Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm

Heterogeneous Slowdown of Dynamics in the Condensate of an Intrinsically Disordered Protein

The high concentration of proteins and other biological macromolecules inside biomolecular condensates leads to dense and confined environments, which can affect the dynamic ensembles and the time scales of the conformational transitions. Here, we use atomistic molecular dynamics (MD) simulations of the intrinsically disordered low complexity domain (LCD) of the human fused in sarcoma (FUS) RNA-binding protein to study how self-crowding inside a condensate affects the dynamic motions of the protein. We found a heterogeneous retardation of the protein dynamics in the condensate with respect to the dilute phase, with large-amplitude motions being strongly slowed by up to 2 orders of magnitude, whereas small-scale motions, such as local backbone fluctuations and side-chain rotations, are less affected. The results support the notion of a liquid-like character of the condensates and show that different protein motions respond differently to the environment.

Quantitative characterization of non-specific interaction of two globular proteins with Dextran T70 in a binary mixture

In a bid to quantify the contribution of molecular structure to non-specific interactions leading to functionally important structural changes in cellular processes, the self-interaction of dextran-T70 (DT70) and its interaction with each of bovine serum albumin (BSA) and ovomucoid trypsin inhibitor (OVO) were studied at pH 7.4 between 5 and 37 °C. The dependences of the apparent molecular weight of each of BSA, OVO and DT70 on the concentration of DT70 were independent of temperature. The activity coefficient of the interaction of each species on DT70 concentration was also independent of temperature. The change in activity coefficient was however dependent on the molecular structure and size of the interacting species. The energy of insertion of each macromolecule in DT70 increased in the order DT70 > BSA > OVO. These findings show that although the enthalpic contribution is negligible, the extent of the entropic contribution to the macromolecular activity coefficient of interaction is chiefly the consequence of the exclusion volume of the interacting macromolecules.

BEMM-GEN: A Toolkit for Generating a Biomolecular Environment-Mimicking Model for Molecular Dynamics Simulation

Understanding the influence of the cellular environment on protein conformations is crucial for elucidating protein functions within living cells. In studies using molecular dynamics (MD) simulation, carbon nanotubes and hydrophobic cages have been widely used to emulate the cellular environment inside specific large biomolecules such as ribosome tunnels and chaperones. However, recent studies suggest that these uniform hydrophobic models may not adequately capture the environmental effects inside each biomolecule. Based on these facts, it is necessary to generate spherical and cylindrical models with varied chemical properties corresponding to the components within target biomolecules. We developed a biomolecular environment-mimicking model generator (BEMM-GEN) that generates spherical and cylindrical models with user-specified chemical properties and allows the integration of arbitrary protein conformations into the generated models. BEMM-GEN provides model and protein complex structures, along with the corresponding parameter files for MD simulation (AMBER and GROMACS), and users immediately run their MD simulation based on the generated input files. BEMM-GEN can be freely downloaded and installed via a Python package manager (pip install BEMM-gen). The source code files and a user manual for operation are provided on GitHub (https://github.com/y4suda/BEMM-GEN).

Diffusion and Viscosity in Mixed Protein Solutions

The viscosity and diffusion properties of crowded protein systems were investigated with molecular dynamics simulations of SH3 mixtures with different crowders, and results were compared with experimental data. The simulations accurately reproduced experimental trends across a wide range of protein concentrations, including highly crowded environments up to 300 g/L. Notably, viscosity increased with crowding but varied little between different crowder types, while diffusion rates were significantly reduced depending on protein-protein interaction strength. Analysis using the Stokes-Einstein relation indicated that the reduction in diffusion exceeded what was expected from viscosity changes alone, with the additional slow-down attributable to transient cluster formation driven by weakly attractive interactions. Contact kinetics analysis further revealed that longer-lived interactions contributed more significantly to reduced diffusion rates than short-lived interactions. This study also highlights the accuracy of current computational methodologies for capturing the dynamics of proteins in highly concentrated solutions and provides insights into the molecular mechanisms affecting protein mobility in crowded environments.

Biomolecular simulations at the exascale: From drug design to organelles and beyond

The rapid advancement in computational power available for research offers to bring not only quantitative improvements, but also qualitative changes in the field of biomolecular simulation. Here, we review the state of biomolecular dynamics simulations at the threshold to exascale resources becoming available. Both developments in parallel and distributed computing will be discussed, providing a perspective on the state of the art of both. A main focus will be on obtaining binding and conformational free energies, with an outlook to macromolecular complexes and (sub)cellular assemblies.

Role of Associated Water Dynamics on Protein Stability and Activity in Crowded Milieu

Macromolecular crowding bridges in vivo and in vitro studies by simulating cellular complexities such as high viscosity and limited space while maintaining the experimental feasibility. Over the last two decades, the impact of macromolecular crowding on protein stability and activity has been a significant topic of study and discussion, though still lacking a thorough mechanistic understanding. This article investigates the role of associated water dynamics on protein stability and activity within crowded environments, using bromelain and Ficoll-70 as the model systems. Traditional crowding theory primarily attributes protein stability to entropic effects (excluded volume) and enthalpic interactions. However, our recent findings suggest that water structure modulation plays a crucial role in a crowded environment. In this report, we strengthen the conclusion of our previous study, i.e., rigid-associated water stabilizes proteins via entropy and destabilizes them via enthalpy, while flexible water has the opposite effect. In the process, we addressed previous shortcomings with a systematic concentration-dependent study using a single-domain protein and component analysis of solvation dynamics. More importantly, we analyze bromelain's hydrolytic activity using the Michaelis-Menten model to understand kinetic parameters like maximum velocity (Vmax) achieved by the system and the Michaelis-Menten coefficient (KM). Results indicate that microviscosity (not the bulk viscosity) controls the enzyme-substrate (ES) complex formation, where an increase in the microviscosity makes the ES complex formation less favorable. On the other hand, flexible associated water dynamics were found to favor the rate of product formation significantly from the ES complex, while rigid associated water hinders it. This study improves our understanding of protein stability and activity in crowded environments, highlighting the critical role of associated water dynamics.

Anisotropic coarse-grain Monte Carlo simulations of lysozyme, lactoferrin, and NISTmAb by precomputing atomistic models

We develop a multiscale coarse-grain model of the NIST Monoclonal Antibody Reference Material 8671 (NISTmAb) to enable systematic computational investigations of high-concentration physical instabilities such as phase separation, clustering, and aggregation. Our multiscale coarse-graining strategy captures atomic-resolution interactions with a computational approach that is orders of magnitude more efficient than atomistic models, assuming the biomolecule can be decomposed into one or more rigid bodies with known, fixed structures. This method reduces interactions between tens of thousands of atoms to a single anisotropic interaction site. The anisotropic interaction between unique pairs of rigid bodies is precomputed over a discrete set of relative orientations and stored, allowing interactions between arbitrarily oriented rigid bodies to be interpolated from the precomputed table during coarse-grained Monte Carlo simulations. We present this approach for lysozyme and lactoferrin as a single rigid body and for the NISTmAb as three rigid bodies bound by a flexible hinge with an implicit solvent model. This coarse-graining strategy predicts experimentally measured radius of gyration and second osmotic virial coefficient data, enabling routine Monte Carlo simulation of medically relevant concentrations of interacting proteins while retaining atomistic detail. All methodologies used in this work are available in the open-source software Free Energy and Advanced Sampling Simulation Toolkit.

Influence of carbonyl cyanide m-chlorophenyl hydrazone on biofilm dynamics, protease, and siderophore production by Burkholderia pseudomallei

Efflux pump inhibitors are a potential therapeutic strategy for managing antimicrobial resistance and biofilm formation. This article evaluated the effect of carbonyl cyanide m-chlorophenyl hydrazone (CCCP) on the biofilm growth dynamics and the production of virulence factors by Burkholderia pseudomallei. The effects of CCCP on planktonic, growing, and mature biofilm, interaction with antibacterial drugs, and protease and siderophore production were assessed. CCCP MICs ranged between 128 and 256 µM. The CCCP (128 µM) had a synergic effect with all the antibiotics tested against biofilms. Additionally, CCCP reduced (p < .05) the biomass of biofilm growth and mature biofilms at 128 and 512 µM, respectively. CCCP also decreased (p < .05) protease production by growing (128 µM) and induced (p < .05) siderophore release by planktonic cells (128 µM) growing biofilms (12.8 and 128 µM) and mature biofilms (512 µM). CCCP demonstrates potential as a therapeutic adjuvant for disassembling B. pseudomallei biofilms and enhancing drug penetration.

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.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

CHARMM-GUI Multicomponent Assembler for modeling and simulation of complex multicomponent systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. In this work, we demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to study complex interactions between small molecules, biomacromolecules, polymers, and nanomaterials.

Size Sensitivity of Metabolite Diffusion in Macromolecular Crowds

Metabolites play crucial roles in cellular processes, yet their diffusion in the densely packed interiors of cells remains poorly understood, compounded by conflicting reports in existing studies. Here, we employ pulsed-gradient stimulated-echo NMR and Brownian/Stokesian dynamics simulations to elucidate the behavior of nano- and subnanometer-sized tracers in crowded environments. Using Ficoll as a crowder, we observe a linear decrease in tracer diffusivity with increasing occupied volume fraction, persisting─somewhat surprisingly─up to volume fractions of 30-40%. While simulations suggest a linear correlation between diffusivity slowdown and particle size, experimental findings hint at a more intricate relationship, possibly influenced by Ficoll's porosity. Simulations and numerical calculations of tracer diffusivity in the E. coli cytoplasm show a nonlinear yet monotonic diffusion slowdown with particle size. We discuss our results in the context of nanoviscosity and discrepancies with existing studies.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

SPANA: Spatial decomposition analysis for cellular-scale molecular dynamics simulations

The rapid increase in computational power with the latest supercomputers has enabled atomistic molecular dynamics (MDs) simulations of biomolecules in biological membrane, cytoplasm, and other cellular environments. These environments often contain a million or more atoms to be simulated simultaneously. Therefore, their trajectory analyses involve heavy computations that can become a bottleneck in the computational studies. Spatial decomposition analysis (SPANA) is a set of analysis tools in the Generalized-Ensemble Simulation System (GENESIS) software package that can carry out MD trajectory analyses of large-scale biological simulations using multiple CPU cores in parallel. SPANA applies the spatial decomposition of a large biological system to distribute structural and dynamical analyses into individual CPU cores, which reduces the computational time and the memory size, significantly. SPANA opens new possibilities for detailed atomistic analyses of biomacromolecules as well as solvent water molecules, ions, and metabolites in MD simulation trajectories of very large biological systems containing more than millions of atoms in cellular environments.

Exploring the dynamics and structure of PpiB in living Escherichia coli cells using electron paramagnetic resonance spectroscopy

The combined effects of the cellular environment on proteins led to the definition of a fifth level of protein structural organization termed quinary structure. To explore the implication of potential quinary structure for globular proteins, we studied the dynamics and conformations of Escherichia coli (E. coli) peptidyl-prolyl cis/trans isomerase B (PpiB) in E. coli cells. PpiB plays a major role in maturation and regulation of folded proteins by catalyzing the cis/trans isomerization of the proline imidic peptide bond. We applied electron paramagnetic resonance (EPR) techniques, utilizing both Gadolinium (Gd(III)) and nitroxide spin labels. In addition to using standard spin labeling approaches with genetically engineered cysteines, we incorporated an unnatural amino acid to achieve Gd(III)-nitroxide orthogonal labeling. We probed PpiB's residue-specific dynamics by X-band continuous wave EPR at ambient temperatures and its structure by double electron-electron resonance (DEER) on frozen samples. PpiB was delivered to E. coli cells by electroporation. We report a significant decrease in the dynamics induced by the cellular environment for two chosen labeling positions. These changes could not be reproduced by adding crowding agents and cell extracts. Concomitantly, we report a broadening of the distance distribution in E. coli, determined by Gd(III)-Gd(III) DEER measurements, as compared with solution and human HeLa cells. This suggests an increase in the number of PpiB conformations present in E. coli cells, possibly due to interactions with other cell components, which also contributes to the reduction in mobility and suggests the presence of a quinary structure.

A Novel Fractional Brownian Dynamics Method for Simulating the Dynamics of Confined Bottle-Brush Polymers in Viscoelastic Solution

In crowded fluids, polymer segments can exhibit anomalous subdiffusion due to the viscoelasticity of the surrounding environment. Previous single-particle tracking experiments revealed that such anomalous diffusion in complex fluids (e.g., in bacterial cytoplasm) can be described by fractional Brownian motion (fBm). To investigate how the viscoelastic media affects the diffusive behaviors of polymer segments without resolving single crowders, we developed a novel fractional Brownian dynamics method to simulate the dynamics of polymers under confinement. In this work, instead of using Gaussian random numbers ("white Gaussian noise") to model the Brownian force as in the standard Brownian dynamics simulations, we introduce fractional Gaussian noise (fGn) in our homemade fractional Brownian dynamics simulation code to investigate the anomalous diffusion of polymer segments by using a simple "bottle-brush"-type polymer model. The experimental results of the velocity autocorrelation function and the exponent that characterizes the subdiffusion of the confined polymer segments can be reproduced by this simple polymer model in combination with fractional Gaussian noise (fGn), which mimics the viscoelastic media.

Bridging Soft Interaction and Excluded Volume in Crowded Milieu through Subtle Protein Dynamics

The impact of macromolecular crowding on biological macromolecules has been elucidated through the excluded volume phenomenon and soft interactions. However, it has often been difficult to provide a clear demarcation between the two regions. Here, using temperature-dependent dynamics (local and global) of the multidomain protein human serum albumin (HSA) in the presence of commonly used synthetic crowders (Dextran 40, PEG 8, Ficoll 70, and Dextran 70), we have shown the presence of a transition that serves as a bridge between the soft and hard regimes. The bridging region is independent of the crowder identity and displays no apparent correlation with the critical overlap concentration of the polymeric crowding agents. Moreover, the dynamics of domains I and II and the protein gating motion respond differently, thereby bringing to the fore the asymmetry underlying the crowder influence on HSA. In addition, solvent-coupled and decoupled protein motions indicate the heterogeneity of the dynamic landscape in the crowded milieu. We also propose an intriguing correlation between protein stability and dynamics, with increased global stability being accompanied by eased local domain motion.

What determines sub-diffusive behavior in crowded protein solutions?

The aqueous environment inside cells is densely packed. A typical cell has a macromolecular concentration in the range 90-450 g/L, with 5%-40% of its volume being occupied by macromolecules, resulting in what is known as macromolecular crowding. The space available for the free diffusion of metabolites and other macromolecules is thus greatly reduced, leading to so-called excluded volume effects. The slow diffusion of macromolecules under crowded conditions has been explained using transient complex formation. However, sub-diffusion noted in earlier works is not well characterized, particularly the role played by transient complex formation and excluded volume effects. We have used Brownian dynamics simulations to characterize the diffusion of chymotrypsin inhibitor 2 in protein solutions of bovine serum albumin and lysozyme at concentrations ranging from 50 to 300 g/L. The predicted changes in diffusion coefficient as a function of crowder concentration are consistent with NMR experiments. The sub-diffusive behavior observed in the sub-microsecond timescale can be explained in terms of a so-called cage effect, arising from rattling motion in a local molecular cage as a consequence of excluded volume effects. By selectively manipulating the nature of interactions between protein molecules, we determined that excluded volume effects induce sub-diffusive dynamics at sub-microsecond timescales. These findings may help to explain the diffusion-mediated effects of protein crowding on cellular processes.

One bead per residue can describe all-atom protein structures

Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.

Effects of Crowding and Cosolutes on Biomolecular Function at Extreme Environmental Conditions

The extent of the effect of cellular crowding and cosolutes on the functioning of proteins and cells is manifold and includes the stabilization of the biomolecular systems, the excluded volume effect, and the modulation of molecular dynamics. Simultaneously, it is becoming increasingly clear how important it is to take the environment into account if we are to shed light on biological function under various external conditions. Many biosystems thrive under extreme conditions, including the deep sea and subseafloor crust, and can take advantage of some of the effects of crowding. These relationships have been studied in recent years using various biophysical techniques, including neutron and X-ray scattering, calorimetry, FTIR, UV-vis and fluorescence spectroscopies. Combining knowledge of the structure and conformational dynamics of biomolecules under extreme conditions, such as temperature, high hydrostatic pressure, and high salinity, we highlight the importance of considering all results in the context of the environment. Here we discuss crowding and cosolute effects on proteins, nucleic acids, membranes, and live cells and explain how it is possible to experimentally separate crowding-induced effects from other influences. Such findings will contribute to a better understanding of the homeoviscous adaptation of organisms and the limits of life in general.

Computational microbiology of bacteria: Advancements in molecular dynamics simulations

Microbiology is traditionally considered within the context of wet laboratory methodologies. Computational techniques have a great potential to contribute to microbiology. Here, we describe our loose definition of "computational microbiology" and provide a short survey focused on molecular dynamics simulations of bacterial systems that fall within this definition. It is our contention that increased compositional complexity and realistic levels of molecular crowding within simulated systems are key for bridging the divide between experimental and computational microbiology.

In silico protein dynamics in the human cytoplasm: Partial folding, misfolding, fold switching, and non-native interactions

We examine the influence of cellular interactions in all-atom models of a section of the Homo sapiens cytoplasm on the early folding events of the three-helix bundle protein B (PB). While genetically engineered PB is known to fold in dilute water box simulations in three microseconds, the three initially unfolded PB copies in our two cytoplasm models using a similar force field did not reach the native state during 30-microsecond simulations. We did however capture the formation of all three helices in a compact native-like topology. Folding in vivo is delayed because intramolecular contact formation within PB is in direct competition with intermolecular contacts between PB and surrounding macromolecules. In extreme cases, intermolecular beta-sheets are formed. Interactions with other macromolecules are also observed to promote structure formation, for example when a PB helix in our simulations is shielded from solvent by macromolecular crowding. Sticking and crowding in our models initiate sampling of helix/sheet structural plasticity of PB. Relatedly, in past in vitro experiments, similar GA domains were shown to switch between two different folds. Finally, we also observed that stickiness between PB and the cellular environment can be modulated in our simulations through the reduction in protein hydrophobicity when we reversed PB back to the wild-type sequence. This study demonstrates that even fast-folding proteins can get stuck in non-native states in the cell, making them useful models for protein-chaperone interactions and early stages of aggregate formation relevant to cellular disease.

Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations

The computational prediction of the viscosity of dense protein solutions is highly desirable, for example, in the early development phase of high-concentration biopharmaceutical formulations where the material needed for experimental determination is typically limited. Here, we use large-scale atomistic molecular dynamics (MD) simulations with explicit solvation to de novo predict the dynamic viscosities of solutions of a monoclonal IgG1 antibody (mAb) from the pressure fluctuations using a Green-Kubo approach. The viscosities at simulated mAb concentrations of 200 and 250 mg/mL are compared to the experimental values, which we measured with rotational rheometry. The computational viscosity of 24 mPa·s at the mAb concentration of 250 mg/mL matches the experimental value of 23 mPa·s obtained at a concentration of 213 mg/mL, indicating slightly different effective concentrations (or activities) in the MD simulations and in the experiments. This difference is assigned to a slight underestimation of the effective mAb-mAb interactions in the simulations, leading to a too loose dynamic mAb network that governs the viscosity. Taken together, this study demonstrates the feasibility of all-atom MD simulations for predicting the properties of dense mAb solutions and provides detailed microscopic insights into the underlying molecular interactions. At the same time, it also shows that there is room for further improvements and highlights challenges, such as the massive sampling required for computing collective properties of dense biomolecular solutions in the high-viscosity regime with reasonable statistical precision.

CHARMM-GUI Multicomponent Assembler for Modeling and Simulation of Complex Multicomponent Systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. We demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to facilitate innovative studies of complex interactions between small (organic and inorganic) molecules, biomacromolecules, polymers, and nanomaterials.

Minimal Coarse-Grained Model for Immunoglobulin G: Diffusion and Binding under Crowding

Immunoglobulin G (IgG) is the most common type of antibody found in blood and extracellular fluids and plays an essential role in our immune response. However, studies of the dynamics and reaction kinetics of IgG-antigen binding under physiological crowding conditions are scarce. Herein, we develop a coarse-grained model of IgG consisting of only six beads that we find minimal for a coarse representation of IgG's shape and a decent reproduction of its flexibility and diffusion properties measured experimentally. Using this model in Brownian dynamics simulations, we find that macromolecular crowding affects only slightly the IgG's flexibility, as described by the distribution of angles between the IgG's arms and stem. Our simulations indicate that, contrary to expectations, crowders slow down the translational diffusion of an IgG less strongly than they do for a smaller Ficoll 70, which we relate to the IgG's conformational size changes induced by crowding. We also find that crowders affect the binding kinetics by decreasing the rate of the first binding step and enhancing the second binding step.

Dynamics of chromosome organization in a minimal bacterial cell

Computational models of cells cannot be considered complete unless they include the most fundamental process of life, the replication and inheritance of genetic material. By creating a computational framework to model systems of replicating bacterial chromosomes as polymers at 10 bp resolution with Brownian dynamics, we investigate changes in chromosome organization during replication and extend the applicability of an existing whole-cell model (WCM) for a genetically minimal bacterium, JCVI-syn3A, to the entire cell-cycle. To achieve cell-scale chromosome structures that are realistic, we model the chromosome as a self-avoiding homopolymer with bending and torsional stiffnesses that capture the essential mechanical properties of dsDNA in Syn3A. In addition, the conformations of the circular DNA must avoid overlapping with ribosomes identitied in cryo-electron tomograms. While Syn3A lacks the complex regulatory systems known to orchestrate chromosome segregation in other bacteria, its minimized genome retains essential loop-extruding structural maintenance of chromosomes (SMC) protein complexes (SMC-scpAB) and topoisomerases. Through implementing the effects of these proteins in our simulations of replicating chromosomes, we find that they alone are sufficient for simultaneous chromosome segregation across all generations within nested theta structures. This supports previous studies suggesting loop-extrusion serves as a near-universal mechanism for chromosome organization within bacterial and eukaryotic cells. Furthermore, we analyze ribosome diffusion under the influence of the chromosome and calculate in silico chromosome contact maps that capture inter-daughter interactions. Finally, we present a methodology to map the polymer model of the chromosome to a Martini coarse-grained representation to prepare molecular dynamics models of entire Syn3A cells, which serves as an ultimate means of validation for cell states predicted by the WCM.

Chemical interactions modulate λ6-85 stability in cells

Because steric crowding is most effective when the crowding agent is similar in size to the molecule that it acts upon and the average macromolecule inside cells is much larger than a small protein or peptide, steric crowding is not predicted to affect their folding inside cells. On the other hand, chemical interactions should perturb in-cell structure and stability because they arise from interactions between the surface of the small protein or peptide and its environment. Indeed, previous in vitro measurements of the λ-repressor fragment, λ6-85 , in crowding matrices comprised of Ficoll or protein crowders support these predictions. Here, we directly quantify the in-cell stability of λ6-85 and distinguish the contribution of steric crowding and chemical interactions to its stability. Using a FRET-labeled λ6-85 construct, we find that the fragment is stabilized by 5°C in-cells compared to in vitro. We demonstrate that this stabilization cannot be explained by steric crowding because, as anticipated, Ficoll has no effect on λ6-85 stability. We find that the in-cell stabilization arises from chemical interactions, mimicked in vitro by mammalian protein extraction reagent (M-PER™). Comparison between FRET values in-cell and in Ficoll confirms that U-2 OS cytosolic crowding is reproduced at macromolecule concentrations of 15% w/v. Our measurements validate the cytomimetic of 15% Ficoll and 20% M-PER™ that we previously developed for protein and RNA folding studies. However, because the in-cell stability of λ6-85 is reproduced by 20% v/v M-PER™ alone, we predict that this simplified mixture could be a useful tool to predict the in-cell behaviors of other small proteins and peptides.

Unusual Aspects of Charge Regulation in Flexible Weak Polyelectrolytes

This article reviews the state of the art of the studies on charge regulation (CR) effects in flexible weak polyelectrolytes (FWPE). The characteristic of FWPE is the strong coupling of ionization and conformational degrees of freedom. After introducing the necessary fundamental concepts, some unconventional aspects of the the physical chemistry of FWPE are discussed. These aspects are: (i) the extension of statistical mechanics techniques to include ionization equilibria and, in particular, the use of the recently proposed Site Binding-Rotational Isomeric State (SBRIS) model, which allows the calculation of ionization and conformational properties on the same foot; (ii) the recent progresses in the inclusion of proton equilibria in computer simulations; (iii) the possibility of mechanically induced CR in the stretching of FWPE; (iv) the non-trivial adsorption of FWPE on ionized surfaces with the same charge sign as the PE (the so-called "wrong side" of the isoelectric point); (v) the influence of macromolecular crowding on CR.

Acceleration of generalized replica exchange with solute tempering simulations of large biological systems on massively parallel supercomputer

Generalized replica exchange with solute tempering (gREST) is one of the enhanced sampling algorithms for proteins or other systems with rugged energy landscapes. Unlike the replica-exchange molecular dynamics (REMD) method, solvent temperatures are the same in all replicas, while solute temperatures are different and are exchanged frequently between replicas for exploring various solute structures. Here, we apply the gREST scheme to large biological systems containing over one million atoms using a large number of processors in a supercomputer. First, communication time on a multi-dimensional torus network is reduced by matching each replica to MPI processors optimally. This is applicable not only to gREST but also to other multi-copy algorithms. Second, energy evaluations, which are necessary for the multistate bennet acceptance ratio (MBAR) method for free energy estimations, are performed on-the-fly during the gREST simulations. Using these two advanced schemes, we observed 57.72 ns/day performance in 128-replica gREST calculations with 1.5 million atoms system using 16,384 nodes in Fugaku. These schemes implemented in the latest version of GENESIS software could open new possibilities to answer unresolved questions on large biomolecular complex systems with slow conformational dynamics.

Optimized OPEP Force Field for Simulation of Crowded Protein Solutions

Macromolecular crowding has profound effects on the mobility of proteins, with strong implications on the rates of intracellular processes. To describe the dynamics of crowded environments, detailed molecular models are needed, capturing the structures and interactions arising in the crowded system. In this work, we present OPEPv7, which is a coarse-grained force field at amino-acid resolution, suited for rigid-body simulations of the structure and dynamics of crowded solutions formed by globular proteins. Using the OPEP protein model as a starting point, we have refined the intermolecular interactions to match the experimentally observed dynamical slowdown caused by crowding. The resulting force field successfully reproduces the diffusion slowdown in homogeneous and heterogeneous protein solutions at different crowding conditions. Coupled with the lattice Boltzmann technique, it allows the study of dynamical phenomena in protein assemblies and opens the way for the in silico rheology of protein solutions.

Allosteric regulation of β-reaction stage I in tryptophan synthase upon the α-ligand binding

Tryptophan synthase (TRPS) is a bifunctional enzyme consisting of α- and β-subunits that catalyzes the last two steps of L-tryptophan (L-Trp) biosynthesis. The first stage of the reaction at the β-subunit is called β-reaction stage I, which converts the β-ligand from an internal aldimine [E(Ain)] to an α-aminoacrylate [E(A-A)] intermediate. The activity is known to increase 3-10-fold upon the binding of 3-indole-D-glycerol-3'-phosphate (IGP) at the α-subunit. The effect of α-ligand binding on β-reaction stage I at the distal β-active site is not well understood despite the abundant structural information available for TRPS. Here, we investigate the β-reaction stage I by carrying out minimum-energy pathway searches based on a hybrid quantum mechanics/molecular mechanics (QM/MM) model. The free-energy differences along the pathway are also examined using QM/MM umbrella sampling simulations with QM calculations at the B3LYP-D3/aug-cc-pVDZ level of theory. Our simulations suggest that the sidechain orientation of βD305 near the β-ligand likely plays an essential role in the allosteric regulation: a hydrogen bond is formed between βD305 and the β-ligand in the absence of the α-ligand, prohibiting a smooth rotation of the hydroxyl group in the quinonoid intermediate, whereas the dihedral angle rotates smoothly after the hydrogen bond is switched from βD305-β-ligand to βD305-βR141. This switch could occur upon the IGP-binding at the α-subunit, as evidenced by the existing TRPS crystal structures.

Vast heterogeneity in cytoplasmic diffusion rates revealed by nanorheology and Doppelgänger simulations

The cytoplasm is a complex, crowded, actively driven environment whose biophysical characteristics modulate critical cellular processes such as cytoskeletal dynamics, phase separation, and stem cell fate. Little is known about the variance in these cytoplasmic properties. Here, we employed particle-tracking nanorheology on genetically encoded multimeric 40 nm nanoparticles (GEMs) to measure diffusion within the cytoplasm of individual fission yeast (Schizosaccharomyces pombe) cellscells. We found that the apparent diffusion coefficients of individual GEM particles varied over a 400-fold range, while the differences in average particle diffusivity among individual cells spanned a 10-fold range. To determine the origin of this heterogeneity, we developed a Doppelgänger simulation approach that uses stochastic simulations of GEM diffusion that replicate the experimental statistics on a particle-by-particle basis, such that each experimental track and cell had a one-to-one correspondence with their simulated counterpart. These simulations showed that the large intra- and inter-cellular variations in diffusivity could not be explained by experimental variability but could only be reproduced with stochastic models that assume a wide intra- and inter-cellular variation in cytoplasmic viscosity. The simulation combining intra- and inter-cellular variation in viscosity also predicted weak nonergodicity in GEM diffusion, consistent with the experimental data. To probe the origin of this variation, we found that the variance in GEM diffusivity was largely independent of factors such as temperature, the actin and microtubule cytoskeletons, cell-cyle stage, and spatial locations, but was magnified by hyperosmotic shocks. Taken together, our results provide a striking demonstration that the cytoplasm is not "well-mixed" but represents a highly heterogeneous environment in which subcellular components at the 40 nm size scale experience dramatically different effective viscosities within an individual cell, as well as in different cells in a genetically identical population. These findings carry significant implications for the origins and regulation of biological noise at cellular and subcellular levels.

Stress-dependent macromolecular crowding in the mitochondrial matrix

Macromolecules of various sizes induce crowding of the cellular environment. This crowding impacts on biochemical reactions by increasing solvent viscosity, decreasing the water-accessible volume and altering protein shape, function, and interactions. Although mitochondria represent highly protein-rich organelles, most of these proteins are somehow immobilized. Therefore, whether the mitochondrial matrix solvent exhibits macromolecular crowding is still unclear. Here, we demonstrate that fluorescent protein fusion peptides (AcGFP1 concatemers) in the mitochondrial matrix of HeLa cells display an elongated molecular structure and that their diffusion constant decreases with increasing molecular weight in a manner typical of macromolecular crowding. Chloramphenicol (CAP) treatment impaired mitochondrial function and reduced the number of cristae without triggering mitochondrial orthodox-to-condensed transition or a mitochondrial unfolded protein response. CAP-treated cells displayed progressive concatemer immobilization with increasing molecular weight and an eightfold matrix viscosity increase, compatible with increased macromolecular crowding. These results establish that the matrix solvent exhibits macromolecular crowding in functional and dysfunctional mitochondria. Therefore, changes in matrix crowding likely affect matrix biochemical reactions in a manner depending on the molecular weight of the involved crowders and reactants.

Diffusive Dynamics of Bacterial Proteome as a Proxy of Cell Death

Temperature variations have a big impact on bacterial metabolism and death, yet an exhaustive molecular picture of these processes is still missing. For instance, whether thermal death is determined by the deterioration of the whole or a specific part of the proteome is hotly debated. Here, by monitoring the proteome dynamics of E. coli, we clearly show that only a minor fraction of the proteome unfolds at the cell death. First, we prove that the dynamical state of the E. coli proteome is an excellent proxy for temperature-dependent bacterial metabolism and death. The proteome diffusive dynamics peaks at about the bacterial optimal growth temperature, then a dramatic dynamical slowdown is observed that starts just below the cell's death temperature. Next, we show that this slowdown is caused by the unfolding of just a small fraction of proteins that establish an entangling interprotein network, dominated by hydrophobic interactions, across the cytoplasm. Finally, the deduced progress of the proteome unfolding and its diffusive dynamics are both key to correctly reproduce the E. coli growth rate.

Molecular dynamics simulation of an entire cell

The ultimate microscope, directed at a cell, would reveal the dynamics of all the cell's components with atomic resolution. In contrast to their real-world counterparts, computational microscopes are currently on the brink of meeting this challenge. In this perspective, we show how an integrative approach can be employed to model an entire cell, the minimal cell, JCVI-syn3A, at full complexity. This step opens the way to interrogate the cell's spatio-temporal evolution with molecular dynamics simulations, an approach that can be extended to other cell types in the near future.

Crowded Environment Regulates the Coacervation of Biopolymers via Nonspecific Interactions

The membrane-less organelles (MLOs) with subcompartments are formed via liquid-liquid phase separation (LLPS) in the crowded cell interior whose background molecules are up to 400 mg/mL. It is still a puzzle how the background molecules regulate the formation, dynamics, and functions of MLOs. Using biphasic coacervate droplets formed by poly(l-lysine) (PLL), quaternized dextran (Q-dextran), and single-stranded oligonucleotides (ss-oligo) as a model of MLO, we online monitored the LLPS process in Bovine Serine Albumin (BSA) solution up to 200.0 mg/mL. Negatively charged BSA is able to form complex or coacervate with positively charged PLL and Q-dextran and thus participates in the LLPS via nonspecific interactions. Results show that BSA effectively regulates the LLPS by controlling the phase distribution, morphologies, and kinetics. With increasing BSA concentration, the spherical biphasic droplets evolve in sequence into phase-inverted flower-like structure, worm-like chains, network structures, and confined coacervates. Each kind of morphology is formed via its own specific growth and fusion pathway. Our work suggests that MLOs could be controlled solely by the crowded environment and provides a further step toward understanding the life process in cell.

A brief history of visualizing membrane systems in molecular dynamics simulations

Understanding lipid dynamics and function, from the level of single, isolated molecules to large assemblies, is more than ever an intensive area of research. The interactions of lipids with other molecules, particularly membrane proteins, are now extensively studied. With advances in the development of force fields for molecular dynamics simulations (MD) and increases in computational resources, the creation of realistic and complex membrane systems is now common. In this perspective, we will review four decades of the history of molecular dynamics simulations applied to membranes and lipids through the prism of molecular graphics.

Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease

Molecular Dynamics simulations are often used in drug design. However, such simulations do not account for the physiological environment of the receptor; hence overlook its impact on biomolecular interactions. To address this lacuna, we identified three objectives to pursue - develop models of physiological environment, study a drug-receptor complex in such environments, and identify methods to analyze these complicated simulations. Two novel physiological models were developed and studied. The first, called 'm10', comprises of 10 of the most abundant cytoplasmic metabolites at physiological concentrations. The second, called 'phy', supplements m10 with an additional crowder protein to elicit macromolecular crowding effect. The main protease (M^pro) of SARS-CoV-2, being essential for viral replication, is an attractive drug target for COVID-19. Hence, we chose M^pro docked with multiple drugs as our model drug-receptor system. With a plethora of compounds, physiological systems can be exceedingly large and complex. A novel Spark-based software (SparkTraj) was developed to rapidly analyze non-specific contacts and water interactions. Our study shows that crowding enhances the difference in the dynamics of apo- vs drug-bound complexes. Metabolites, at times as a cluster, were seen interacting with the protease, drugs, and binding sites in drug-free receptor. Except one that crawled to an adjacent pocket in phy, the drugs remained in their respective pockets in all simulations. Given these observations, we hope that the models and approach presented here would help the optimization, evaluation, and selection of potential drugs. Generic biomolecular dynamics could also benefit from such models and tools.Communicated by Ramaswamy H. Sarma.

Size of the protein-protein energy funnel in crowded environment

Association of proteins to a significant extent is determined by their geometric complementarity. Large-scale recognition factors, which directly relate to the funnel-like intermolecular energy landscape, provide important insights into the basic rules of protein recognition. Previously, we showed that simple energy functions and coarse-grained models reveal major characteristics of the energy landscape. As new computational approaches increasingly address structural modeling of a whole cell at the molecular level, it becomes important to account for the crowded environment inside the cell. The crowded environment drastically changes protein recognition properties, and thus significantly alters the underlying energy landscape. In this study, we addressed the effect of crowding on the protein binding funnel, focusing on the size of the funnel. As crowders occupy the funnel volume, they make it less accessible to the ligands. Thus, the funnel size, which can be defined by ligand occupancy, is generally reduced with the increase of the crowders concentration. This study quantifies this reduction for different concentration of crowders and correlates this dependence with the structural details of the interacting proteins. The results provide a better understanding of the rules of protein association in the crowded environment.

Characterizing Transient Protein-Protein Interactions by Trp-Cys Quenching and Computer Simulations

Transient protein-protein interactions occur frequently under the crowded conditions encountered in biological environments. Tryptophan-cysteine quenching is introduced as an experimental approach with minimal labeling for characterizing such interactions between proteins due to its sensitivity to nano- to microsecond dynamics on subnanometer length scales. The experiments are paired with computational modeling at different resolutions including fully atomistic molecular dynamics simulations for interpretation of the experimental observables and to gain molecular-level insights. This approach is applied to model systems, villin variants and the drkN SH3 domain, in the presence of protein G crowders. It is demonstrated that Trp-Cys quenching experiments can differentiate between overall attractive and repulsive interactions between different proteins, and they can discern variations in interaction preferences at different protein surface locations. The close integration between experiment and simulations also provides an opportunity to evaluate different molecular force fields for the simulation of concentrated protein solutions.

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

Reversible Unwrapping Algorithm for Constant-Pressure Molecular Dynamics Simulations

Molecular simulation technologies have afforded researchers a unique look into the nanoscale interactions driving physical processes. However, a limitation for molecular dynamics (MD) simulations is that they must be performed on finite-sized systems in order to map onto computational resources. To minimize artifacts arising from finite-sized simulation systems, it is common practice for MD simulations to be performed with periodic boundary conditions (PBCs). However, in order to calculate specific physical properties, such as mean square displacements to calculate diffusion coefficients, continuous particle trajectories where the atomic movements are continuous and do not jump between cell faces are required. In these cases, modifying atomic coordinates through unwrapping schemes is an essential post-processing tool to remove these jumps. Here, two established trajectory unwrapping schemes are applied to 1 μs wrapped trajectories for a small water box and lysozyme in water. The existing schemes can result in spurious diffusion coefficients, long bonds within unwrapped molecules, and inconsistent atomic coordinates when coordinates are rewrapped after unwrapping. We determine that prior unwrapping schemes do not account for changing periodic box dimensions and introduce an additional correction term to the existing displacement unwrapping scheme to correct for these artifacts. We also demonstrate that the resulting algorithm is a hybrid between the existing heuristic and displacement unwrapping schemes. After treatment using this new unwrapping scheme, molecular geometries are correct even after long simulations. In anticipation for longer MD trajectories, we develop implementations for this new scheme in multiple PBC handling tools.

Resolving molecular diffusion and aggregation of antibody proteins with megahertz X-ray free-electron laser pulses

X-ray free-electron lasers (XFELs) with megahertz repetition rate can provide novel insights into structural dynamics of biological macromolecule solutions. However, very high dose rates can lead to beam-induced dynamics and structural changes due to radiation damage. Here, we probe the dynamics of dense antibody protein (Ig-PEG) solutions using megahertz X-ray photon correlation spectroscopy (MHz-XPCS) at the European XFEL. By varying the total dose and dose rate, we identify a regime for measuring the motion of proteins in their first coordination shell, quantify XFEL-induced effects such as driven motion, and map out the extent of agglomeration dynamics. The results indicate that for average dose rates below 1.06 kGy μs^-1 in a time window up to 10 μs, it is possible to capture the protein dynamics before the onset of beam induced aggregation. We refer to this approach as correlation before aggregation and demonstrate that MHz-XPCS bridges an important spatio-temporal gap in measurement techniques for biological samples.

Modified Protein-Water Interactions in CHARMM36m for Thermodynamics and Kinetics of Proteins in Dilute and Crowded Solutions

Proper balance between protein-protein and protein-water interactions is vital for atomistic molecular dynamics (MD) simulations of globular proteins as well as intrinsically disordered proteins (IDPs). The overestimation of protein-protein interactions tends to make IDPs more compact than those in experiments. Likewise, multiple proteins in crowded solutions are aggregated with each other too strongly. To optimize the balance, Lennard-Jones (LJ) interactions between protein and water are often increased about 10% (with a scaling parameter, λ = 1.1) from the existing force fields. Here, we explore the optimal scaling parameter of protein-water LJ interactions for CHARMM36m in conjunction with the modified TIP3P water model, by performing enhanced sampling MD simulations of several peptides in dilute solutions and conventional MD simulations of globular proteins in dilute and crowded solutions. In our simulations, 10% increase of protein-water LJ interaction for the CHARMM36m cannot maintain stability of a small helical peptide, (AAQAA)₃ in a dilute solution and only a small modification of protein-water LJ interaction up to the 3% increase (λ = 1.03) is allowed. The modified protein-water interactions are applicable to other peptides and globular proteins in dilute solutions without changing thermodynamic properties from the original CHARMM36m. However, it has a great impact on the diffusive properties of proteins in crowded solutions, avoiding the formation of too sticky protein-protein interactions.

Effect of Protein–Protein Interactions on Translational Diffusion of Spheroidal Proteins

One of the commonly accepted approaches to estimate protein–protein interactions (PPI) in aqueous solutions is the analysis of their translational diffusion. The present review article observes a phenomenological approach to analyze PPI effects via concentration dependencies of self- and collective translational diffusion coefficient for several spheroidal proteins derived from the pulsed field gradient NMR (PFG NMR) and dynamic light scattering (DLS), respectively. These proteins are rigid globular α-chymotrypsin (ChTr) and human serum albumin (HSA), and partly disordered α-casein (α-CN) and β-lactoglobulin (β-Lg). The PPI analysis enabled us to reveal the dominance of intermolecular repulsion at low ionic strength of solution (0.003–0.01 M) for all studied proteins. The increase in the ionic strength to 0.1–1.0 M leads to the screening of protein charges, resulting in the decrease of the protein electrostatic potential. The increase of the van der Waals potential for ChTr and α-CN characterizes their propensity towards unstable weak attractive interactions. The decrease of van der Waals interactions for β-Lg is probably associated with the formation of stable oligomers by this protein. The PPI, estimated with the help of interaction potential and idealized spherical molecular geometry, are in good agreement with experimental data.

Evaluation of Weak Interactions of Proteins and Organic Cations with DNA Duplex Structures

Molecular interactions and reactions in living cells occur with high background concentrations of organic compounds including proteins. Uncharged water-soluble polymers are commonly used cosolutes in studies on molecular crowding, and most studies argue about the effects of intracellular crowding based on results obtained using polymer cosolutes. Further investigations using protein crowders and organic cations are important in understanding the effects of cellular environments on nucleic acids with negatively charged surfaces. We assessed the effects of using model globular proteins, serum proteins, histone proteins, structurally flexible polypeptides, di- and polyamines, and uncharged polymers. Thermal stability analysis of DNA oligonucleotide structures revealed that unlike conventional polymer cosolutes, basic globular proteins (lysozyme and cytochrome c) at high concentrations stabilized long internal and bulge loop structures but not fully matched duplexes. The selective stabilization of long loop structures suggests preferential binding to unpaired nucleotides in loops through weak electrostatic interactions. Furthermore, the ability of the proteins to stabilize the loop structures was enhanced under macromolecular crowding conditions. Remarkably, the effects of basic proteins on the stability of fully matched duplexes were dissimilar to those of basic amino-acid-rich polypeptides and polyamines. This study provides new insights into the interaction of nucleic acid structures with organic cations.

Electromechanical Photophysics of GFP Packed Inside Viral Protein Cages Probed by Force-Fluorescence Hybrid Single-Molecule Microscopy

Packing biomolecules inside virus capsids has opened new avenues for the study of molecular function in confined environments. These systems not only mimic the highly crowded conditions in nature, but also allow their manipulation at the nanoscale for technological applications. Here, green fluorescent proteins are packed in virus-like particles derived from P22 bacteriophage procapsids. The authors explore individual virus cages to monitor their emission signal with total internal reflection fluorescence microscopy while simultaneously changing the microenvironment with the stylus of atomic force microscopy. The mechanical and electronic quenching can be decoupled by ≈10% each using insulator and conductive tips, respectively. While with conductive tips the fluorescence quenches and recovers regardless of the structural integrity of the capsid, with the insulator tips quenching only occurs if the green fluorescent proteins remain organized inside the capsid. The electronic quenching is associated with the coupling of the protein fluorescence emission with the tip surface plasmon resonance. In turn, the mechanical quenching is a consequence of the unfolding of the aggregated proteins during the mechanical disruption of the capsid.

Crowding-induced protein destabilization in the absence of soft attractions

It is generally assumed that volume exclusion by macromolecular crowders universally stabilizes the native states of proteins and destabilization suggests soft attractions between crowders and protein. Here we show that proteins can be destabilized even by crowders that are purely repulsive. With a coarse-grained sequence-based model, we study the folding thermodynamics of two sequences with different native folds, a helical hairpin and a β-barrel, in a range of crowder volume fractions, φ_c. We find that the native state, N, remains structurally unchanged under crowded conditions, while the size of the unfolded state, U, decreases monotonically with φ_c. Hence, for all φ_c>0, U is entropically disfavored relative to N. This entropy-centric view holds for the helical hairpin protein, which is stabilized under all crowded conditions as quantified by changes in either the folding midpoint temperature, T_m, or the free energy of folding. We find, however, that the β-barrel protein is destabilized under low-T, low-φ_c conditions. This destabilization can be understood from two characteristics of its folding: 1) a relatively compact U at T<T_m, such that U is only weakly disfavored entropically by the crowders; and 2) a transient, compact, and relatively low-energy nonnative state that has a maximum population of only a few percent at φ_c=0, but increasing monotonically with φ_c. Overall, protein destabilization driven by hard-core effects appears possible when a compaction of U leads to even a modest population of compact nonnative states that are energetically competitive with N.

Impact of Inert Crowders on Host-Guest Recognition Process

Biological environments typically contain high concentrations (300-400 mg/mL) of different macromolecules at volume fractions as large as 30%-40%. Biomolecular recognition processes, a ubiquitous biological phenomena, occurring in such crowded heterogeneous media would differ significantly compared to the dilute buffer solutions. Here we quantify the potential impact of inert crowders on prototypical host-guest recognition process by explicit-solvent molecular dynamics (MD) simulations in atomic resolution. We demonstrate that the crowders, when smaller in size, would facilitate the binding process of the guest molecule by decreasing the free energy barrier for binding via excluded volume effect and desolvation of the host receptor. However, the extent of crowder-induced stabilization of a host-guest complex is found to be significantly higher when the guest molecule is sterically constricted to approach the host along a centrosymmetric direction, compared to its unrestricted, freely diffusive movement. A kinetic analysis of the recognition process reveals that the origin of a relatively stronger crowder impact during constricted movement of guest molecule lies in the relatively enhanced residence time of the guest inside the host by crowders. Together, our results suggest that the extent of impact of crowding on recognition processes would be contingent upon the presence or absence of constriction on ligand movement.