Atomistic simulations of competition between substrates binding to an enzyme

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.

SDA 7: A modular and parallel implementation of the simulation of diffusional association software

The simulation of diffusional association (SDA) Brownian dynamics software package has been widely used in the study of biomacromolecular association. Initially developed to calculate bimolecular protein-protein association rate constants, it has since been extended to study electron transfer rates, to predict the structures of biomacromolecular complexes, to investigate the adsorption of proteins to inorganic surfaces, and to simulate the dynamics of large systems containing many biomacromolecular solutes, allowing the study of concentration-dependent effects. These extensions have led to a number of divergent versions of the software. In this article, we report the development of the latest version of the software (SDA 7). This release was developed to consolidate the existing codes into a single framework, while improving the parallelization of the code to better exploit modern multicore shared memory computer architectures. It is built using a modular object-oriented programming scheme, to allow for easy maintenance and extension of the software, and includes new features, such as adding flexible solute representations. We discuss a number of application examples, which describe some of the methods available in the release, and provide benchmarking data to demonstrate the parallel performance.

ReaDDy--a software for particle-based reaction-diffusion dynamics in crowded cellular environments

We introduce the software package ReaDDy for simulation of detailed spatiotemporal mechanisms of dynamical processes in the cell, based on reaction-diffusion dynamics with particle resolution. In contrast to other particle-based reaction kinetics programs, ReaDDy supports particle interaction potentials. This permits effects such as space exclusion, molecular crowding and aggregation to be modeled. The biomolecules simulated can be represented as a sphere, or as a more complex geometry such as a domain structure or polymer chain. ReaDDy bridges the gap between small-scale but highly detailed molecular dynamics or Brownian dynamics simulations and large-scale but little-detailed reaction kinetics simulations. ReaDDy has a modular design that enables the exchange of the computing core by efficient platform-specific implementations or dynamical models that are different from Brownian dynamics.

Substrate channeling and enzyme complexes for biotechnological applications

Substrate channeling is a process of transferring the product of one enzyme to an adjacent cascade enzyme or cell without complete mixing with the bulk phase. Such phenomena can occur in vivo, in vitro, or ex vivo. Enzyme-enzyme or enzyme-cell complexes may be static or transient. In addition to enhanced reaction rates through substrate channeling in complexes, numerous potential benefits of such complexes are protection of unstable substrates, circumvention of unfavorable equilibrium and kinetics imposed, forestallment of substrate competition among different pathways, regulation of metabolic fluxes, mitigation of toxic metabolite inhibition, and so on. Here we review numerous examples of natural and synthetic complexes featuring substrate channeling. Constructing synthetic in vivo, in vitro or ex vivo complexes for substrate channeling would have great biotechnological potentials in metabolic engineering, multi-enzyme-mediated biocatalysis, and cell-free synthetic pathway biotransformation (SyPaB).

Diffusional channeling in the sulfate-activating complex: combined continuum modeling and coarse-grained brownian dynamics studies

Enzymes required for sulfur metabolism have been suggested to gain efficiency by restricted diffusion (i.e., channeling) of an intermediate APS(2-) between active sites. This article describes modeling of the whole channeling process by numerical solution of the Smoluchowski diffusion equation, as well as by coarse-grained Brownian dynamics. The results suggest that electrostatics plays an essential role in the APS(2-) channeling. Furthermore, with coarse-grained Brownian dynamics, the substrate channeling process has been studied with reactions in multiple active sites. Our simulations provide a bridge for numerical modeling with Brownian dynamics to simulate the complicated reaction and diffusion and raise important questions relating to the electrostatically mediated substrate channeling in vitro, in situ, and in vivo.

Identification of protein interaction partners and protein-protein interaction sites

Rigid-body docking has become quite successful in predicting the correct conformations of binary protein complexes, at least when the constituent proteins do not undergo large conformational changes upon binding. However, determining whether two given proteins interact is a more difficult problem. Successful docking procedures often give equally good scores for proteins that do not interact experimentally. This is the case for the multiple minimization approach we use here. An analysis of the results where all proteins within a set are docked with all other proteins (complete cross-docking) shows that the predictions can be greatly improved if the location of the correct binding interface on each protein is known, since the experimental complexes are much more likely to bring these two interfaces into contact, at the same time as yielding good interaction energy scores. While various methods exist for identifying binding interfaces, it is shown that simply studying the interaction of all potential protein pairs within a data set can itself help to identify the correct interfaces.

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.

How do biomolecular systems speed up and regulate rates?

The viability of a biological system depends upon careful regulation of the rates of various processes. These rates have limits imposed by intrinsic chemical or physical steps (e.g., diffusion). These limits can be expanded by interactions and dynamics of the biomolecules. For example, (a) a chemical reaction is catalyzed when its transition state is preferentially bound to an enzyme; (b) the folding of a protein molecule is speeded up by specific interactions within the transition-state ensemble and may be assisted by molecular chaperones; (c) the rate of specific binding of a protein molecule to a cellular target can be enhanced by mechanisms such as long-range electrostatic interactions, nonspecific binding and folding upon binding; (d) directional movement of motor proteins is generated by capturing favorable Brownian motion through intermolecular binding energy; and (e) conduction and selectivity of ions through membrane channels are controlled by interactions and the dynamics of channel proteins. Simple physical models are presented here to illustrate these processes and provide a unifying framework for understanding speed attainment and regulation in biomolecular systems.

Space in systems biology of signaling pathways--towards intracellular molecular crowding in silico

How cells utilize intracellular spatial features to optimize their signaling characteristics is still not clearly understood. The physical distance between the cell-surface receptor and the gene expression machinery, fast reactions, and slow protein diffusion coefficients are some of the properties that contribute to their intricacy. This article reviews computational frameworks that can help biologists to elucidate the implications of space in signaling pathways. We argue that intracellular macromolecular crowding is an important modeling issue, and describe how recent simulation methods can reproduce this phenomenon in either implicit, semi-explicit or fully explicit representation.

Atomic-level observation of macromolecular crowding effects: escape of a protein from the GroEL cage

Experimental work has demonstrated that the efficient operation of the GroEL-GroES chaperonin machinery is sensitive to the presence of macromolecular crowding agents. Here, I describe atomically detailed computer simulations that provide a microscopic view of how crowding effects are exerted. Simulations were performed to compute the free energy required to extract the protein rhodanese from the central cavity of GroEL into solutions containing a range of crowder concentrations. The computed energetics allow the total yield of folded protein to be predicted; the calculated yields show a nonlinear dependence on the concentration of crowding agent identical to that observed experimentally. The close correspondence between simulation and experiment prompts the use of the former in a truly predictive setting: simulations are used to suggest that more effective crowding agents might be designed by exploiting an "agoraphobic effect."

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.