The effect of cellular receptor diffusion on receptor-mediated viral binding using Brownian adhesive dynamics (BRAD) simulations

Brownian adhesive dynamics (BRAD) is a new method for simulating the attachment of viruses to cell surfaces. In BRAD, the motion of the virus is subject to stochastic bond formation and breakage, and thermal motion owing to collisions from the solvent. In the model, the virus is approximated as a rigid sphere and the cell surface is approximated as a rigid plane coated with receptors. In this article, we extend BRAD to allow for the mobility of receptors in the plane of the membrane, both before and after they are ligated by viral attachment proteins. Allowing the proteins to move within the membrane produced several differences in behavior from when the receptors are immobilized. First, the mean steady-state bond number is unaffected by changes in cellular receptor density because proteins are now free to diffuse into the contact area, and the extent of binding is dictated by the availability of viral attachment proteins. Second, the time required to reach steady-state binding increases as both the cellular receptor number decreases and the receptor mobility decreases. This is because receptor diffusion is a slower process than the binding kinetics of the proteins. Decreasing the rate of protein binding was found to decrease the fraction of viruses bound to steady state, but not the extent of binding for those viruses that were bound. Increasing the binding rate increased the fraction of viruses bound, until no further viruses could bind. Alterations in receptor binding kinetics had no discernable effect on the mean steady-state bond number between virus and cell, because interactions were of sufficiently high affinity that all available receptor-viral attachment proteins were destined to bind at steady state.     

Docking enzyme-inhibitor complexes using a preference-based free-energy surface

We present a docking scheme that utilizes both a surface complementarity screen as well as an energetic criterion based on surface area burial. Twenty rigid enzyme/inhibitor complexes with known coordinate sets are arbitrarily separated and reassembled to an average all-atom rms (root mean square) deviation of 1.0 Å from the native complexes. Docking is accomplished by a hierarchical search of geometrically compatible triplets of surface normals on each molecule. A pruned tree of possible bound configurations is built up using successive consideration of larger and larger triplets. The best scoring configurations are then passed through a free-energy screen where the lowest energy member is selected as the predicted native state. The free energy approximation is derived from observations of surface burial by atom pairs across the interface of known enzyme/inhibitor complexes. The occurrence of specific atom-atom surface burial, for a set of complexes with well-defined secondary structure both in the bound and unbound states, is parameterized to mimic the free energy of binding. The docking procedure guides the inhibitor into its native state using orientation and distance-dependent functions that reproduce the ideal model of free energies with an average rms deviation of 0.9 kcal/mol. For all systems studied, this docking procedure identifies a single, unique minimum energy configuration that is highly compatible with the native state. © 1996 Wiley-Liss, Inc.     

Molecular modelling study of changes induced by netropsin binding to nucleosome core particles.

It is well known that certain sequence-dependent modulators in structure appear to determine the rotational positioning of DNA on the nucleosome core particle. That preference is rather weak and could be modified by some ligands as netropsin, a minor-groove binding antibiotic. We have undertaken a molecular modelling approach to calculate the relative energy of interaction between a DNA molecule and the protein core particle. The histones particle is considered as a distribution of positive charges on the protein surface that interacts with the DNA molecule. The molecular electrostatic potentials for the DNA, simulated as a discontinuous cylinder, were calculated using the values for all the base pairs. Computing these parameters, we calculated the relative energy of interaction and the more stable rotational setting of DNA. The binding of four molecules of netropsin to this model showed that a new minimum of energy is obtained when the DNA turns toward the protein surface by about 180 degrees, so a new energetically favoured structure appears where netropsin binding sites are located facing toward the histones surface. The effect of netropsin could be explained in terms of an induced change in the phasing of DNA on the core particle. The induced rotation is considered to optimize non-bonded contacts between the netropsin molecules and the DNA backbone.     

Specific adhesion of glycophorin liposomes to a lectin surface in shear flow.

The adhesion of cells to other cells or to surfaces by receptor-ligand binding in a shear field is an important aspect of many different biological processes and various cell separation techniques. The purpose of this study was to observe the adhesion of model cells with receptor molecules embedded in their surfaces to a ligand-coated surface under well-defined flow conditions in a parallel plate flow chamber. Liposomes containing glycophorin were used as the model cells to permit a variation in the adhesion parameters and then to observe the effect on adhesion. A mathematical model for cell sedimentation was created to predict the deposition time and the velocity preceding adhesion for the selection of experimental operating conditions and the methods useful for data analysis. The likelihood of cell attachment was represented by a quantity called the sticking probability which was defined as the inverse of the number of times a liposome made contact with the surface before attachment occurred. The sticking probability decreased as the cell receptor concentration was lowered from approximately 10(4) to 10(2) receptors per 4-microns diam liposome and as the shear rate increased from 5 to 22 s-1. The effect of the wall shear rate and particle diameter on detachment of liposomes from a surface was also observed.     

Cytochrome C interaction with cardiolipin/phosphatidylcholine model membranes: effect of cardiolipin protonation

Resonance energy transfer between anthrylvinyl-labeled phosphatidylcholine as a donor and heme moiety of cytochrome c (cyt c) as an acceptor has been employed to explore the protein binding to model membranes, composed of phosphatidylcholine and cardiolipin (CL). The existence of two types of protein-lipid complexes has been hypothesized where either deprotonated or partially protonated CL molecules are responsible for cyt c attachment to bilayer surface. To quantitatively describe cyt c membrane binding, the adsorption model based on scaled particle and double layer theories has been employed, with potential-dependent association constants being treated as a function of acidic phospholipid mole fraction, degree of CL protonation, ionic strength, and surface coverage. Multiple arrays of resonance energy transfer data obtained under conditions of varying pH, ionic strength, CL content, and protein/lipid molar ratio have been analyzed in terms of the model of energy transfer in two-dimensional systems combined with the adsorption model allowing for area exclusion and electrostatic effects. The set of recovered model parameters included effective protein charge, intrinsic association constants, and heme distance from the bilayer midplane for both types of protein-lipid complexes. Upon increasing CL mole fraction from 10 to 20 mol % (the value close to that characteristic of the inner mitochondrial membrane), the binding equilibrium dramatically shifted toward cyt c association with partially protonated CL species. The estimates of heme distance from bilayer center suggest shallow bilayer location of cyt c at physiological pH, whereas at pH below 6.0, the protein tends to insert into membrane core.     

Shear-driven rolling of DNA-adhesive microspheres

Many biologically important cell binding processes, such as the rolling of leukocytes in the vasculature, are multivalent, being mediated by large numbers of weak binding ligands. Quantitative agreement between experiments and models of rolling has been elusive and often limited by the poor understanding of the binding and unbinding kinetics of the ligands involved. Here, we present a cell-free experimental model for such rolling, consisting of polymer microspheres whose adhesion to a glass surface is mediated by ligands with well-understood force-dependent binding free energy—short complementary DNA strands. We observe robust rolling activity for certain values of the shear rate and the grafted DNA strands’ binding free energy and force sensitivity. The simulation framework developed to model leukocyte rolling, adhesive dynamics, quantitatively captures the mean rolling velocity and lateral diffusivity of the experimental particles using known values of the experimental parameters. Moreover, our model captures the velocity variations seen within the trajectories of single particles. Particle-to-particle variations can be attributed to small, plausible differences in particle characteristics. Overall, our findings confirm that state-of-the-art adhesive dynamics simulations are able to capture the complex physics of particle rolling, boding well for their extension to modeling more complex systems of rolling cells.     

A probabilistic model for ligand-cytoskeleton transmembrane adhesion: predicting the behavior of microspheres on the surface of migrating cells

A theoretical model describing the attachment and cytoskeletal coupling of microspheres to the dorsal surface of motile cells was developed. Integral membrane receptors beneath a ligand-coated microsphere are allowed to be either free, attached to the microsphere, bound to the rearward moving actin network, or linked to both the bead and the cytoskeleton, and to switch between these four states. The binding transitions being modeled as chemical reactions governed by rate constants taken from literature, the chance for a receptor to be in each binding state over time is obtained by solving mass-balance equations for the probability functions. The population of n such receptors beneath the microsphere is accounted for by a binomial distribution for each state. Adhesion and transmembrane coupling (resulting in microsphere transport) being defined by a minimal number of ligand-receptor and receptor-cytoskeleton bonds, respectively, the probabilities of attachment and transport of the microsphere over time are expressed in terms of state probability distributions. It is found that increasing the ligand density raises the attachment and transport probabilities, in good quantitative agreement with recent experiments using optical tweezers and accurate position tracking. Increasing the bead size does not affect attachment, but raises the transport probability with a marked transition for bead diameter around 100 nm, as for experimental data. Increasing the restraining force decreases the transport probability, probably by inducing a rupture of receptor-cytoskeleton bonds. This study thus provides a framework that helps understand the process of cortical flow associated with cell locomotion.     

Engineered antifouling microtopographies: the role of Reynolds number in a model that predicts attachment of zoospores of Ulva and cells of Cobetia marina

A correlation between the attachment density of cells from two phylogenetic groups (prokaryotic Bacteria and eukaryotic Plantae), with surface roughness is reported for the first time. The results represent a paradigm shift in the understanding of cell attachment, which is a critical step in the biofouling process. The model predicts that the attachment densities of zoospores of the green alga, Ulva, and cells of the marine bacterium, Cobetia marina, scale inversely with surface roughness. The size and motility of the bacterial cells and algal spores were incorporated into the attachment model by multiplying the engineered roughness index (ERI_II), which is a representation of surface energy, by the Reynolds number (Re) of the cells. The results showed a negative linear correlation of normalized, transformed attachment density for both organisms with ERI_II · Re (R ² = 0.77). These studies demonstrate for the first time that organisms respond in a uniform manner to a model, which incorporates surface energy and the Reynolds number of the organism.     

DNA conformation and energy in nucleosome core: a theoretical approach

DNA conformation in complex with proteins is far from its canonical B-form. The affinity of complex formation and structure of DNA depend on its attachment configuration and sequence. In this article, we develop a mechanical model to address the problem of DNA structure and energy under deformation. DNA in nucleosome core particle is described as an example. The structure and energy of nucleosomal DNA is calculated based on its sequence and positioning state. The inferred structure has remarkable similarity with X-ray data. Although there is no sequence-specific interaction of bases and the histone core, we found considerable sequence dependency for the nucleosomal DNA positioning. The affinity of nucleosome formation for several sequences is examined and the differences are compatible with observations. We argue that structural energy determines the natural state of nucleosomal DNA and is the main reason for affinity differences in vitro. This theory can be utilized for the DNA structure and energy determination in protein–DNA complexes in general.

An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:17     

Enzymes as molecular automata: a stochastic model of self-oscillatory glycolytic cycles in cellular metabolism

A stochastic model based on the molecular automata approach was developed to simulate the cyclic dynamics of glycolysis-gluconeogenesis in cell energy metabolism. The stochastic algorithm, based on Gillespie's method, simulates the master equation associated with any network of enzymatically controlled reactions. This model of the glycolytic-gluconeogenetic cycle was developed by assembling the stochastic kinetic reactions controlled by two enzymes: phosphofructokinase (PFKase) and fructose-1, 6-biphosphatase (FBPase). In order to obtain the hysteresis behaviour predicted by classical Sel'kov analysis, a minimum complexity is required in the allosteric regulations. This implies that the FBPase cannot have a single binding site for its transition to the inactive state (a minimum of two or three binding sites is necessary). Given the multimeric structure of this enzyme, this kinetic hypothesis is tenable. Other possible applications of the stochastic automata approach for different cases of channels, receptors and enzymatic control are suggested.     

A Deep-Structured Conditional Random Field Model for Object Silhouette Tracking

In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering.     

Green mussel Perna viridis L.: attachment behaviour and preparation of antifouling surfaces

The green mussel Perna viridis LINNE can be kept in simulated seawater for more than 6 months in good condition. The mussel forms many threads by secreting an adhesive protein from the foot, and attaches with more than 50 byssal threads, which makes most mussels clump together. In order to investigate the preparation of the antifouling surfaces toward green mussels, the attachment of mussels was tested using glass surfaces modified with silane coupling agents, together with non-treated material surfaces such as glass and silicone. The correlation between the attachment percentage and the mean number of the secreted byssus was highly significant, indicating that the mussel selects a favorable surface prior to the secretion of byssus. The relationships between the mussel attachment and the surface chemical parameters (surface free energy (sfe) and its dispersion and polar components) were examined based on a working hypothesis, which we have previously reported. The result of statistical regression test indicated that a certain correlation was found between the dispersion component and the mussel attachment, while the polar component did not correlate to the mussel attachment. The present surface chemical approach provided an additional clue for the preparation of ecologically clean antifouling materials that takes into account the combination of the wettability of both the marine adhesive proteins (MAP) and the modified surfaces.     

Free energies of binding of polychlorinated biphenyls to the estrogen receptor from a single simulation

Relative free energies of binding to the ligand-binding domain of the estrogen receptor have been calculated for a series of 17 hydroxylated polychlorinated biphenyls. Because traditional thermodynamic integration or perturbation approaches are hardly feasible for these numbers of compounds, the one-step perturbation approach is applied and is shown to yield accurate results based on only two 2-ns molecular dynamics simulations of an unphysical, judiciously chosen, reference state. The mean absolute difference between the calculated and experimental binding free energies for the 17 compounds is 3.4 kJ/mol, which illustrates the accuracy of the GROMOS biomolecular force field used. Excluding the three largest ligands from the comparison reduces the deviation to 2.0 kJ/mol (i.e., < k(B)T). Apart from the relative free energy, structural information about the binding mode and binding orientation for every compound can also be extracted from the simulation, showing that a ligand bound to its receptor cannot be represented by a single conformation, but it samples an ensemble of different orientations.     

Hierarchical sampling for metastable conformers determines biomolecular recognition: the case of malectin and diglucosylated N-glycan interactions

Structural information over the entire course of binding interactions based on the analyses of energy landscapes is described, which provides a framework to understand the events involved during biomolecular recognition. Conformational dynamics of malectin’s exquisite selectivity for diglucosylated N-glycan (Dig-N-glycan), a highly flexible oligosaccharide comprising of numerous dihedral torsion angles, are described as an example. For this purpose, a novel approach based on hierarchical sampling for acquiring metastable molecular conformations constituting low-energy minima for understanding the structural features involved in a biologic recognition is proposed. For this purpose, four variants of principal component analysis were employed recursively in both Cartesian space and dihedral angles space that are characterized by free energy landscapes to select the most stable conformational substates. Subsequently, k-means clustering algorithm was implemented for geometric separation of the major native state to acquire a final ensemble of metastable conformers. A comparison of malectin complexes was then performed to characterize their conformational properties. Analyses of stereochemical metrics and other concerted binding events revealed surface complementarity, cooperative and bidentate hydrogen bonds, water-mediated hydrogen bonds, carbohydrate–aromatic interactions including CH–π and stacking interactions involved in this recognition. Additionally, a striking structural transition from loop to β-strands in malectin CRD upon specific binding to Dig-N-glycan is observed. The interplay of the above-mentioned binding events in malectin and Dig-N-glycan supports an extended conformational selection model as the underlying binding mechanism.     

Pleiotrophin inhibits HIV infection by binding the cell surface-expressed nucleolin

The growth factor pleiotrophin (PTN) has been reported to bind heparan sulfate and nucleolin, two components of the cell surface implicated in the attachment of HIV-1 particles to cells. Here we show that PTN inhibits HIV-1 infection by its capacity to inhibit HIV-1 particle attachment to the surface of permissive cells. The beta-sheet domains of PTN appear to be implicated in this inhibitory effect on the HIV infection, in particular the domain containing amino acids 60-110. PTN binding to the cell surface is mediated by high and low affinity binding sites. Other inhibitors of HIV attachment known to bind specifically surface expressed nucleolin, such as the pseudopeptide HB-19 and the cytokine midkine prevent the binding of PTN to its low affinity-binding site. Confocal immunofluorescence laser microscopy revealed that the cross-linking of surface-bound PTN with a specific antibody results in the clustering of cell surface-expressed nucleolin and the colocalization of both PTN and nucleolin signals. Following its binding to surface-nucleolin, PTN is internalized by a temperature sensitive mechanism, a process which is inhibited by HB-19 and is independent of heparan and chondroitin sulfate proteoglycans. Nevertheless, proteoglycans might play a role in the concentration of PTN on the cell surface for a more efficient interaction with nucleolin. Our results demonstrate for the first time that PTN inhibits HIV infection and suggest that the cell surface-expressed nucleolin is a low affinity receptor for PTN binding to cells and it is also implicated in PTN entry into cells by an active process.     

Application of multiple response optimization design to quantum dot-encoded microsphere bioconjugates hybridization assay

The optimization of DNA hybridization for genotyping assays is a complex experimental problem that depends on multiple factors such as assay formats, fluorescent probes, target sequence, experimental conditions, and data analysis. Quantum dot-doped particle bioconjugates have been previously described as fluorescent probes to identify single nucleotide polymorphisms even though this advanced fluorescent material has shown structural instability in aqueous environments. To achieve the optimization of DNA hybridization to quantum dot-doped particle bioconjugates in suspension while maximizing the stability of the probe materials, a nonsequential optimization approach was evaluated. The design of experiment with response surface methodology and multiple optimization response was used to maximize the recovery of fluorescent probe at the end of the assay simultaneously with the optimization of target–probe binding. Hybridization efficiency was evaluated by the attachment of fluorescent oligonucleotides to the fluorescent probe through continuous flow cytometry detection. Optimal conditions were predicted with the model and tested for the identification of single nucleotide polymorphisms. The design of experiment has been shown to significantly improve biochemistry and biotechnology optimization processes. Here we demonstrate the potential of this statistical approach to facilitate the optimization of experimental protocol that involves material science and molecular biology.     

Endothelial nanoparticle binding kinetics are matrix and size dependent

Nanoparticles are increasingly important in medical research for application to areas such as drug delivery and imaging. Understanding the interactions of nanoparticles with cells in physiologically relevant environments is vital for their acceptance, and cell–particle interactions likely vary based on the design of the particle including its size, shape, and surface chemistry. For this reason, the kinetic interactions of fluorescent nanoparticles of sizes 20, 100, 200, and 500 nm with human umbilical vein endothelial cells (HUVEC) were determined by (1) measuring nanoparticles per cell at 37 and 4°C (to inhibit endocytosis) and (2) modeling experimental particle uptake data with equations describing particle attachment, detachment, and internalization. Additionally, the influence of cell substrate compliance on nanoparticle attachment and uptake was investigated. Results show that the number of binding sites per cell decreased with increasing nanoparticle size, while the attachment coefficient increased. By comparing HUVEC grown on either a thin coating of collagen or on top of three‐dimensional collagen hydrogel, nanoparticle attachment and internalization were shown to be influenced significantly by the substrate on which the cells are cultured. This study concludes that both particle size and cell culture substrate compliance appreciably influence the binding of nanoparticles; important factors in translating in vitro studies of nanoparticle interactions to in vivo studies focused on therapeutic or diagnostic applications. Biotechnol. Bioeng. 2011;108: 2988–2998. © 2011 Wiley Periodicals, Inc.     

Binding free energy landscape of domain-peptide interactions

Peptide recognition domains (PRDs) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell. How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood. We explore the peptide binding process of PDZ domains, a large PRD family, from an equilibrium perspective using an all-atom Monte Carlo (MC) approach. Our focus is two different PDZ domains representing two major PDZ classes, I and II. For both domains, a binding free energy surface with a strong bias toward the native bound state is found. Moreover, both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface. Consistent with this, various binding observables show a temperature dependence well described by a simple two-state model. We also find important differences in the details between the two domains. While both domains exhibit well-defined binding free energy barriers, the class I barrier is significantly weaker than the one for class II. To probe this issue further, we apply our method to a PDZ domain with dual specificity for class I and II peptides, and find an analogous difference in their binding free energy barriers. Lastly, we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions. These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I. Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial, rate-limiting step.