Christopher H. Chang

The Energy Landscape for the Interaction of the Family 1 Carbohydrate-Binding Module and the Cellulose Surface is Altered by Hydrolyzed Glycosidic Bonds

A multiscale simulation model is used to construct potential and free energy surfaces for the carbohydrate-binding module [CBM] from an industrially important cellulase, Trichoderma reesei cellobiohydrolase I, on the hydrophobic face of a coarse-grained cellulose Ibeta polymorph. We predict from computation that the CBM alone exhibits regions of stability on the hydrophobic face of cellulose every 5 and 10 A, corresponding to a glucose unit and a cellobiose unit, respectively. In addition, we predict a new role for the CBM: specifically, that in the presence of hydrolyzed cellulose chain ends, the CBM exerts a thermodynamic driving force to translate away from the free cellulose chain ends. This suggests that the CBM is not only required for binding to cellulose, as has been known for two decades, but also that it has evolved to both assist the enzyme in recognizing a cellulose chain end and exert a driving force on the enzyme during processive hydrolysis of cellulose.

Brownian Dynamics and Molecular Dynamics Study of the Association between Hydrogenase and Ferredoxin from Chlamydomonas reinhardtii

The [FeFe] hydrogenase from the green alga Chlamydomonas reinhardtii can catalyze the reduction of protons to hydrogen gas using electrons supplied from photosystem I and transferred via ferredoxin. To better understand the association of the hydrogenase and the ferredoxin, we have simulated the process over multiple timescales. A Brownian dynamics simulation method gave an initial thorough sampling of the rigid-body translational and rotational phase spaces, and the resulting trajectories were used to compute the occupancy and free-energy landscapes. Several important hydrogenase-ferredoxin encounter complexes were identified from this analysis, which were then individually simulated using atomistic molecular dynamics to provide more details of the hydrogenase and ferredoxin interaction. The ferredoxin appeared to form reasonable complexes with the hydrogenase in multiple orientations, some of which were good candidates for inclusion in a transition state ensemble of configurations for electron transfer.

Proton transport in Clostridium pasteurianum [FeFe] hydrogenase I: a computational study.

To better understand the proton transport through the H2 production catalysts, the [FeFe] hydrogenases, we have undertaken a modeling and simulation study of the proton transfer processes mediated by amino acid side-chain residues in hydrogenase I from Clostridium pasteurianum. Free-energy calculation studies show that the side chains of two conserved glutamate residues, Glu-279 and Glu-282, each possess two stable conformations with energies that are sensitive to protonation state. Coordinated conformational changes of these residues can form a proton shuttle between the surface Glu-282 and Cys-299, which is the penultimate proton donor to the catalytic H-cluster. Calculated acid dissociation constants are consistent with a proton relay connecting the H-cluster to the bulk solution. The complete proton-transport process from the surface-disposed Glu-282 to Cys-299 is studied using coupled semiempirical quantum-mechanical/classical-mechanical dynamics. Two-dimensional free-energy maps show the mechanisms of proton transport, which involve Glu-279, Ser-319, and a short internal water relay to connect functionally Glu-282 with the H-cluster. The findings of conformational bistability, PT event coupling with pKa mismatch, and water participation have implications in the design of artificial water reduction or general electrocatalytic H2-production catalysts.

Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation

BackgroundHigher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.ResultsSimulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing β-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter kcat and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and β-amylase kcat and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.ConclusionsWe find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

Photons, Photosynthesis, and High-Performance Computing: Challenges, Progress, and Promise of Modeling Metabolism in Green Algae

The complexity associated with biological metabolism considered at a kinetic level presents a challenge to quantitative modeling. In particular, the relatively sparse knowledge of parameters for enzymes with known kinetic responses is problematic. The possible space of these parameters is of high-dimension, and sampling of such a space typifies a combinatorial explosion of possible dynamic states. However, with sufficient quantitative transcriptomics, proteomics, and metabolomics data at hand, these challenges could be met by high-performance software with sampling, fitting, and optimization capabilities. With this in mind, we present the High-Performance Systems Biology Toolkit HiPer SBTK, an evolving software package to simulate, fit, and optimize metabolite concentrations and fluxes within the space of rate and binding parameters associated with detailed enzyme kinetic models. We present our chosen modeling paradigm for the formulation of metabolic pathway models, the means to address the challenge of representing such models in a precise and persistent fashion using the standardized Systems Biology Markup Language, and our second-generation model of H2-associated Chlamydomonas metabolism. Processing of such models for hierarchically parallelized simulation and optimization, job specification by the user through a GUI interface, software capabilities and initial scaling data, and the mapping of the computation to biological questions is also discussed. Moreover, we present near-term future software and model development goals.

Addressing unknown constants and metabolic network behaviors through petascale computing: understanding H2 production in green algae

The Genomics Revolution has resulted in a massive and growing quantity of whole-genome DNA sequences, which encode the metabolic catalysts necessary for life. However, gene annotations can rarely be complete, and measurement of the kinetic constants associated with the encoded enzymes can not possibly keep pace, necessitating the use of careful modeling to explore plausible network behaviors. Key challenges are (1) quantitatively formulating kinetic laws governing each transformation in a fixed model network; (2) characterizing the stable solution (if any) of the associated ordinary differential equations (ODEs); (3) fitting the latter to metabolomics data as it becomes available; and, (4) optimizing a model output against the possible space of kinetic parameters, with respect to properties such as robustness of network response, or maximum consumption/production. This SciDAC-2 project addresses this large-scale uncertainty in the genome-scale metabolic network of the water-splitting, H2-producing green alga Chlamydomonas reinhardtii. Each metabolic transformation is formulated as an irreversible steady-state process, such that the vast literature on known enzyme mechanisms may be incorporated directly. To start, glycolysis, the tricarboxylic acid cycle, and basic fermentation pathways have been encoded in Systems Biology Markup Language (SBML) with careful annotation and consistency with the KEGG database, yielding a model with 3 compartments, 95 species, 38 reactions, and 109 kinetic constants. To study and optimize such models with a view toward larger models, we have developed a system which takes as input an SBML model, and automatically produces C code that when compiled and executed optimizes the models kinetic parameters according to test criteria. We describe the system and present numerical results. Further development, including overlaying of a parallel multistart algorithm, will allow optimization of thousands of parameters on high-performance systems ranging from distributed grids to unified petascale architectures.

Enhancing a Pathway-Genome Database (PGDB) to capture subcellular localization of metabolites and enzymes: the nucleotide-sugar biosynthetic pathways of Populus trichocarpa.

Understanding how cellular metabolism works and is regulated requires that the underlying biochemical pathways be adequately represented and integrated with large metabolomic data sets to establish a robust network model. Genetically engineering energy crops to be less recalcitrant to saccharification requires detailed knowledge of plant polysaccharide structures and a thorough understanding of the metabolic pathways involved in forming and regulating cell-wall synthesis. Nucleotide-sugars are building blocks for synthesis of cell wall polysaccharides. The biosynthesis of nucleotide-sugars is catalyzed by a multitude of enzymes that reside in different subcellular organelles, and precise representation of these pathways requires accurate capture of this biological compartmentalization. The lack of simple localization cues in genomic sequence data and annotations however leads to missing compartmentalization information for eukaryotes in automatically generated databases, such as the Pathway-Genome Databases (PGDBs) of the SRI Pathway Tools software that drives much biochemical knowledge representation on the internet. In this report, we provide an informal mechanism using the existing Pathway Tools framework to integrate protein and metabolite sub-cellular localization data with the existing representation of the nucleotide-sugar metabolic pathways in a prototype PGDB for Populus trichocarpa. The enhanced pathway representations have been successfully used to map SNP abundance data to individual nucleotide-sugar biosynthetic genes in the PGDB. The manually curated pathway representations are more conducive to the construction of a computational platform that will allow the simulation of natural and engineered nucleotide-sugar precursor fluxes into specific recalcitrant polysaccharide(s). Database URL: The curated Populus PGDB is available in the BESC public portal at http://cricket.ornl.gov/cgi-bin/beocyc_home.cgi and the nucleotide-sugar biosynthetic pathways can be directly accessed at http://cricket.ornl.gov:1555/PTR/new-image?object=SUGAR-NUCLEOTIDES.

Simulation, Characterization, and Optimization of Metabolic Models with the High Performance Systems Biology Toolkit

The High Performance Systems Biology Toolkit (HiPer SBTK) is a collection of simulation and optimization components for metabolic modeling and the means to assemble these components into large parallel processing hierarchies suiting a particular simulation and optimization need. The components come in a variety of different categories: model translation, model simulation, parameter sampling, sensitivity analysis, parameter estimation, and optimization. They can be configured at runtime into hierarchically parallel arrangements to perform nested combinations of simulation characterization tasks with excellent parallel scaling to thousands of processors. We describe the observations that led to the system, the components, and how one can arrange them. We show nearly 90% efficient scaling to over 13,000 processors, and we demonstrate three complex yet typical examples that have run on

Theoretical Studies on Conjugated Phenyl-Cored Thiophene Dendrimers for Photovoltaic Applications

\sim

Calculated Hydride Donor Abilities of Five-Coordinate Transition Metal Hydrides [HM(diphosphine)2]+ (M = Ni, Pd, Pt) as a Function of the Bite Angle and Twist Angle of Diphosphine Ligands

1000 processors and accomplished billions of stiff ordinary differential equation simulations. This work opens the door for the systems biology metabolic modeling community to take effective advantage of large scale high performance computing resources for the first time.