Hermann-Georg Holzhütter

The speciation of the proteome

IntroductionIn proteomics a paradox situation developed in the last years. At one side it is basic knowledge that proteins are post-translationally modified and occur in different isoforms. At the other side the protein expression concept disclaims post-translational modifications by connecting protein names directly with function.DiscussionOptimal proteome coverage is today reached by bottom-up liquid chromatography/mass spectrometry. But quantification at the peptide level in shotgun or bottom-up approaches by liquid chromatography and mass spectrometry is completely ignoring that a special peptide may exist in an unmodified form and in several-fold modified forms. The acceptance of the protein species concept is a basic prerequisite for meaningful quantitative analyses in functional proteomics. In discovery approaches only top-down analyses, separating the protein species before digestion, identification and quantification by two-dimensional gel electrophoresis or protein liquid chromatography, allow the correlation between changes of a biological situation and function.ConclusionTo obtain biological relevant information kinetics and systems biology have to be performed at the protein species level, which is the major challenge in proteomics today.

Finding one's way in proteomics: a protein species nomenclature

Our knowledge of proteins has greatly improved in recent years, driven by new technologies in the fields of molecular biology and proteome research. It has become clear that from a single gene not only one single gene product but many different ones - termed protein species - are generated, all of which may be associated with different functions. Nonetheless, an unambiguous nomenclature for describing individual protein species is still lacking. With the present paper we therefore propose a systematic nomenclature for the comprehensive description of protein species. The protein species nomenclature is flexible and adaptable to every level of knowledge and of experimental data in accordance with the exact chemical composition of individual protein species. As a minimum description the entry name (gene name + species according to the UniProt knowledgebase) can be used, if no analytical data about the target protein species are available.

Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks

BackgroundIn recent years, constrained optimization – usually referred to as flux balance analysis (FBA) – has become a widely applied method for the computation of stationary fluxes in large-scale metabolic networks. The striking advantage of FBA as compared to kinetic modeling is that it basically requires only knowledge of the stoichiometry of the network. On the other hand, results of FBA are to a large degree hypothetical because the method relies on plausible but hardly provable optimality principles that are thought to govern metabolic flux distributions.ResultsTo augment the reliability of FBA-based flux calculations we propose an additional side constraint which assures thermodynamic realizability, i.e. that the flux directions are consistent with the corresponding changes of Gibbs free energies. The latter depend on metabolite levels for which plausible ranges can be inferred from experimental data. Computationally, our method results in the solution of a mixed integer linear optimization problem with quadratic scoring function. An optimal flux distribution together with a metabolite profile is determined which assures thermodynamic realizability with minimal deviations of metabolite levels from their expected values. We applied our novel approach to two exemplary metabolic networks of different complexity, the metabolic core network of erythrocytes (30 reactions) and the metabolic network iJR904 of Escherichia coli (931 reactions). Our calculations show that increasing network complexity entails increasing sensitivity of predicted flux distributions to variations of standard Gibbs free energy changes and metabolite concentration ranges. We demonstrate the usefulness of our method for assessing critical concentrations of external metabolites preventing attainment of a metabolic steady state.ConclusionOur method incorporates the thermodynamic link between flux directions and metabolite concentrations into a practical computational algorithm. The weakness of conventional FBA to rely on intuitive assumptions about the reversibility of biochemical reactions is overcome. This enables the computation of reliable flux distributions even under extreme conditions of the network (e.g. enzyme inhibition, depletion of substrates or accumulation of end products) where metabolite concentrations may be drastically altered.

Molecular dioxygen enters the active site of 12/15-lipoxygenase via dynamic oxygen access channels

Cells contain numerous enzymes that use molecular oxygen for their reactions. Often, their active sites are buried deeply inside the protein, which raises the question whether there are specific access channels guiding oxygen to the site of catalysis. Choosing 12/15-lipoxygenase as a typical example for such oxygen-dependent enzymes, we determined the oxygen distribution within the protein and defined potential routes for oxygen access. For this purpose, we have applied an integrated strategy of structural modeling, molecular dynamics simulations, site-directed mutagenesis, and kinetic measurements. First, we computed the 3D free-energy distribution for oxygen, which led to identification of four oxygen channels in the protein. All channels connect the protein surface with a region of high oxygen affinity at the active site. This region is localized opposite to the nonheme iron providing a structural explanation for the reaction specificity of this lipoxygenase isoform. The catalytically most relevant path can be obstructed by L367F exchange, which leads to a strongly increased Michaelis constant for oxygen. The blocking mechanism is explained in detail by reordering the hydrogen-bonding network of water molecules. Our results provide strong evidence that the main route for oxygen access to the active site of the enzyme follows a channel formed by transiently interconnected cavities whereby the opening and closure are governed by side chain dynamics.

Identification of HLA-B27-Restricted Peptides from the Chlamydia trachomatis Proteome with Possible Relevance to HLA-B27-Associated Diseases

The association of HLA-B27 with ankylosing spondylitis and reactive arthritis is the strongest one known between an MHC class I Ag and a disease. We have searched the proteome of the bacterium Chlamydia trachomatis for HLA-B27 binding peptides that are stimulatory for CD8+ cells both in a model of HLA-B27 transgenic mice and in patients. This was done by combining two biomathematical computer programs, the first of which predicts HLA-B27 peptide binding epitopes, and the second the probability of HLA-B27 peptide generation by the proteasome system. After preselection, immunodominant peptides were identified by Ag-specific flow cytometry. Using this approach we have identified for the first time nine peptides derived from different C. trachomatis proteins that are stimulatory for CD8+ T cells. Eight of these nine murine-derived peptides were recognized by cytotoxic T cells. The same strategy was used to identify B27-restricted chlamydial peptides in three patients with reactive arthritis. Eleven peptides were found to be stimulatory for patient-derived CD8+ T cells, of which eight overlapped those found in mice. Additionally, we applied the tetramer technology, showing that a B27/chlamydial peptide containing one of the chlamydial peptides stained CD8+ T cells in patients with Chlamydia-induced arthritis. This comprehensive approach offers the possibility of clarifying the pathogenesis of B27-associated diseases.

Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

BackgroundDespite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed.ResultsHere, we present a computational analysis of the metabolism of Plasmodiumfalciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasites metabolic network was embedded into that of its host (erythrocyte). Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment.ConclusionsThe results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

Evidence for the Existence of a Non-catalytic Modifier Site of Peptide Hydrolysis by the 20 S Proteasome

The 20 S proteasome is an endoprotease complex that preferentially cleaves peptides C-terminal of hydrophobic, basic, and acidic residues. Recently, we showed that these specific activities, classified as chymotrypsin-like, trypsin-like, and peptidylglutamyl peptide-hydrolyzing (PGPH) activity, are differently affected by Ritonavir, an inhibitor of human immunodeficiency virus-1 protease. Ritonavir competitively inhibited the chymotrypsin-like activity, whereas the trypsin-like activity was enhanced. Here we demonstrate that the Ritonavir-mediated up-regulation of the trypsin-like activity is not affected by specific active site inhibitors of the chymo-trypsin-like and PGPH activity. Moreover, we show that the mutual regulation of chymotrypsin-like and PGPH activities by their substrates as described previously by a “cyclical bite-chew” model is not affected by selective inhibitors of the respective active sites. These data challenge the bite-chew model and suggest that effectors of proteasome activity can act by binding to non-catalytic sites. Accordingly, we propose a kinetic “two-site modifier” model that assumes that the substrate (or effector) may bind to an active site as well as to a second non-catalytic modifier site. This model appears to be valid as it describes the complex kinetic effects of Ritonavir very well. Since Ritonavir partially inhibits major histocompatibility complex class I restricted antigen presentation, the postulated modifier site may be required to coordinate the active centers of the proteasome for the production of class I peptide ligands.

The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks

The dynamic behavior of metabolic networks is governed by numerous regulatory mechanisms, such as reversible phosphorylation, binding of allosteric effectors or temporal gene expression, by which the activity of the participating enzymes can be adjusted to the functional requirements of the cell. For most of the cellular enzymes, such regulatory mechanisms are at best qualitatively known, whereas detailed enzyme‐kinetic models are lacking. To explore the possible dynamic behavior of metabolic networks in cases of lacking or incomplete enzyme‐kinetic information, we present a computational approach based on structural kinetic modeling. We derive statistical measures for the relative impact of enzyme‐kinetic parameters on dynamic properties (such as local stability) and apply our approach to the metabolism of human erythrocytes. Our findings show that allosteric enzyme regulation significantly enhances the stability of the network and extends its potential dynamic behavior. Moreover, our approach allows to differentiate quantitatively between metabolic states related to senescence and metabolic collapse of the human erythrocyte. We think that the proposed method represents an important intermediate step on the long way from topological network analysis to detailed kinetic modeling of complex metabolic networks.

Quantifying the Contribution of the Liver to Glucose Homeostasis: A Detailed Kinetic Model of Human Hepatic Glucose Metabolism

Despite the crucial role of the liver in glucose homeostasis, a detailed mathematical model of human hepatic glucose metabolism is lacking so far. Here we present a detailed kinetic model of glycolysis, gluconeogenesis and glycogen metabolism in human hepatocytes integrated with the hormonal control of these pathways by insulin, glucagon and epinephrine. Model simulations are in good agreement with experimental data on (i) the quantitative contributions of glycolysis, gluconeogenesis, and glycogen metabolism to hepatic glucose production and hepatic glucose utilization under varying physiological states. (ii) the time courses of postprandial glycogen storage as well as glycogen depletion in overnight fasting and short term fasting (iii) the switch from net hepatic glucose production under hypoglycemia to net hepatic glucose utilization under hyperglycemia essential for glucose homeostasis (iv) hormone perturbations of hepatic glucose metabolism. Response analysis reveals an extra high capacity of the liver to counteract changes of plasma glucose level below 5 mM (hypoglycemia) and above 7.5 mM (hyperglycemia). Our model may serve as an important module of a whole-body model of human glucose metabolism and as a valuable tool for understanding the role of the liver in glucose homeostasis under normal conditions and in diseases like diabetes or glycogen storage diseases.

The virtual liver: a multidisciplinary, multilevel challenge for systems biology

The liver is the central metabolic organ in human physiology, with functions that are fundamentally important to the detoxification of xenobiotics (drugs), the maintenance of homeostasis of numerous blood metabolites, and the production of mediators of the acute phase response. Liver toxicity, whether actual or implied is the reason for the failure of a significant proportion of many promising novel medicines that consequently never reach the market, and diseases such as atherosclerosis, diabetes, and fatty liver diseases, that are a major burden on current health resources, are directly linked to functional and structural disorders of the liver. This article presents the concepts and approaches underpinning one of the most exciting and ambitious modeling projects in the field of systems biology and systems medicine. This major multidisciplinary research program is aimed at developing a whole‐organ model of the human liver, representing its central physiological functions under normal and pathological conditions The model will be composed of a larger battery of interconnected submodels representing liver anatomy and physiology, integrating processes across hierarchical levels in space, time, and structural organization. In this article, we outline the general architecture of the liver model and present first step taken to reach this ambitious goal. WIREs Syst Biol Med 2012 doi: 10.1002/wsbm.1158