Molecular simulations of cellular processes

Dynamics of nucleoplasm in human leukemia cells: A thrust towards designing anti-leukemic agents

The human inosine monophosphate dehydrogenase (hIMPDH) is a metabolic enzyme that possesses a unique ability to self-assemble into higher-order structures, forming cytoophidia. The hIMPDH II isoform is more active in chronic myeloid leukemia (CML) cancer cells, making it a promising target for anti-leukemic therapy. However, the structural details and molecular mechanisms of the dynamics of hIMPDHcytoophidia assembly in vitro need to be better understood, and it is crucial to reconstitute the computational nucleoplasm model with cytophilic-like polymers in vitro to characterize their structure and function. Finally, a computational model and its dynamics of the nucleoplasm for CML cells have been proposed in this short review. This research on nucleoplasm aims to aid the scientific community's understanding of how metabolic enzymes like hIMPDH function in cancer and normal cells. However, validating and justifying the computational results from modeling and simulation with experimental data is essential. The new insights gained from this research could explain the structure/topology, geometrical, and electronic consequences of hIMPDH inhibitors on leukemic and normal cells. They could lead to further advancements in the knowledge of nucleoplasmic chemical reaction dynamics.

Conformational ensembles explain NMR spectra of frozen intrinsically disordered proteins

Protein regions which are intrinsically disordered, exist as an ensemble of rapidly interconverting structures. Cooling proteins to cryogenic temperatures for dynamic nuclear polarization (DNP) magic angle spinning (MAS) NMR studies suspends most of the motions, resulting in peaks that are broad but not featureless. To demonstrate that detailed conformational restraints can be retrieved from the peak shapes of frozen proteins alone, we developed and used a simulation framework to assign peak features to conformers in the ensemble. We validated our simulations by comparing them to spectra of α-synuclein acquired under different experimental conditions. Our assignments of peaks to discrete dihedral angle populations suggest that structural constraints are attainable under cryogenic conditions. The ability to infer ensemble populations from peak shapes has important implications for DNP MAS NMR studies of proteins with regions of disorder in living cells because chemical shifts are the most accessible measured parameter.

The interplay between surface-functionalized gold nanoparticles and negatively charged lipid vesicles

The comprehensive understanding of the interactions between gold nanoparticles (AuNPs) and phospholipid vesicles has important implications in various biomedical applications; however, this is not yet well understood. Here, coarse-grained molecular dynamics (CGMD) simulations were performed to study the interactions between functionalized AuNPs and negatively charged lipid vesicles, and the effects of the surface chemistry and surface charge density (SCD) of AuNPs were analyzed. It is revealed that AuNPs with different surface ligands adhere to the membrane surface (anionic AuNPs) or get into the vesicle bilayer (hydrophobic and cationic AuNPs). Due to the loose arrangement of lipid molecules, AuNPs penetrate curved vesicle membranes more easily than planar lipid bilayers. Cationic AuNPs present three different interaction modes with the vesicle, namely insertion, partial penetration and complete penetration, which are decided by the SCD difference. Both hydrophobic interaction and electrostatic interaction play crucial roles in the interplay between cationic AuNPs and lipid vesicles. For the cationic AuNP with a low SCD, it gets into the lipid bilayer without membrane damage through the hydrophobic interaction, and it is finally stabilized in the hydrophobic interior of the vesicle membrane in a thermodynamically stable "snorkeling" configuration. For the cationic AuNP with a high SCD, it crosses the vesicle membrane and gets into the vesicle core through a membrane pore induced by strong electrostatic interaction. In this process, the membrane structure is destroyed. These findings provide a molecular-level understanding of the interplay between AuNPs and lipid vesicles, which may further expand the application of functional AuNPs in modern biomedicine.

Protein-nanoparticle interactions and a new insight

The study of protein-nanoparticle interactions provides knowledge about the bio-reactivity of nanoparticles, and creates a database of nanoparticles for applications in nanomedicine, nanodiagnosis, and nanotherapy. The problem arises when nanoparticles come in contact with physiological fluids such as plasma or serum, wherein they interact with the proteins (or other biomolecules). This interaction leads to the coating of proteins on the nanoparticle surface, mostly due to the electrostatic interaction, called 'corona'. These proteins are usually partially unfolded. The protein corona can deter nanoparticles from their targeted functionalities, such as drug/DNA delivery at the site and fluorescence tagging of diseased tissues. The protein corona also has many repercussions on cellular intake, inflammation, accumulation, degradation, and clearance of the nanoparticles from the body depending on the exposed part of the proteins. Hence, the protein-nanoparticle interaction and the configuration of the bound-proteins on the nanosurface need thorough investigation and understanding. Several techniques such as DLS and zeta potential measurement, UV-vis spectroscopy, fluorescence spectroscopy, circular dichroism, FTIR, and DSC provide valuable information in the protein-nanoparticle interaction study. Besides, theoretical simulations also provide additional understanding. Despite a lot of research publications, the fundamental question remained unresolved. Can we aim for the application of functional nanoparticles in medicine? A new insight, given by us, in this article assumes a reasonable solution to this crucial question.

Back to the Future: Genetically Encoded Fluorescent Proteins as Inert Tracers of the Intracellular Environment

Over the past decades, the discovery and development of genetically encoded fluorescent proteins (FPs) has brought a revolution into our ability to study biologic phenomena directly within living matter. First, FPs enabled fluorescence-labeling of a variety of molecules of interest to study their localization, interactions and dynamic behavior at various scales-from cells to whole organisms/animals. Then, rationally engineered FP-based sensors facilitated the measurement of physicochemical parameters of living matter-especially at the intracellular level, such as ion concentration, temperature, viscosity, pressure, etc. In addition, FPs were exploited as inert tracers of the intracellular environment in which they are expressed. This oft-neglected role is made possible by two distinctive features of FPs: (i) the quite null, unspecific interactions of their characteristic β-barrel structure with the molecular components of the cellular environment; and (ii) their compatibility with the use of time-resolved fluorescence-based optical microscopy techniques. This review seeks to highlight the potential of such unique combinations of properties and report on the most significative and original applications (and related advancements of knowledge) produced to date. It is envisioned that the use of FPs as inert tracers of living matter structural organization holds a potential for several lines of further development in the next future, discussed in the last section of the review, which in turn can lead to new breakthroughs in bioimaging.

Building a macro-mixing dual-basin Gō model using the Multistate Bennett Acceptance Ratio

The dual-basin Gō-model is a structural-based coarsegrained model for simulating a conformational transition between two known structures of a protein. Two parameters are required to produce a dual-basin potential mixed using two single-basin potentials, although the determination of mixing parameters is usually not straightforward. Here, we have developed an efficient scheme to determine the mixing parameters using the Multistate Bennett Acceptance Ratio (MBAR) method after short simulations with a set of parameters. In the scheme, MBAR allows us to predict observables at various unsimulated conditions, which are useful to improve the mixing parameters in the next round of iterative simulations. The number of iterations that are necessary for obtaining the converged mixing parameters are significantly reduced in the scheme. We applied the scheme to two proteins, the glutamine binding protein and the ribose binding protein, for showing the effectiveness in the parameter determination. After obtaining the converged parameters, both proteins show frequent conformational transitions between open and closed states, providing the theoretical basis to investigate structure-dynamics-function relationships of the proteins.

Whole-Cell Models and Simulations in Molecular Detail

Comprehensive data about the composition and structure of cellular components have enabled the construction of quantitative whole-cell models. While kinetic network-type models have been established, it is also becoming possible to build physical, molecular-level models of cellular environments. This review outlines challenges in constructing and simulating such models and discusses near- and long-term opportunities for developing physical whole-cell models that can connect molecular structure with biological function.

Computing long time scale biomolecular dynamics using quasi-stationary distribution kinetic Monte Carlo (QSD-KMC)

It is a challenge to obtain an accurate model of the state-to-state dynamics of a complex biological system from molecular dynamics (MD) simulations. In recent years, Markov state models have gained immense popularity for computing state-to-state dynamics from a pool of short MD simulations. However, the assumption that the underlying dynamics on the reduced space is Markovian induces a systematic bias in the model, especially in biomolecular systems with complicated energy landscapes. To address this problem, we have devised a new approach we call quasistationary distribution kinetic Monte Carlo (QSD-KMC) that gives accurate long time state-to-state evolution while retaining the entire time resolution even when the dynamics is highly non-Markovian. The proposed method is a kinetic Monte Carlo approach that takes advantage of two concepts: (i) the quasistationary distribution, the distribution that results when a trajectory remains in one state for a long time (the dephasing time), such that the next escape is Markovian, and (ii) dynamical corrections theory, which properly accounts for the correlated events that occur as a trajectory passes from state to state before it settles again. In practice, this is achieved by specifying, for each escape, the intermediate states and the final state that has resulted from the escape. Implementation of QSD-KMC imposes stricter requirements on the lengths of the trajectories than in a Markov state model approach as the trajectories must be long enough to dephase. However, the QSD-KMC model produces state-to-state trajectories that are statistically indistinguishable from an MD trajectory mapped onto the discrete set of states for an arbitrary choice of state decomposition. Furthermore, the aforementioned concepts can be used to construct a Monte Carlo approach to optimize the state boundaries regardless of the initial choice of states. We demonstrate the QSD-KMC method on two one-dimensional model systems, one of which is a driven nonequilibrium system, and on two well-characterized biomolecular systems.

Surface reaction-diffusion kinetics on lattice at the microscopic scale

Microscopic models of reaction-diffusion processes on the cell membrane can link local spatiotemporal effects to macroscopic self-organized patterns often observed on the membrane. Simulation schemes based on the microscopic lattice method (MLM) can model these processes at the microscopic scale by tracking individual molecules, represented as hard spheres, on fine lattice voxels. Although MLM is simple to implement and is generally less computationally demanding than off-lattice approaches, its accuracy and consistency in modeling surface reactions have not been fully verified. Using the Spatiocyte scheme, we study the accuracy of MLM in diffusion-influenced surface reactions. We derive the lattice-based bimolecular association rates for two-dimensional (2D) surface-surface reaction and one-dimensional (1D) volume-surface adsorption according to the Smoluchowski-Collins-Kimball model and random walk theory. We match the time-dependent rates on lattice with off-lattice counterparts to obtain the correct expressions for MLM parameters in terms of physical constants. The expressions indicate that the voxel size needs to be at least 0.6% larger than the molecule to accurately simulate surface reactions on triangular lattice. On square lattice, the minimum voxel size should be even larger, at 5%. We also demonstrate the ability of MLM-based schemes such as Spatiocyte to simulate a reaction-diffusion model that involves all dimensions: three-dimensional (3D) diffusion in the cytoplasm, 2D diffusion on the cell membrane, and 1D cytoplasm-membrane adsorption. With the model, we examine the contribution of the 2D reaction pathway to the overall reaction rate at different reactant diffusivity, reactivity, and concentrations.

Exploring Configuration Space and Path Space of Biomolecules Using Enhanced Sampling Techniques-Searching for Mechanism and Kinetics of Biomolecular Functions

To understand functions of biomolecules such as proteins, not only structures but their conformational change and kinetics need to be characterized, but its atomistic details are hard to obtain both experimentally and computationally. Here, we review our recent computational studies using novel enhanced sampling techniques for conformational sampling of biomolecules and calculations of their kinetics. For efficiently characterizing the free energy landscape of a biomolecule, we introduce the multiscale enhanced sampling method, which uses a combined system of atomistic and coarse-grained models. Based on the idea of Hamiltonian replica exchange, we can recover the statistical properties of the atomistic model without any biases. We next introduce the string method as a path search method to calculate the minimum free energy pathways along a multidimensional curve in high dimensional space. Finally we introduce novel methods to calculate kinetics of biomolecules based on the ideas of path sampling: one is the Onsager⁻Machlup action method, and the other is the weighted ensemble method. Some applications of the above methods to biomolecular systems are also discussed and illustrated.

Filamentous Aggregates of Tau Proteins Fulfil Standard Amyloid Criteria Provided by the Fuzzy Oil Drop (FOD) Model

Abnormal filamentous aggregates that are formed by tangled tau protein turn out to be classic amyloid fibrils, meeting all the criteria defined under the fuzzy oil drop model in the context of amyloid characterization. The model recognizes amyloids as linear structures where local hydrophobicity minima and maxima propagate in an alternating manner along the fibril's long axis. This distribution of hydrophobicity differs greatly from the classic monocentric hydrophobic core observed in globular proteins. Rather than becoming a globule, the amyloid instead forms a ribbonlike (or cylindrical) structure.

Impact of NR1I2, adenosine triphosphate-binding cassette transporters genetic polymorphisms on the pharmacokinetics of ginsenoside compound K in healthy Chinese volunteers

Background

Ginsenoside compound K (CK) is a promising drug candidate for rheumatoid arthritis. This study examined the impact of polymorphisms in NR1I2, adenosine triphosphate–binding cassette (ABC) transporter genes on the pharmacokinetics of CK in healthy Chinese individuals.

Methods

Forty-two targeted variants in seven genes were genotyped in 54 participants using Sequenom MassARRAY system to investigate their association with major pharmacokinetic parameters of CK and its metabolite 20(S)-protopanaxadiol (PPD). Subsequently, molecular docking was simulated using the AutoDock Vina program.

Results

ABCC4 rs1751034 TT and rs1189437 TT were associated with increased exposure of CK and decreased exposure of 20(S)-PPD, whereas CFTR rs4148688 heterozygous carriers had the lowest maximum concentration (C_max) of CK. The area under the curve from zero to the time of the last quantifiable concentration (AUC_last) of CK was decreased in NR1I2 rs1464602 and rs2472682 homozygous carriers, while C_max was significantly reduced only in rs2472682. ABCC4 rs1151471 and CFTR rs2283054 influenced the pharmacokinetics of 20(S)-PPD. In addition, several variations in ABCC2, ABCC4, CFTR, and NR1I2 had minor effects on the pharmacokinetics of CK. Quality of the best homology model of multidrug resistance protein 4 (MRP4) was assessed, and the ligand interaction plot showed the mode of interaction of CK with different MRP4 residues.

Conlusion

ABCC4 rs1751034 and rs1189437 affected the pharmacokinetics of both CK and 20(S)-PPD. NR1I2 rs1464602 and rs2472682 were only associated with the pharmacokinetics of CK. Thus, these hereditary variances could partly explain the interindividual differences in the pharmacokinetics of CK.

Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures

Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.