Enzymes Immobilized by Microencapsulation Within Spherical Ultrathin Polymeric Membranes

Biological cells in nature have diameters of about 7 to 50 μm, with a cell membrane of about 0.01 μm in thickness. This means that the total membrane area available for diffusion can be extremely large in comparison to the volume and that the cell membrane can also be very thin without the loss of mechanical strength. In addition, rapid mixing is possible within the individual microscopic compartments. Biological cells have very complex and selective enzyme systems to act on substrates crossing the cell membrane. Artificial cells in the form of spherical ultrathin polymeric membrane envelopes containing enzymes have been prepared in this laboratory with some of these properties [1-3]. Detailed papers and upadated information published since then on this subject have been described in a recent monograph [4].     

CHARMM-GUI Nanodisc Builder for modeling and simulation of various nanodisc systems

Nanodiscs are discoidal protein–lipid complexes that have wide applications in membrane protein studies. Modeling and simulation of nanodiscs are challenging due to the absence of structures of many membrane scaffold proteins (MSPs) that wrap around the membrane bilayer. We have developed CHARMM-GUI Nanodisc Builder ( http://www.charmm-gui.org/input/nanodisc ) to facilitate the setup of nanodisc simulation systems by modeling the MSPs with defined size and known structural features. A total of 11 different nanodiscs with a diameter from 80 to 180 Å are made available in both the all-atom CHARMM and two coarse-grained (PACE and Martini) force fields. The usage of the Nanodisc Builder is demonstrated with various simulation systems. The structures and dynamics of proteins and lipids in these systems were analyzed, showing similar behaviors to those from previous all-atom and coarse-grained nanodisc simulations. We expect the Nanodisc Builder to be a convenient and reliable tool for modeling and simulation of nanodisc systems. © 2019 Wiley Periodicals, Inc.     

Box length search algorithm for molecular simulation of systems containing periodic structures

We have developed a box length search algorithm to efficiently find the appropriate box dimensions for constant-volume molecular simulation of periodic structures. The algorithm works by finding the box lengths that equalize the pressure in each direction while maintaining constant total volume. Maintaining the volume at a fixed value ensures that quantitative comparisons can be made between simulation and experimental, theoretical or other simulation results for systems that are incompressible or nearly incompressible. We test the algorithm on a system of phase-separated block copolymers that has a preferred box length in one dimension. We also describe and test a Monte Carlo algorithm that allows the box lengths to change while maintaining constant volume. We find that the box length search algorithm converges at least two orders of magnitude more quickly than the variable box length Monte Carlo method. Although the box length search algorithm is not ergodic, it successfully finds the box length that minimizes the free energy of the system. We verify this by examining the free energy as determined by the Monte Carlo simulation.     

CHARMM‐GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides

A complex cell envelope, composed of a mixture of lipid types including lipopolysaccharides, protects bacteria from the external environment. Clearly, the proteins embedded within the various components of the cell envelope have an intricate relationship with their local environment. Therefore, to obtain meaningful results, molecular simulations need to mimic as far as possible this chemically heterogeneous system. However, setting up such systems for computational studies is far from trivial, and consequently the vast majority of simulations of outer membrane proteins still rely on oversimplified phospholipid membrane models. This work presents an update of CHARMM‐GUI Martini Maker for coarse‐grained modeling and simulation of complex bacterial membranes with lipopolysaccharides. The qualities of the outer membrane systems generated by Martini Maker are validated by simulating them in bilayer, vesicle, nanodisc, and micelle environments (with and without outer membrane proteins) using the Martini force field. We expect this new feature in Martini Maker to be a useful tool for modeling large, complicated bacterial outer membrane systems in a user‐friendly manner. © 2017 Wiley Periodicals, Inc.     

Automatic identification of model reductions for discrete stochastic simulation

Multiple time scales in cellular chemical reaction systems present a challenge for the efficiency of stochastic simulation. Numerous model reductions have been proposed to accelerate the simulation of chemically reacting systems by exploiting time scale separation. However, these are often identified and deployed manually, requiring expert knowledge. This is time-consuming, prone to error, and opportunities for model reduction may be missed, particularly for large models. We propose an automatic model analysis algorithm using an adaptively weighted Petri net to dynamically identify opportunities for model reductions for both the stochastic simulation algorithm and tau-leaping simulation, with no requirement of expert knowledge input. Results are presented to demonstrate the utility and effectiveness of this approach.     

Reaction Brownian dynamics and the effect of spatial fluctuations on the gain of a push-pull network

Brownian Dynamics algorithms have been widely used for simulating systems in soft-condensed matter physics. In recent times, their application has been extended to the simulation of coarse-grained models of biochemical networks. In these models, components move by diffusion and interact with one another upon contact. However, when reactions are incorporated into a Brownian dynamics algorithm, care must be taken to avoid violations of the detailed-balance rule, which would introduce systematic errors in the simulation. We present a Brownian dynamics algorithm for simulating reaction-diffusion systems that rigorously obeys detailed balance for equilibrium reactions. By comparing the simulation results to exact analytical results for a bimolecular reaction, we show that the algorithm correctly reproduces both equilibrium and dynamical quantities. We apply our scheme to a "push-pull" network in which two antagonistic enzymes covalently modify a substrate. Our results highlight that spatial fluctuations of the network components can strongly reduce the gain of the response of a biochemical network.     

酶催化反应模拟作用的研究及分析应用

生物转是化学和生物学交叉研究领域，包括生物催化剂（酶）工程和反应介质工程两大要素。一方面，开发性能优良的模拟酶，能模拟天然酶生物体内的高催化活性（酶模拟）；另一方面，介质工程可以用体外的方法模拟酶在生物体内细胞膜的微环境（膜模拟），对用体外的方法研究生物内催化信息，探讨生物体系的生命现象具有重要的意义。     

Exact on-lattice stochastic reaction-diffusion simulations using partial-propensity methods

Stochastic reaction-diffusion systems frequently exhibit behavior that is not predicted by deterministic simulation models. Stochastic simulation methods, however, are computationally expensive. We present a more efficient stochastic reaction-diffusion simulation algorithm that samples realizations from the exact solution of the reaction-diffusion master equation. The present algorithm, called partial-propensity stochastic reaction-diffusion (PSRD) method, uses an on-lattice discretization of the reaction-diffusion system and relies on partial-propensity methods for computational efficiency. We describe the algorithm in detail, provide a theoretical analysis of its computational cost, and demonstrate its computational performance in benchmarks. We then illustrate the application of PSRD to two- and three-dimensional pattern-forming Gray-Scott systems, highlighting the role of intrinsic noise in these systems.     

Cluster algorithm to perform parallel Monte Carlo simulation of atomistic systems

We propose an efficient algorithm to perform Monte Carlo simulations of dense systems using multiple particle moves. The method is intended to be used in the atomistic simulation of complex systems, where the computing requirements for a single simulation run make advisable the use of parallel computing. The algorithm is based on the use of steps in which all the particle positions of the system are perturbed simultaneously. A division of the system in clusters of particles is performed, using a bonding criterion which makes feasible that the acceptance or rejection of the new particle coordinates can be carried out independently for each cluster.     

A new technique for metropolis Monte Carlo simulation to capture aggregate structures of fine particles: Cluster-moving Monte Carlo algorithm

We present a new Monte Carlo simulation procedure which is capable of capturing aggregate structures in a suspension where fine particles are dispersed. The algorithm we call the “cluster-moving” Monte Carlo algorithm involves moving aggregates (clusters) as unitary particles at every certain Monte Carlo step. We discuss here the theoretical background of the cluster-moving Monte Carlo algorithm and the availability of the algorithm for simulations of systems where fine particles aggregate. The results of simulations for two model systems, magnetic fluids and colloidal dispersions, have shown that the new algorithm produces much more rapid convergence than the conventional one for unstable dispersion systems and reproduces physically reasonable aggregate structures of fine particles.     

Stochastic simulation of chemically reacting systems using multi-core processors

In recent years, computer simulations have become increasingly useful when trying to understand the complex dynamics of biochemical networks, particularly in stochastic systems. In such situations stochastic simulation is vital in gaining an understanding of the inherent stochasticity present, as these models are rarely analytically tractable. However, a stochastic approach can be computationally prohibitive for many models. A number of approximations have been proposed that aim to speed up stochastic simulations. However, the majority of these approaches are fundamentally serial in terms of central processing unit (CPU) usage. In this paper, we propose a novel simulation algorithm that utilises the potential of multi-core machines. This algorithm partitions the model into smaller sub-models. These sub-models are then simulated, in parallel, on separate CPUs. We demonstrate that this method is accurate and can speed-up the simulation by a factor proportional to the number of processors available.     

Molecular dynamics simulations of lipid bilayers

Computer simulation methods are becoming increasingly widespread as tools for studying the structure and dynamics of lipid bilayer membranes. The length scale and time scale accessible to atomic-level molecular dynamics simulations are rapidly increasing, providing insight into the relatively slow motions of molecular reorientation and translation and demonstrating that effects due to the finite size of the simulation cell can influence simulation results. Additionally, significant advances have been made in the complexity of membrane systems studied, including bilayers with cholesterol, small solute molecules, and lipid-protein and lipid-DNA complexes. Especially promising is the progress that continues to be made in the comparison of simulation results with experiment, both to validate the simulation algorithms and to aid in the interpretation of existing experimental data.     

NMR and Molecular Genetics as Tools for Investigating Protein Interactions in Membrane Systems

In our laboratory, we have applied the tools of nuclear magnetic resonance (NMR) spectroscopy and molecular genetics to investigate the structural and dynamic properties of membrane-associated proteins and their interactions with membrane components. There are two general classes of membrane proteins, i.e., intrinsic and peripheral ones. For the intrinsic membrane proteins, we have chosen the membranebound D-lactate dehydrogenase (D-LDH) of Escherichia coli as a model to study protein-lipid interactions in membranes. D-LDH is a respiratory enzyme of molecularweight 65, 000 containing flavin adenine dinucleotide (FAD) as a cofactor. The activity of purified D-LDH is enhanced up to 100-fold by lipids and detergents. The gene for D-LDH has been sequenced, and production of the enzyme amplified up to 300-times normal levels. We have biosynthetically incorporated 5-fluorotryptophan (5F-Trp) into D-LDH and studied the five Trp residues by ¹⁹F-NMR spectroscopy. In order to gain additional information using ¹⁹F-NMR, site-specific, oligonucleotide-directed mutagenesis has been used to insert a sixth Trp into D-LDH at various positions throughout the 571-amino acid chain. These mutant D-LDHs are being characterized biochemically and through NMR. For peripheral membrane proteins, we have chosen two periplasmic binding proteins, histidine-binding protein J (J protein) of Salmonella Typhimurium and glutamine-binding protein (GlnBP) of E. coli as models to investigate the structure-function relationship in periplasmic binding protein-mediated active transport systems. These two proteins both have molecular weights of approximately 25, 000. By using mutant J proteins and GlnBPs and site-specific, oligonucleotide-directed mutagenesis techniques, we have assigned several resonances to specific amino acid residues. We are investigating the relationship between ligand-induced conformational changes in these two proteins and their roles in the active transport of ligand across the cell membrane. We have found that a combination of isotopic labeling, biochemistry, molecular biology, and NMR is a very useful approach to investigate various interactions of membrane-associated protein systems.     

How enzyme dynamics helps catalyze a reaction in atomic detail: a transition path sampling study

We have applied the Transition Path Sampling algorithm to the reaction catalyzed by the enzyme Lactate Dehydrogenase. This study demonstrates the ease of scaling Transition Path Sampling for applications on many degree of freedom systems, whose energy surface is a complex terrain of valleys and saddle points. As a Monte Carlo importance sampling method, transition path sampling is capable of surmounting barriers in path phase space and focuses simulation on the rare event of enzyme catalyzed atom transfers. Generation of the transition path ensemble, for this reaction, resolves a paradox in the literature in which some studies exposed the catalytic mechanism of hydride and proton transfer by lactate dehydrogenase to be concerted and others stepwise. Transition path sampling has confirmed both mechanisms as possible paths from reactants to products. With the objective to identify a generalized, reduced reaction coordinate, time series of both donor-acceptor distances and residue distances from the active site have been examined. During the transition from pyruvate to lactate, residues located behind the transferring hydride collectively compress toward the active site causing residues located behind the hydride acceptor to relax away. It is demonstrated that an incomplete compression/relaxation transition across the donor-acceptor axis compromises the reaction.     

Improving on nature's defenses: optimization & high throughput screening of antimicrobial peptides

Antimicrobial peptides (AMPs) are ubiquitous in nature where they play important roles in host defense and microbial control. Despite their natural origin, antimicrobial spectrum and potency, the lead peptide candidates that so far have entered pharmaceutical development have all been further optimized by rational or semi-rational approaches. In recent years, several high throughput screening (HTS) systems have been developed to specifically address optimization of AMPs. These include a range of computational in silico systems and cell-based in vivo systems. The in silico-based screening systems comprise several computational methods such as Quantitative Structure/Activity Relationships (QSAR) as well as simulation methods mimicking peptide/membrane interactions. The in vivo-based systems can be divided in cis-acting and trans-acting screening systems. The cis-acting pre-screens, where the AMP exerts its antimicrobial effect on the producing cell, allow screening of millions or even billions of lead candidates for their basic antimicrobial or membrane-perturbating activity. The trans-acting screens, where the AMP is secreted or actively liberated from the producing cell and interacts with cells different from the producing cell, allow for screening under more complex and application-relevant conditions. This review describes the application of HTS systems employed for AMPs and lists advantages as well as limitations of these systems.     

Mechanistic Aspects for the Modulation of Enzyme Reactions on the DNA Scaffold

Cells have developed intelligent systems to implement the complex and efficient enzyme cascade reactions via the strategies of organelles, bacterial microcompartments and enzyme complexes. The scaffolds such as the membrane or protein in the cell are believed to assist the co-localization of enzymes and enhance the enzymatic reactions. Inspired by nature, enzymes have been located on a wide variety of carriers, among which DNA scaffolds attract great interest for their programmability and addressability. Integrating these properties with the versatile DNA–protein conjugation methods enables the spatial arrangement of enzymes on the DNA scaffold with precise control over the interenzyme distance and enzyme stoichiometry. In this review, we survey the reactions of a single type of enzyme on the DNA scaffold and discuss the proposed mechanisms for the catalytic enhancement of DNA-scaffolded enzymes. We also review the current progress of enzyme cascade reactions on the DNA scaffold and discuss the factors enhancing the enzyme cascade reaction efficiency. This review highlights the mechanistic aspects for the modulation of enzymatic reactions on the DNA scaffold.     

Monte Carlo simulation of osmotic equilibria

We present a Metropolis Monte Carlo simulation algorithm for the Tpπ-ensemble, where T is the temperature, p is the overall external pressure, and π is the osmotic pressure across the membrane. The algorithm, which can be applied to small molecules or sorption of small molecules in polymer networks, is tested for the case of Lennard-Jones interactions.     

Collective motion artifacts arising in long‐duration molecular dynamics simulations

We tested a variety of molecular dynamics simulation strategies in long‐duration (up to several nanoseconds) constant‐temperature simulations of liquid water under periodic boundary conditions. Such long durations are necessary to achieve adequate conformational sampling in simulations of membrane assemblies and other large biomolecular systems. Under a variety of circumstances, serious artifacts arise in the form of spurious collective behavior that becomes obvious only after the simulation has gone at least several hundred picoseconds. The potential energy of the system drops and the system changes from a liquid to an icy or glassy state. The underlying cause is accumulated center‐of‐mass motion of the system, coupled with velocity rescaling associated with constant‐temperature control. The velocity rescaling in the constant‐temperature algorithm reduces the thermal velocity as the net center‐of‐mass velocity grows, effectively causing the kinetic energy of the system to drain from thermal motions into coordinated motions. We found that the incidence and magnitude of the underlying artifactual motion leading to the spurious transition is mediated by: choice of method for computing electrostatic interactions; choice of ensemble; size of the simulation cell; SHAKE tolerance; frequency of nonbonded pairlist updating; and closeness of coupling to the temperature bath. The appearance of the spurious transition can be avoided by periodically subtracting net center‐of‐mass motion during the dynamics, or by improving the accuracy of the simulation by means of tightening SHAKE tolerance and updating nonbonded pairlists every timestep. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 121–131, 2000     

Efficient combination of Wang-Landau and transition matrix Monte Carlo methods for protein simulations

An efficient combination of the Wang-Landau and transition matrix Monte Carlo methods for protein and peptide simulations is described. At the initial stage of simulation the algorithm behaves like the Wang-Landau algorithm, allowing to sample the entire interval of energies, and at the later stages, it behaves like transition matrix Monte Carlo method and has significantly lower statistical errors. This combination allows to achieve fast convergence to the correct values of density of states. We propose that the violation of TTT identities may serve as a qualitative criterion to check the convergence of density of states. The simulation process can be parallelized by cutting the entire interval of simulation into subintervals. The violation of ergodicity in this case is discussed. We test the algorithm on a set of peptides of different lengths and observe good statistical convergent properties for the density of states. We believe that the method is of general nature and can be used for simulations of other systems with either discrete or continuous energy spectrum.