetic genes.

EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference

EFICAz (Enzyme Function Inference by Combined Approach) is an automatic engine for large-scale enzyme function inference that combines predictions from four different methods developed and optimized to achieve high prediction accuracy: (i) recognition of functionally discriminating residues (FDRs) in enzyme families obtained by a Conservation-controlled HMM Iterative procedure for Enzyme Family classification (CHIEFc), (ii) pairwise sequence comparison using a family specific Sequence Identity Threshold, (iii) recognition of FDRs in Multiple Pfam enzyme families, and (iv) recognition of multiple Prosite patterns of high specificity. For FDR (i.e. conserved positions in an enzyme family that discriminate between true and false members of the family) identification, we have developed an Evolutionary Footprinting method that uses evolutionary information from homofunctional and heterofunctional multiple sequence alignments associated with an enzyme family. The FDRs show a significant correlation with annotated active site residues. In a jackknife test, EFICAz shows high accuracy (92%) and sensitivity (82%) for predicting four EC digits in testing sequences that are <40% identical to any member of the corresponding training set. Applied to Escherichia coli genome, EFICAz assigns more detailed enzymatic function than KEGG, and generates numerous novel predictions.

Inorganic polyphosphate in Bacillus cereus: motility, biofilm formation, and sporulation

Chains of inorganic polyphosphate (poly-P) with hundreds of P(i) residues linked by phosphoanhydride bonds, as in ATP, are found in every bacterial, fungal, plant, and animal cell, in which they perform various functions. In the spore-forming Bacillus cereus, we have identified three principal enzymes and genes involved in the metabolism of poly-P, namely, (i) poly-P kinase (PPK), which synthesizes poly-P reversibly from ATP, (ii) exopolyphosphatase (PPX), which hydrolyzes poly-P to P(i), and (iii) poly-P/AMP phosphotransferase (PAP), which uses poly-P as a donor to convert AMP to ADP, reversibly. In the null mutant of ppk, poly-P levels are reduced to <5% of the WT; in the ppx mutant, the PPK activity is elevated 10-fold, and the accumulation of poly-P is elevated approximately 1,000-fold. All of the null mutants of ppk, ppx, and pap showed defects in motility and biofilm formation, but sporulation efficiency was impaired only in the ppx mutant. These enzymes and genes in B. cereus are nearly identical to those in the very closely related pathogen Bacillus anthracis, and, thus, they may provide attractive targets for the treatment of anthrax.

Links between DNA replication and recombination in prokaryotes

Recombination plays a crucial role in underpinning genome duplication, ensuring that replication blocks are removed or bypassed, and that the replication machinery is subsequently reloaded back onto the DNA. Recent studies have identified a surprising variety of ways in which damaged replication forks are repaired and have shown that the mechanism used depends on the nature of the original blocking lesion. Indeed, an emerging theme is that a single recombination enzyme or complex can perform highly varied tasks, depending on the context of the recombination reaction.

Efficient extraction of mapping rules of atoms from enzymatic reaction data

Many computational problems and methods have been proposed for analysis of biological pathways. Among them, this paper focuses on extraction of mapping rules of atoms from enzymatic reaction data, which is useful for drug design, simulation of tracer experiments, and consistency checking of pathway databases. Most of existing methods for this problem are based on maximal common subgraph algorithms. In this paper, we propose a novel approach based on graph partition and graph isomorphism. We show that this problem is NP-hard in general, but can be solved in polynomial time for wide classes of enzymatic reactions. We also present an O(n(1.5)) time algorithm for a special but fundamental class of reactions, where n is the maximum size of compounds appearing in a reaction. We develop practical polynomial-time algorithms in which the Morgan algorithm is used for computing the normal form of a graph, where it is known that the Morgan algorithm works correctly for most chemical structures. Computational experiments are performed for these practical algorithms using the chemical reaction data stored in the KEGG/LIGAND database. The results of computational experiments suggest that practical algorithms are useful in many cases.

Selenium and selenoproteins in mammals: extraordinary, essential, enigmatic

Selenium (Se), once known only for its potential toxicity, is now well established as an essential trace element for mammals. Insufficient Se intake predisposes to and manifests in a variety of diseases. Recent studies have proven that it is the synthesis of selenocysteine (Sec)-containing proteins, designated selenoproteins, which represents an essential prerequisite for regular development and a long and healthy life. New transgenic mouse models analysing those selenoproteins with proven enzymatic functions displayed particular phenotypes and highlighted essential Se-dependent processes in development, growth or against specific challenges. While there is a growing molecular understanding of and general agreement on the importance of sufficiently high Se intake and undisturbed selenoprotein biosynthesis, many of the recently identified selenoproteins are still uncharacterised, and the effects and consequences of supra-physiological Se dosages are not biochemically understood. With the recent definition of the human and mouse selenoproteomes and a growing number of available tools, the Se field is now geared for a great leap forward. Se biology has already broadened our knowledge about the genetic code and about protein translation. It now holds great promises also for a better understanding of some key aspects of cancer, inflammation, fertility and prevention of age-associated diseases.

Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming

This paper describes recent insights into the role of implicit context within the representations of evolving artefacts and specifically within the program representation used by enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own definition and is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and presents experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context within representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.

A perspective on cold enzymes: current knowledge and frequently asked questions

Studies on psychrophilic enzymes to determine the structural features important for cold-activity have attracted increased attention in the last few years. This enhanced interest is due to the attractive properties of such proteins, i.e. a high specific activity and a low thermal stability, and thus, these enzymes constitute a tremendous potential for fundamental research and biotechnological applications. This review examines the impact of low temperatures on life, the diversity of adaptation to counteract these effects and gives an overview of the features proposed to account for low thermal stability and cold-activity, following the chronological order of the catalytic cycle phases. Moreover, we present an overview of recent techniques used in the analysis of the flexibility of a protein structure which is an important concept in cold-adaptation; an overview of biotechnological potential of psychrophilic enzymes and finally, a few frequently asked questions about cold-adaptation and their possible answers.

Genome wide analysis of TNF-inducible genes reveals that antioxidant enzymes are induced by TNF and responsible for elimination of ROS

We recently showed that TNF induces accumulation of reactive oxygen species (ROS) that mediates necrosis in murine embryonic fibroblasts (MEFs) derived from TRAF2- and TRAF5-double deficient (DKO) mice. To elucidate the defects that subsequently cause accumulation of ROS in DKO MEFs, we compared gene expression profiles of wild-type and DKO MEFs before and after TNF stimulation using cDNA microarrays. Interestingly, many antioxidant enzymes are induced by TNF in wild-type MEFs, induction of these genes is impaired in DKO MEFs. Taken that TNF induces accumulation of ROS in DKO, but not wild-type MEFs, upregulation of antioxidant enzyme(s) might play a crucial role in elimination of ROS.

Interdomain communications in bifunctional enzymes: how are different activities coordinated?

Although bifunctional enzymes containing two different active centers located within separate domains are quite common in living systems, the significance of this bifunctionality is not always clear, and the molecular mechanisms of site-site interactions in such complex systems have come under the scrutiny of science only in recent years. This review summarizes recent data on the mechanisms of communication between active centers in bifunctional enzymes. Three types of enzymes are considered: (1) those catalyzing consecutive reactions of a metabolic pathway and exhibiting substrate channeling (glutamate synthase and imidazole glycerol phosphate synthase), (2) those catalyzing consecutive reactions without substrate channeling (lysine-ketoglutarate reductase/saccharopine dehydrogenase), and (3) those catalyzing opposed reactions (6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase). The functional role of interdomain communications is briefly discussed.

Experimental Evidence for the Limiting Role of Enzymatic Reactions in Chemoattractant-induced Pseudopod Extension in Human Neutrophils

Chemoattractant-stimulated pseudopod growth in human neutrophils was used as a model system to study the rate-limiting mechanism of cytoskeleton rearrangement induced by activated G-protein-coupled receptors. Cells were activated with N-formyl-Met-Leu-Phe, and the temperature dependence of the rate of pseudopod extension was measured in the presence of pharmacological inhibitors with known mechanisms of action. Three groups of inhibitors were used: (i) inhibitors sequestering substrates involved in F-actin polymerization (latrunculin A for G-actin and cytochalasin D for actin filament-free barbed ends) or sequestering secondary messengers (PIP-binding peptide for phosphoinositide lipids); (ii) competitively binding inhibitors (Akt-inhibitor for Akt/protein kinase B); and (iii) inhibitors that reduce enzyme activity (wortmannin for phosphoinositide 3-kinase and chelerythrine for protein kinase C). The experimental data are consistent with a model in which the relative involvement of a given pathway of F-actin polymerization to the measured rate of pseudopod extension is limited by a slowest (bottleneck) reaction in the cascade of reactions involved in the overall signaling pathway. The approach we developed was used to demonstrate that chemoattractant-induced pseudopod growth and mechanically stimulated cytoskeleton rearrangement are controlled by distinct pathways of F-actin polymerization.

Dietary antioxidants and exercise

Muscular exercise promotes the production of radicals and other reactive oxygen species in the working muscle. Growing evidence indicates that reactive oxygen species are responsible for exercise-induced protein oxidation and contribute to muscle fatigue. To protect against exercise-induced oxidative injury, muscle cells contain complex endogenous cellular defence mechanisms (enzymatic and non-enzymatic antioxidants) to eliminate reactive oxygen species. Furthermore, exogenous dietary antioxidants interact with endogenous antioxidants to form a cooperative network of cellular antioxidants. Knowledge that exercise-induced oxidant formation can contribute to muscle fatigue has resulted in numerous investigations examining the effects of antioxidant supplementation on human exercise performance. To date, there is limited evidence that dietary supplementation with antioxidants will improve human performance. Furthermore, it is currently unclear whether regular vigorous exercise increases the need for dietary intake of antioxidants. Clearly, additional research that analyses the antioxidant requirements of individual athletes is needed.