Molecular simulations of diffusion and association in multimacromolecular systems

Development of hybrid biomicroparticles: cellulose exposing functionalized fusion proteins

BACKGROUND

One of the leading current trends in technology is the miniaturization of devices to the microscale and nanoscale. The highly advanced approaches are based on biological systems, subjected to bioengineering using chemical, enzymatic and recombinant methods. Here we have utilised the biological affinity towards cellulose of the cellulose binding domain (CBD) fused with recombinant proteins.

RESULTS

Here we focused on fusions with 'artificial', concatemeric proteins with preprogrammed functions, constructed using DNA FACE™ technology. Such CBD fusions can be efficiently attached to micro-/nanocellulose to form functional, hybrid bionanoparticles. Microcellulose (MCC) particles were generated by a novel approach to enzymatic hydrolysis using Aspergillus sp. cellulase. The interaction between the constructs components - MCC, CBD and fused concatemeric proteins - was evaluated. Obtaining of hybrid biomicroparticles of a natural cellulose biocarrier with proteins with therapeutic properties, fused with CBD, was confirmed. Further, biological tests on the hybrid bioMCC particles confirmed the lack of their cytotoxicity on 46BR.1 N fibroblasts and human adipose derived stem cells (ASCs). The XTT analysis showed a slight inhibition of the proliferation of 46BR.1 N fibroblasts and ACSs cells stimulated with the hybrid biomicroparticles. However, in both cases no changes in the morphology of the examined cells after incubation with the hybrid biomicroparticles' MCC were detected.

CONCLUSIONS

Microcellulose display with recombinant proteins involves utilizing cellulose, a natural polymer found in plants, as a platform for presenting or displaying proteins. This approach harnesses the structural properties of cellulose to express or exhibit various recombinant proteins on its surface. It offers a novel method for protein expression, presentation, or immobilization, enabling various applications in biotechnology, biomedicine, and other fields. Microcellulose shows promise in biomedical fields for wound healing materials, drug delivery systems, tissue engineering scaffolds, and as a component in bio-sensors due to its biocompatibility and structural properties.

Investigating Intrinsically Disordered Proteins With Brownian Dynamics

Intrinsically disordered proteins (IDPs) have recently become systems of great interest due to their involvement in modulating many biological processes and their aggregation being implicated in many diseases. Since IDPs do not have a stable, folded structure, however, they cannot be easily studied with experimental techniques. Hence, conducting a computational study of these systems can be helpful and be complementary with experimental work to elucidate their mechanisms. Thus, we have implemented the coarse-grained force field for proteins (COFFDROP) in Browndye 2.0 to study IDPs using Brownian dynamics (BD) simulations, which are often used to study large-scale motions with longer time scales and diffusion-limited molecular associations. Specifically, we have checked our COFFDROP implementation with eight naturally occurring IDPs and have investigated five (Glu-Lys)25 IDP sequence variants. From measuring the hydrodynamic radii of eight naturally occurring IDPs, we found the ideal scaling factor of 0.786 for non-bonded interactions. We have also measured the entanglement indices (average C α distances to the other chain) between two (Glu-Lys)25 IDP sequence variants, a property related to molecular association. We found that entanglement indices decrease for all possible pairs at excess salt concentration, which is consistent with long-range interactions of these IDP sequence variants getting weaker at increasing salt concentration.

Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model

We recently proposed a computational procedure to simulate the dissociation of protein/ligand complexes using the dissociation Parallel Cascade Selection Molecular Dynamics simulation (dPaCS-MD) method and to analyze the generated trajectories using the Markov state model (MSM). This procedure, called dPaCS-MD/MSM, enables calculation of the dissociation free energy profile and the standard binding free energy. To examine whether this method can reproduce experimentally determined binding free energies for a variety of systems, we used it to investigate the dissociation of three protein/ligand complexes: trypsin/benzamine, FKBP/FK506, and adenosine A_2A receptor/T4E. First, dPaCS-MD generated multiple dissociation pathways within a reasonable computational time for all the complexes, although the complexes differed significantly in the size of the molecules and in intermolecular interactions. Subsequent MSM analyses produced free energy profiles for the dissociations, which provided insights into how each ligand dissociates from the protein. The standard binding free energies obtained by dPaCS-MD/MSM are in good agreement with experimental values for all the complexes. We conclude that dPaCS-MD/MSM can accurately calculate the binding free energies of these complexes.

Computational Insights into the Binding Mechanism of OxyS sRNA with Chaperone Protein Hfq

Under the oxidative stress condition, the small RNA (sRNA) OxyS that acts as essential post-transcriptional regulators of gene expression is produced and plays a regulatory function with the assistance of the RNA chaperone Hfq protein. Interestingly, experimental studies found that the N48A mutation of Hfq protein could enhance the binding affinity with OxyS while resulting in the defection of gene regulation. However, how the Hfq protein interacts with sRNA OxyS and the origin of the stronger affinity of N48A mutation are both unclear. In this paper, molecular dynamics (MD) simulations were performed on the complex structure of Hfq and OxyS to explore their binding mechanism. The molecular mechanics generalized born surface area (MM/GBSA) and interaction entropy (IE) method were combined to calculate the binding free energy between Hfq and OxyS sRNA, and the computational result was correlated with the experimental result. Per-residue decomposition of the binding free energy revealed that the enhanced binding ability of the N48A mutation mainly came from the increased van der Waals interactions (vdW). This research explored the binding mechanism between Oxys and chaperone protein Hfq and revealed the origin of the strong binding affinity of N48A mutation. The results provided important insights into the mechanism of gene expression regulation affected by protein mutations.

Multiscale Simulations Examining Glycan Shield Effects on Drug Binding to Influenza Neuraminidase

Influenza neuraminidase is an important drug target. Glycans are present on neuraminidase and are generally considered to inhibit antibody binding via their glycan shield. In this work, we studied the effect of glycans on the binding kinetics of antiviral drugs to the influenza neuraminidase. We created all-atom in silico systems of influenza neuraminidase with experimentally derived glycoprofiles consisting of four systems with different glycan conformations and one system without glycans. Using Brownian dynamics simulations, we observe a two- to eightfold decrease in the rate of ligand binding to the primary binding site of neuraminidase due to the presence of glycans. These glycans are capable of covering much of the surface area of neuraminidase, and the ligand binding inhibition is derived from glycans sterically occluding the primary binding site on a neighboring monomer. Our work also indicates that drugs preferentially bind to the primary binding site (i.e., the active site) over the secondary binding site, and we propose a binding mechanism illustrating this. These results help illuminate the complex interplay between glycans and ligand binding on the influenza membrane protein neuraminidase.

Improvements to the APBS biomolecular solvation software suite

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that have provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses the three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this article, we discuss the models and capabilities that have recently been implemented within the APBS software package including a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory-based algorithm for determining pK_a values, and an improved web-based visualization tool for viewing electrostatics.

Large-scale molecular dynamics simulation: Effect of polarization on thrombin-ligand binding energy

Molecular dynamics (MD) simulations lasting 500 ns were performed in explicit water to investigate the effect of polarization on the binding of ligands to human α-thrombin based on the standard nonpolarizable AMBER force field and the quantum-derived polarized protein-specific charge (PPC). The PPC includes the electronic polarization effect of the thrombin-ligand complex, which is absent in the standard force field. A detailed analysis and comparison of the results of the MD simulation with experimental data provided strong evidence that intra-protein, protein-ligand hydrogen bonds and the root-mean-square deviation of backbone atoms were significantly stabilized through electronic polarization. Specifically, two critical hydrogen bonds between thrombin and the ligand were broken at approximately 190 ns when AMBER force field was used and the number of intra-protein backbone hydrogen bonds was higher under PPC than under AMBER. The thrombin-ligand binding energy was computed using the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method, and the results were consistent with the experimental value obtained using PPC. Because hydrogen bonds were unstable, it was failed to predict the binding affinity under the AMBER force field. Furthermore, the results of the present study revealed that differences in the binding free energy between AMBER and PPC almost comes from the electrostatic interaction. Thus, this study provides evidence that protein polarization is critical to accurately describe protein-ligand binding.

Analysis of Ligand-Receptor Association and Intermediate Transfer Rates in Multienzyme Nanostructures with All-Atom Brownian Dynamics Simulations

We present the second-generation GeomBD Brownian dynamics software for determining interenzyme intermediate transfer rates and substrate association rates in biomolecular complexes. Substrate and intermediate association rates for a series of enzymes or biomolecules can be compared between the freely diffusing disorganized configuration and various colocalized or complexed arrangements for kinetic investigation of enhanced intermediate transfer. In addition, enzyme engineering techniques, such as synthetic protein conjugation, can be computationally modeled and analyzed to better understand changes in substrate association relative to native enzymes. Tools are provided to determine nonspecific ligand-receptor association residence times, and to visualize common sites of nonspecific association of substrates on receptor surfaces. To demonstrate features of the software, interenzyme intermediate substrate transfer rate constants are calculated and compared for all-atom models of DNA origami scaffold-bound bienzyme systems of glucose oxidase and horseradish peroxidase. Also, a DNA conjugated horseradish peroxidase enzyme was analyzed for its propensity to increase substrate association rates and substrate local residence times relative to the unmodified enzyme. We also demonstrate the rapid determination and visualization of common sites of nonspecific ligand-receptor association by using HIV-1 protease and an inhibitor, XK263. GeomBD2 accelerates simulations by precomputing van der Waals potential energy grids and electrostatic potential grid maps, and has a flexible and extensible support for all-atom and coarse-grained force fields. Simulation software is written in C++ and utilizes modern parallelization techniques for potential grid preparation and Brownian dynamics simulation processes. Analysis scripts, written in the Python scripting language, are provided for quantitative simulation analysis. GeomBD2 is applicable to the fields of biophysics, bioengineering, and enzymology in both predictive and explanatory roles.

Electrostatic Steering Accelerates C3d:CR2 Association

Electrostatic effects are ubiquitous in protein interactions and are found to be pervasive in the complement system as well. The interaction between complement fragment C3d and complement receptor 2 (CR2) has evolved to become a link between innate and adaptive immunity. Electrostatic interactions have been suggested to be the driving factor for the association of the C3d:CR2 complex. In this study, we investigate the effects of ionic strength and mutagenesis on the association of C3d:CR2 through Brownian dynamics simulations. We demonstrate that the formation of the C3d:CR2 complex is ionic strength-dependent, suggesting the presence of long-range electrostatic steering that accelerates the complex formation. Electrostatic steering occurs through the interaction of an acidic surface patch in C3d and the positively charged CR2 and is supported by the effects of mutations within the acidic patch of C3d that slow or diminish association. Our data are in agreement with previous experimental mutagenesis and binding studies and computational studies. Although the C3d acidic patch may be locally destabilizing because of unfavorable Coulombic interactions of like charges, it contributes to the acceleration of association. Therefore, acceleration of function through electrostatic steering takes precedence to stability. The site of interaction between C3d and CR2 has been the target for delivery of CR2-bound nanoparticle, antibody, and small molecule biomarkers, as well as potential therapeutics. A detailed knowledge of the physicochemical basis of C3d:CR2 association may be necessary to accelerate biomarker and drug discovery efforts.

Multiscale Estimation of Binding Kinetics Using Brownian Dynamics, Molecular Dynamics and Milestoning

The kinetic rate constants of binding were estimated for four biochemically relevant molecular systems by a method that uses milestoning theory to combine Brownian dynamics simulations with more detailed molecular dynamics simulations. The rate constants found using this method agreed well with experimentally and theoretically obtained values. We predicted the association rate of a small charged molecule toward both a charged and an uncharged spherical receptor and verified the estimated value with Smoluchowski theory. We also calculated the kon rate constant for superoxide dismutase with its natural substrate, O2-, in a validation of a previous experiment using similar methods but with a number of important improvements. We also calculated the kon for a new system: the N-terminal domain of Troponin C with its natural substrate Ca2+. The kon calculated for the latter two systems closely resemble experimentally obtained values. This novel multiscale approach is computationally cheaper and more parallelizable when compared to other methods of similar accuracy. We anticipate that this methodology will be useful for predicting kinetic rate constants and for understanding the process of binding between a small molecule and a protein receptor.

Bridging scales through multiscale modeling: a case study on protein kinase A

The goal of multiscale modeling in biology is to use structurally based physico-chemical models to integrate across temporal and spatial scales of biology and thereby improve mechanistic understanding of, for example, how a single mutation can alter organism-scale phenotypes. This approach may also inform therapeutic strategies or identify candidate drug targets that might otherwise have been overlooked. However, in many cases, it remains unclear how best to synthesize information obtained from various scales and analysis approaches, such as atomistic molecular models, Markov state models (MSM), subcellular network models, and whole cell models. In this paper, we use protein kinase A (PKA) activation as a case study to explore how computational methods that model different physical scales can complement each other and integrate into an improved multiscale representation of the biological mechanisms. Using measured crystal structures, we show how molecular dynamics (MD) simulations coupled with atomic-scale MSMs can provide conformations for Brownian dynamics (BD) simulations to feed transitional states and kinetic parameters into protein-scale MSMs. We discuss how milestoning can give reaction probabilities and forward-rate constants of cAMP association events by seamlessly integrating MD and BD simulation scales. These rate constants coupled with MSMs provide a robust representation of the free energy landscape, enabling access to kinetic, and thermodynamic parameters unavailable from current experimental data. These approaches have helped to illuminate the cooperative nature of PKA activation in response to distinct cAMP binding events. Collectively, this approach exemplifies a general strategy for multiscale model development that is applicable to a wide range of biological problems.

Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/

Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design. Given their importance, it is vital to determine which protein interactions have functional relevance and to characterize the protein competition inherent to crowded environments, as the cytoplasm or the cellular organelles. We show that combining coarse-grain molecular cross-docking simulations and binding site predictions based on evolutionary sequence analysis is a viable route to identify true interacting partners for hundreds of proteins with a variate set of protein structures and interfaces. Also, we realize a large-scale analysis of protein binding promiscuity and provide a numerical characterization of partner competition and level of interaction strength for about 28000 false-partner interactions. Finally, we demonstrate that binding site prediction is useful to discriminate native partners, but also to scale up the approach to thousands of protein interactions. This study is based on the large computational effort made by thousands of internautes helping World Community Grid over a period of 7 months. The complete dataset issued by the computation and the analysis is released to the scientific community.

Protein-surface interaction maps for ion-exchange chromatography

In this paper, protein-surface interaction maps were generated by performing coarse-grained protein-surface calculations. This approach allowed for the rapid determination of the protein-surface interaction energies at a range of orientations and distances. Interaction maps of lysozyme indicated that there was a contiguous series of orientations corresponding to several adjacent preferred binding regions on the protein surface. Examination of these orientations provided insight into the residues involved in surface interactions, which qualitatively agreed with the retention data for single-site mutants. Interaction maps of lysozyme single-site mutants were also generated and provided significant insight into why these variants exhibited significant differences in their chromatographic behavior. This approach was also employed to study the binding behavior of CspB and related mutants. The results indicated that, in addition to describing general trends in the data, these maps provided significant insight into retention data of the single-site mutants. In particular, subtle retention trends observed with the K12 and K13 mutants were well-described using this interaction map approach. Finally, the number of interaction points with energies stronger than -2 kcal/mol was shown to be able to semi-quantitatively predict the behavior of most of the mutants. This rapid approach for calculating protein-surface interaction maps is expected to facilitate future method development for separating closely related protein variants in ion-exchange systems.

Diffusion, crowding & protein stability in a dynamic molecular model of the bacterial cytoplasm

A longstanding question in molecular biology is the extent to which the behavior of macromolecules observed in vitro accurately reflects their behavior in vivo. A number of sophisticated experimental techniques now allow the behavior of individual types of macromolecule to be studied directly in vivo; none, however, allow a wide range of molecule types to be observed simultaneously. In order to tackle this issue we have adopted a computational perspective, and, having selected the model prokaryote Escherichia coli as a test system, have assembled an atomically detailed model of its cytoplasmic environment that includes 50 of the most abundant types of macromolecules at experimentally measured concentrations. Brownian dynamics (BD) simulations of the cytoplasm model have been calibrated to reproduce the translational diffusion coefficients of Green Fluorescent Protein (GFP) observed in vivo, and “snapshots” of the simulation trajectories have been used to compute the cytoplasm's effects on the thermodynamics of protein folding, association and aggregation events. The simulation model successfully describes the relative thermodynamic stabilities of proteins measured in E. coli, and shows that effects additional to the commonly cited “crowding” effect must be included in attempts to understand macromolecular behavior in vivo.

The interior of a typical bacterial cell is a highly crowded place in which molecules must jostle and compete with each other in order to carry out their biological functions. The conditions under which such molecules are typically studied in vitro, however, are usually quite different: one or a few different types of molecules are studied as they freely diffuse in a dilute, aqueous solution. There is therefore a significant disconnect between the conditions under which molecules can be most usefully studied and the conditions under which such molecules usually “live”, and developing ways to bridge this gap is likely to be important for properly understanding molecular behavior in vivo. Toward this end, we show in this work that computer simulations can be used to model the interior of bacterial cells at a near atomic level of detail: the rates of diffusion of proteins are matched to known experimental values, and their thermodynamic stabilities are found to be in good agreement with the few measurements that have so far been performed in vivo. While the simulation approach is certainly not free of assumptions, it offers a potentially important complement to experimental techniques and provides a vivid illustration of molecular behavior inside a biological cell that is likely to be of significant educational value.

Cytoplasmic protein mobility in osmotically stressed Escherichia coli

Facile diffusion of globular proteins within a cytoplasm that is dense with biopolymers is essential to normal cellular biochemical activity and growth. Remarkably, Escherichia coli grows in minimal medium over a wide range of external osmolalities (0.03 to 1.8 osmol). The mean cytoplasmic biopolymer volume fraction ((phi)) for such adapted cells ranges from 0.16 at 0.10 osmol to 0.36 at 1.45 osmol. For cells grown at 0.28 osmol, a similar phi range is obtained by plasmolysis (sudden osmotic upshift) using NaCl or sucrose as the external osmolyte, after which the only available cellular response is passive loss of cytoplasmic water. Here we measure the effective axial diffusion coefficient of green fluorescent protein (D(GFP)) in the cytoplasm of E. coli cells as a function of (phi) for both plasmolyzed and adapted cells. For plasmolyzed cells, the median D(GFP) (D(GFP)(m)) decreases by a factor of 70 as (phi) increases from 0.16 to 0.33. In sharp contrast, for adapted cells, D(GFP)(m) decreases only by a factor of 2.1 as (phi) increases from 0.16 to 0.36. Clearly, GFP diffusion is not determined by (phi) alone. By comparison with quantitative models, we show that the data cannot be explained by crowding theory. We suggest possible underlying causes of this surprising effect and further experiments that will help choose among competing hypotheses. Recovery of the ability of proteins to diffuse in the cytoplasm after plasmolysis may well be a key determinant of the time scale of the recovery of growth.

Atomically detailed simulations of concentrated protein solutions: the effects of salt, pH, point mutations, and protein concentration in simulations of 1000-molecule systems

An ability to accurately simulate the dynamic behavior of concentrated macromolecular solutions would be of considerable utility in studies of a wide range of biological systems. With this goal in mind, a Brownian dynamics (BD) simulation method is reported here that allows systems to be modeled that comprise in excess of 1000 protein molecules, all of which are treated in atomic detail. Intermolecular forces are described in the method using an energy function that incorporates electrostatic and hydrophobic interactions and that is calibrated to reproduce experimental thermodynamic information with a single adjustable parameter. Using the method, BD simulations have been performed over a wide range of pH and ionic strengths for three proteins: hen egg white lysozyme (HEWL), chymotrypsinogen, and T4 lysozyme. The simulations reproduce experimental trends in second virial coefficients (B(22)) and translational diffusion coefficients, correctly capture changes in B(22) values due to single amino acid substitutions, and reveal a new explanation for the difficulties reported previously in the literature in reproducing B(22) values for protein solutions of very low ionic strength. In addition, a strong correlation is found between a residue's probability of being involved in a protein-protein contact in the simulations and its probability of being involved in an experimental crystal contact. Finally, exploratory simulations of HEWL indicate that the simulation model also gives a promising description of behavior at very high protein concentrations (approximately 250 g/L), suggesting that it may provide a suitable computational framework for modeling the complex behavior exhibited by macromolecules in cellular conditions.

Modeling 2D and 3D diffusion

Modeling obstructed diffusion is essential to the understanding of diffusion-mediated processes in the crowded cellular environment. Simple Monte Carlo techniques for modeling obstructed random walks are explained and related to Brownian dynamics and more complicated Monte Carlo methods. Random number generation is reviewed in the context of random walk simulations. Programming techniques and event-driven algorithms are discussed as ways to speed simulations.

Travel Depth, a New Shape Descriptor for Macromolecules: Application to Ligand Binding

Depth is a term frequently applied to the shape and surface of macromolecules, describing for example the grooves in DNA, the shape of an enzyme active site, or the binding site for a small molecule in a protein. Yet depth is a difficult property to define rigorously in a macromolecule, and few computational tools exist to quantify this notion, to visualize it, or analyze the results. We present our notion of travel depth, simply put the physical distance a solvent molecule would have to travel from a surface point to a suitably defined reference surface. To define the reference surface, we use the limiting form of the molecular surface with increasing probe size: the convex hull. We then present a fast, robust approximation algorithm to compute travel depth to every surface point. The travel depth is useful because it works for pockets of any size and complexity. It also works for two interesting special cases. First, it works on the grooves in DNA, which are unbounded in one direction. Second, it works on the case of tunnels, that is pockets that have no "bottom", but go through the entire macromolecule. Our algorithm makes it straightforward to quantify discussions of depth when analyzing structures. High-throughput analysis of macromolecule depth is also enabled by our algorithm. This is demonstrated by analyzing a database of protein-small molecule binding pockets, and the distribution of bound magnesium ions in RNA structures. These analyses show significant, but subtle effects of depth on ligand binding localization and strength.