On the structure and dynamics of the complex of the nucleosome and the linker histone.

Several different models of the linker histone (LH)-nucleosome complex have been proposed, but none of them has unambiguously revealed the position and binding sites of the LH on the nucleosome. Using Brownian dynamics-based docking together with normal mode analysis of the nucleosome to account for the flexibility of two flanking 10 bp long linker DNAs (L-DNA), we identified binding modes of the H5-LH globular domain (GH5) to the nucleosome. For a wide range of nucleosomal conformations with the L-DNA ends less than 65 Å apart, one dominant binding mode was identified for GH5 and found to be consistent with fluorescence recovery after photobleaching (FRAP) experiments. GH5 binds asymmetrically with respect to the nucleosomal dyad axis, fitting between the nucleosomal DNA and one of the L-DNAs. For greater distances between L-DNA ends, docking of GH5 to the L-DNA that is more restrained and less open becomes favored. These results suggest a selection mechanism by which GH5 preferentially binds one of the L-DNAs and thereby affects DNA dynamics and accessibility and contributes to formation of a particular chromatin fiber structure. The two binding modes identified would, respectively, favor a tight zigzag chromatin structure or a loose solenoid chromatin fiber.

Brownian dynamics simulation of protein solutions: structural and dynamical properties.

The study of solutions of biomacromolecules provides an important basis for understanding the behavior of many fundamental cellular processes, such as protein folding, self-assembly, biochemical reactions, and signal transduction. Here, we describe a Brownian dynamics simulation procedure and its validation for the study of the dynamic and structural properties of protein solutions. In the model used, the proteins are treated as atomically detailed rigid bodies moving in a continuum solvent. The protein-protein interaction forces are described by the sum of electrostatic interaction, electrostatic desolvation, nonpolar desolvation, and soft-core repulsion terms. The linearized Poisson-Boltzmann equation is solved to compute electrostatic terms. Simulations of homogeneous solutions of three different proteins with varying concentrations, pH, and ionic strength were performed. The results were compared to experimental data and theoretical values in terms of long-time self-diffusion coefficients, second virial coefficients, and structure factors. The results agree with the experimental trends and, in many cases, experimental values are reproduced quantitatively. There are no parameters specific to certain protein types in the interaction model, and hence the model should be applicable to the simulation of the behavior of mixtures of macromolecules in cell-like crowded environments.

Cross-species analysis of the glycolytic pathway by comparison of molecular interaction fields.

The electrostatic potential of an enzyme is a key determinant of its substrate interactions and catalytic turnover. Here we invoke comparative analysis of protein electrostatic potentials, along with sequence and structural analysis, to classify and characterize all the enzymes in an entire pathway across a set of different organisms. The electrostatic potentials of the enzymes from the glycolytic pathway of 11 eukaryotes were analyzed by qPIPSA (quantitative protein interaction property similarity analysis). The comparison allows the functional assignment of neuron-specific isoforms of triosephosphate isomerase from zebrafish, the identification of unusual protein surface interaction properties of the mosquito glucose-6-phosphate isomerase and the functional annotation of ATP-dependent phosphofructokinases and cofactor-dependent phosphoglycerate mutases from plants. We here show that plants possess two parallel pathways to convert glucose. One is similar to glycolysis in humans, the other is specialized to let plants adapt to their environmental conditions. We use differences in electrostatic potentials to estimate kinetic parameters for the triosephosphate isomerases from nine species for which published parameters are not available. Along the core glycolytic pathway, phosphoglycerate mutase displays the most conserved electrostatic potential. The largest cross-species variations are found for glucose-6-phosphate isomerase, enolase and fructose-1,6-bisphosphate aldolase. The extent of conservation of electrostatic potentials along the pathway is consistent with the absence of a single rate-limiting step in glycolysis.

On the contributions of diffusion and thermal activation to electron transfer between Phormidium laminosum plastocyanin and cytochrome f: Brownian dynamics simulations with explicit modeling of nonpolar desolvation interactions and electron transfer events.

The factors that determine the extent to which diffusion and thermal activation processes govern electron transfer (ET) between proteins are debated. The process of ET between plastocyanin (PC) and cytochrome f (CytF) from the cyanobacterium Phormidium laminosum was initially thought to be diffusion-controlled but later was found to be under activation control (Schlarb-Ridley, B. G.; et al. Biochemistry 2005, 44, 6232). Here we describe Brownian dynamics simulations of the diffusional association of PC and CytF, from which ET rates were computed using a detailed model of ET events that was applied to all of the generated protein configurations. The proteins were modeled as rigid bodies represented in atomic detail. In addition to electrostatic forces, which were modeled as in our previous simulations of protein-protein association, the proteins interacted by a nonpolar desolvation (hydrophobic) force whose derivation is described here. The simulations yielded close to realistic residence times of transient protein-protein encounter complexes of up to tens of microseconds. The activation barrier for individual ET events derived from the simulations was positive. Whereas the electrostatic interactions between P. laminosum PC and CytF are weak, simulations for a second cyanobacterial PC-CytF pair, that from Nostoc sp. PCC 7119, revealed ET rates influenced by stronger electrostatic interactions. In both cases, the simulations imply significant contributions to ET from both diffusion and thermal activation processes.

The acidic, glutamine-rich Mpn474 protein of Mycoplasma pneumoniae is surface exposed and covers the complete cell.

The protein Mpn474 encoded by the mpn474 gene of the human-pathogenic Mycoplasma pneumoniae contains 1033 amino acids and has an isoelectric point of 4.79, which is caused by the large excess of glutamic acid residues (11 %). Although the protein lacks recognizable export signals we showed by immuno-electron microscopy that Mpn474 is surface exposed, covering the cell completely. By combining cross-linking and careful treatment of the bacterial cells with Triton X-100, we found that this protein is weakly bound to the cell surface, while the true transmembrane protein Mpn141 (adhesin P1) is firmly attached under the same experimental conditions. A transposon mutant in the mpn474 gene, which has no obvious phenotype, served as negative control for the immunodetection.

webPIPSA: a web server for the comparison of protein interaction properties.

Protein molecular interaction fields are key determinants of protein functionality. PIPSA (Protein Interaction Property Similarity Analysis) is a procedure to compare and analyze protein molecular interaction fields, such as the electrostatic potential. PIPSA may assist in protein functional assignment, classification of proteins, the comparison of binding properties and the estimation of enzyme kinetic parameters. webPIPSA is a web server that enables the use of PIPSA to compare and analyze protein electrostatic potentials. While PIPSA can be run with downloadable software (see http://projects.eml.org/mcm/software/pipsa), webPIPSA extends and simplifies a PIPSA run. This allows non-expert users to perform PIPSA for their protein datasets. With input protein coordinates, the superposition of protein structures, as well as the computation and analysis of electrostatic potentials, is automated. The results are provided as electrostatic similarity matrices from an all-pairwise comparison of the proteins which can be subjected to clustering and visualized as epograms (tree-like diagrams showing electrostatic potential differences) or heat maps. webPIPSA is freely available at: http://pipsa.eml.org.

Calculating enzyme kinetic parameters from protein structures.

Enzyme kinetic parameters can differ between different species and isoenzymes for the same catalysed reaction. Computational approaches to calculate enzymatic kinetic parameters from the three-dimensional structures of proteins will be reviewed briefly here. Enzyme kinetic parameters may be derived by modelling and simulating the rate-determining process. An alternative, approximate, but more computationally efficient approach is the comparison of molecular interaction fields for experimentally characterized enzymes and those for which parameters should be determined. A correlation between differences in interaction fields and experimentally determined kinetic parameters can be used to determine parameters for orthologous enzymes from other species. The estimation of enzymatic kinetic parameters is an important step in setting up mathematical models of biochemical pathways in systems biology.

qPIPSA: relating enzymatic kinetic parameters and interaction fields.

BACKGROUND: The simulation of metabolic networks in quantitative systems biology requires the assignment of enzymatic kinetic parameters. Experimentally determined values are often not available and therefore computational methods to estimate these parameters are needed. It is possible to use the three-dimensional structure of an enzyme to perform simulations of a reaction and derive kinetic parameters. However, this is computationally demanding and requires detailed knowledge of the enzyme mechanism. We have therefore sought to develop a general, simple and computationally efficient procedure to relate protein structural information to enzymatic kinetic parameters that allows consistency between the kinetic and structural information to be checked and estimation of kinetic constants for structurally and mechanistically similar enzymes. RESULTS: We describe qPIPSA: quantitative Protein Interaction Property Similarity Analysis. In this analysis, molecular interaction fields, for example, electrostatic potentials, are computed from the enzyme structures. Differences in molecular interaction fields between enzymes are then related to the ratios of their kinetic parameters. This procedure can be used to estimate unknown kinetic parameters when enzyme structural information is available and kinetic parameters have been measured for related enzymes or were obtained under different conditions. The detailed interaction of the enzyme with substrate or cofactors is not modeled and is assumed to be similar for all the proteins compared. The protein structure modeling protocol employed ensures that differences between models reflect genuine differences between the protein sequences, rather than random fluctuations in protein structure. CONCLUSION: Provided that the experimental conditions and the protein structural models refer to the same protein state or conformation, correlations between interaction fields and kinetic parameters can be established for sets of related enzymes. Outliers may arise due to variation in the importance of different contributions to the kinetic parameters, such as protein stability and conformational changes. The qPIPSA approach can assist in the validation as well as estimation of kinetic parameters, and provide insights into enzyme mechanism.

Bridging from molecular simulation to biochemical networks.

How can we make the connection between the three-dimensional structures of individual proteins and understanding how complex biological systems involving many proteins work? The modelling and simulation of protein structures can help to answer this question for systems ranging from multimacromolecular complexes to organelles and cells. On one hand, multiscale modelling and simulation techniques are advancing to permit the spatial and temporal properties of large systems to be simulated using atomic-detail structures. On the other hand, the estimation of kinetic parameters for the mathematical modelling of biochemical pathways using protein structure information provides a basis for iterative manipulation of biochemical pathways guided by protein structure. Recent advances include the structural modelling of protein complexes on the genomic level, novel coarse-graining strategies to increase the size of the system and the time span that can be simulated, and comparative molecular field analyses to estimate enzyme kinetic parameters.

Comparison of the binding and reactivity of plant and mammalian peroxidases to indole derivatives by computational docking.

The oxidation of melatonin by the mammalian myeloperoxidase (MPO) provides protection against the damaging effects of reactive oxygen species. Indole derivatives, such as melatonin and serotonin, are also substrates of the plant horseradish peroxidase (HRP), but this enzyme exhibits remarkable differences from MPO in the specificity and reaction rates for these compounds. A structural understanding of the determinants of the reactivity of these enzymes to indole derivatives would greatly aid their exploitation for biosynthetic and drug design applications. Consequently, after validation of the docking procedure, we performed computational docking of melatonin and serotonin to structural models of the ferric and compound I and II (co I and co II, respectively) states of HRP and MPO. The substrates dock at the heme edge on the distal side, but with different orientations in the two proteins. The distal cavity is larger in MPO than in HRP; however, in MPO, the substrates make closer contacts with the heme involving ring stacking, whereas in HRP, no ring stacking is observed. The observed differences in substrate binding may contribute to the higher reaction rates and lower substrate specificity of MPO relative to those of HRP. The docking results, along with the previously measured heme-protein reduction potentials, suggest that the differentially lowered reaction rates of co II of HRP and MPO with respect to those of co I could stem from as yet undetermined conformational or electrostatic differences between the co I and co II states of MPO, which are absent in HRP.

Diffusional encounter of barnase and barstar.

We present an analysis of trajectories from Brownian dynamics simulations of diffusional protein-protein encounter for the well-studied system of barnase and barstar. This analysis reveals details about the optimal association pathways, the regions of the encounter complex, possible differences of the pathways for dissociation and association, the coupling of translational and rotation motion, and the effect of mutations on the trajectories. We found that a small free-energy barrier divides the energetically most favorable region into a region of the encounter complex above the barnase binding interface and a region around a second energy minimum near the RNA binding loop. When entering the region of the encounter complex from the region near the RNA binding loop, barstar has to change its orientation to increase the electrostatic attraction between the proteins. By concentrating the analysis on the successful binding trajectories, we found that the region of the second minimum is not essential for the binding of barstar to barnase. Nevertheless, this region may be helpful to steer barstar into the region of the encounter complex. When applying the same analysis to several barnase mutants, we found that single mutations may drastically change the free-energy landscape and may significantly alter the population of the two minima. Therefore, certain protein-protein pairs may require careful adaptation of the positions of encounter and transition states when interpreting mutation effects on kinetic rates of association and/or dissociation.

How optimal are the binding energetics of barnase and barstar?

The extracellular ribonuclease barnase and its intracellular inhibitor barstar bind fast and with high affinity. Although extensive experimental and theoretical studies have been carried out on this system, it is unclear what the relative importance of different contributions to the high affinity is and whether binding can be improved through point mutations. In this work, we first applied Poisson-Boltzmann electrostatic calculations to 65 barnase-barstar complexes with mutations in both barnase and barstar. The continuum electrostatic calculations with a van der Waals surface dielectric boundary definition result in the electrostatic interaction free energy providing the dominant contribution favoring barnase-barstar binding. The results show that the computed electrostatic binding free energy can be improved through mutations at W44/barstar and E73/barnase. Furthermore, the determinants of binding affinity were quantified by applying COMparative BINding Energy (COMBINE) analysis to derive quantitative structure-activity relationships (QSARs) for the 65 complexes. The COMBINE QSAR model highlights approximately 20 interfacial residue pairs as responsible for most of the differences in binding affinity between the mutant complexes, mainly due to electrostatic interactions. Based on the COMBINE model, together with Brownian dynamics simulations to compute diffusional association rate constants, several mutants were designed to have higher binding affinities than the wild-type proteins.

Concerted simulations reveal how peroxidase compound III formation results in cellular oscillations.

A major problem in mathematical modeling of the dynamics of complex biological systems is the frequent lack of knowledge of kinetic parameters. Here, we apply Brownian dynamics simulations, based on protein three-dimensional structures, to estimate a previously undetermined kinetic parameter, which is then used in biochemical network simulations. The peroxidase-oxidase reaction involves many elementary steps and displays oscillatory dynamics important for immune response. Brownian dynamics simulations were performed for three different peroxidases to estimate the rate constant for one of the elementary steps crucial for oscillations in the peroxidase-oxidase reaction, the association of superoxide with peroxidase. Computed second-order rate constants agree well with available experimental data and permit prediction of rate constants at physiological conditions. The simulations show that electrostatic interactions depress the rate of superoxide association with myeloperoxidase, bringing it into the range necessary for oscillatory behavior in activated neutrophils. Such negative electrostatic steering of enzyme-substrate association presents a novel control mechanism and lies in sharp contrast to the electrostatically-steered fast association of superoxide and Cu/Zn superoxide dismutase, which is also simulated here. The results demonstrate the potential of an integrated and concerted application of structure-based simulations and biochemical network simulations in cellular systems biology.

MolSurfer: A macromolecular interface navigator.

We describe the current status of the Java molecular graphics tool, MolSurfer. MolSurfer has been designed to assist the analysis of the structures and physico-chemical properties of macromolecular interfaces. MolSurfer provides a coupled display of two-dimensional (2D) maps of the interfaces generated with the ADS software and a three-dimensional (3D) view of the macromolecular structure in the Java PDB viewer, WebMol. The interfaces are analytically defined and properties such as electrostatic potential or hydrophobicity are projected on to them. MolSurfer has been applied previously to analyze a set of 39 protein-protein complexes, with structures available from the Protein Data Bank (PDB). A new application, described here, is the visualization of 75 interfaces in structures of protein-DNA and protein-RNA complexes. Another new feature is that the MolSurfer web server is now able to compute and map Poisson-Boltzmann electrostatic potentials of macromolecules onto interfaces. The MolSurfer web server is available at http://projects.villa-bosch.de/mcm/software/molsurfer.

ProSAT: functional annotation of protein 3D structures.

ProSAT (for Protein Structure Annotation Tool) is a tool to facilitate interactive visualization of non-structure-based functional annotations in protein 3D structures. It performs automated mapping of the functional annotations onto the protein structure and allows functional sites to be readily identified upon visualization. The current version of ProSAT can be applied to large datasets of protein structures for fast visual identification of active and other functional sites derived from the SwissProt and Prosite databases.

Biomolecular diffusional association.

The primary method for the computational study of biomolecular diffusional association is Brownian dynamics. Recent work has seen advances in the efficiency of computing association rates and in the accuracy of simulation models. New areas to which Brownian dynamics has been applied include protein polymerisation and protein adsorption to a surface. There has recently been particularly intense study of protein-protein association, and Brownian dynamics, together with other theoretical and experimental approaches, has led to new insights into the determinants of protein-protein binding kinetics.